Multi-omics insights into the effects of Allium mongolicum Regel flavonoids on growth, antioxidant capacity, and immune regulation in Saanen dairy male goats

Multi-omics insights into the effects of Allium mongolicum Regel flavonoids on growth, antioxidant capacity, and immune regulation in Saanen dairy male goats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics insights into the effects of Allium mongolicum Regel flavonoids on growth, antioxidant capacity, and immune regulation in Saanen dairy male goats lei xu, Aihuan Yu, Yaodi Xie, Ruixin Yang, Wenliang Tao, Chenxu Sun, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7997729/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background In intensive farming systems, oxidative stress and immune suppression often limit the production performance of ruminants. Allium mongolicum Regel flavonoids (AMRF), a characteristic plant-derived bioactive compound found in Northwest China, have shown potential antioxidant, anti-inflammatory, and intestinal microecological regulatory effects. However, their mechanism of action in Saanen dairy goat (SDG) remains unclear. This study investigated the regulatory effects of AMRF on the growth performance, antioxidant capacity, and immune function of SDGs using multi-omics approaches. Results Eighteen healthy castrated SDGs (3 ± 0.1 months old) with similar body weights (16.38 ± 1.36 kg) were selected and randomly assigned to two groups (n = 9 each), with all animals housed in individual pens. The control group received a basal diet, while the treatment group received 2.8 g AMRF per goat per day. The experimental period lasted 139 d, including a 15-d adaptation and a 124-d formal trial. Compared with the control group, dietary supplementation of AMRF significantly increased final body weight and average daily gain in SDGs. Among rumen fermentation parameters, the pH ( P = 0.044), microbial protein ( P = 0.029), and valeric acid concentration ( P = 0.042) were significantly increased, while the ammonia nitrogen ( P = 0.041) was significantly decreased. For serum indicators, the contents of total protein ( P = 0.037) and immunoglobulin A ( P = 0.028) were significantly increased; the total antioxidant capacity ( P = 0.001) was extremely significantly increased; and the contents of total cholesterol ( P = 0.011), glucose ( P = 0.049), and malondialdehyde ( P = 0.030) were significantly decreased. Multi-omics analysis revealed that AMRF increased the relative abundances of beneficial microorganisms, including the rumen genus Alloprevotella , cecal phylum Bacteroidota , and colonic genus Alistipes , while reducing harmful microorganisms such as Escherichia – Shigella . Additionally, AMRF upregulated the plasma key differential metabolites 12-hydroxyeicosatetraenoic acid and α -D-glucose, downregulated thromboxane B₂, activated the arginine biosynthesis and glutathione metabolism pathways, and regulated the expression of key differential genes in the liver, such as PTGS1 , CSF1R , and ND6 . Conclusion AMRF optimizes rumen nitrogen metabolism by modulating the gastrointestinal microbiota of SDGs, thereby improving plasma metabolic profiles and influencing the expression of liver genes through key plasma metabolites and metabolic pathways. These processes act synergistically to enhance antioxidant capacity, immune function, and growth performance, providing a theoretical basis for promoting healthy ruminant production. Allium mongolicum Regel flavonoids Saanen dairy male goats growth performance antioxidant immunity multi-omics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Under intensive farming systems, factors such as high-energy diets and environmental stress often disrupt the oxidative–antioxidative balance in animals, leading to immune suppression. The coordination between oxidation, antioxidation, and immune response forms the foundation for efficient growth and overall health in livestock. Oxidative stress can impair cell integrity by inducing lipid peroxidation of membranes and inhibiting key enzymes, thereby reducing the digestion and absorption efficiency of nutrients. Similarly, weakened immune function forces the diversion of more energy toward pathogen defense rather than growth and development[ 1 ]. During the fattening stage, ruminants are exposed to high temperatures, crowding, and energy-dense diets, all of which intensify metabolic pressure and energy imbalance[ 2 ]. These stressors trigger oxidative stress, such as increased malondialdehyde (MDA) levels and immune suppression, such as reduced immunoglobulin G (IgG) levels through multiple pathways, ultimately increasing the risk of diarrhea and respiratory infections[ 3 ]. Studies indicate that oxidative stress reduces feed conversion efficiency and average daily gain (ADG) in ruminants and markedly raises the incidence of disease[ 4 , 5 ]. Therefore, it is essential to identify safe and effective natural feed additives to counter these adverse effects. Flavonoids, the principal bioactive components of plant-derived feed additives, play a vital role in alleviating oxidative damage under stress conditions. They improve immune responses and enhance production performance through their antioxidant, immunomodulatory, and intestinal health–promoting functions. These plant-derived phytochemicals are divided into several subclasses, among which isoflavones (for example, daidzein) and flavanones (for example, citrus flavonoids) have attracted particular attention because of their strong bioavailability and widespread biological activities. Flavonoids exert their protective effects by donating electrons to neutralize free radicals and by stimulating antioxidant enzyme systems[ 6 ]. Previous research also demonstrates that flavonoids such as quercetin and kaempferol modulate immune cell activity (for example, by reducing the heterophil-to-lymphocyte ratio), reinforce intestinal barrier integrity, and suppress inflammatory cytokine release[ 7 ]. Additionally, supplementation with flavonoid-based additives has been shown to increase milk yield, milk fat, and protein content in dairy cows, while promoting weight gain and improving feed efficiency in beef cattle[ 8 ]. Allium mongolicum Regel is a characteristic wild plant native to the arid and semi-arid regions of northwestern China. Its extracts are rich in active components such as flavonoids and polyphenols, which exhibit multiple biological activities, including antioxidation, anti-inflammation, and regulation of intestinal microecology[ 9 , 10 ]. Previous studies have shown that dietary supplementation with Allium mongolicum Regel flavonoids (AMRF) in Small-tailed Han sheep can significantly improve production performance and overall health through two complementary mechanisms. First, AMRF directly modulates immune pathways by activating intestinal β -defensin genes (for example, sBD-2 expression increased by up to 171%) and pro-inflammatory cytokines (IL-1 β , IL-6, and TNF- α ), thereby strengthening the mucosal immune barrier. Second, AMRF indirectly optimizes nutrient allocation, reduces immune stress losses, increases ADG by approximately 41%, and decreases the feed-to-gain ratio by 23.6%[ 11 , 12 ]. Further studies confirmed that the effective dietary dose of AMRF is 2.8 g per goat per day, which markedly enhances growth performance and serum antioxidant function in meat sheep. This improvement is closely associated with the modulation of the intestinal microbiota[ 9 ]. Compared with synthetic antioxidants, natural flavonoids have the advantages of high compatibility, low residue, and broader physiological regulation. They can influence functions of the intestine, liver, and immune system. Such regulatory potential has been partly demonstrated in poultry[ 13 ], pigs[ 14 ], and meat sheep[ 15 ]. For example, grape seed extract rich in polyphenols has been shown to increase beneficial intestinal bacteria such as lactic acid bacteria, decrease pathogenic bacteria such as Clostridium, and improve intestinal morphology by increasing the villus height-to-crypt depth ratio[ 16 ]. Another study reported that flavonoids such as glycyrrhiza flavonoids suppress the synthesis of pro-inflammatory mediators, including prostaglandins (for example, PGE₂) and leukotrienes (for example, LTB₄ and LTC₄) by inhibiting key enzymes in arachidonic acid (AA) metabolism, such as cyclooxygenase (COX) and lipoxygenase (LOX), thereby reducing inflammatory responses[ 17 ]. In addition, AA and its metabolites can regulate voltage-gated ion channels (for example, calcium and potassium channels), influencing cell excitability and energy distribution[ 18 ]. Flavonoids may indirectly affect these ion channels by modulating AA metabolism, thereby influencing energy metabolism. However, systematic studies evaluating the above mechanisms in fattening dairy male goats remain limited. Therefore, we hypothesize that AMRF may regulate the gastrointestinal microbiota of dairy goats, optimize the rumen microecological environment, alter blood metabolites, and enrich metabolic pathways. In turn, the differential metabolites transported to the liver may further influence the expression of nutrient metabolism–related genes, ultimately improving the growth performance of the animal. Additionally, the antioxidative and anti-inflammatory activities of AMRF may enhance the antioxidant and immune capacities, thereby indirectly promoting the growth of the dairy goat. As newborn ruminants, the rumen of SDG is physiologically immature, and their intestinal microbiota is highly susceptible to external influences. Although the early rumen microbial community is relatively simple, colonization begins rapidly after birth and reaches relative stability within 3–4 months[ 19 ]. This developmental phase represents a “golden window” for nutritional and microbial intervention, as the microbial ecosystem exhibits high plasticity and the benefits of early intervention can persist into adulthood[ 20 ]. Thus, this stage provides an ideal model for investigating the regulatory effects of non-antibiotic feed additives. In this study, AMRF was supplemented in the diet of SDGs, and an integrated multi-omics approach combining 16S rRNA sequencing, untargeted metabolomics, and transcriptomics was employed to systematically evaluate its effects on intestinal microbiota composition, plasma metabolites, and hepatic gene expression. Through this approach, we aimed to elucidate how AMRF regulates growth performance, antioxidant capacity, and immune function in dairy male goats. The findings provide a theoretical basis for the use of natural plant extracts in promoting the healthy and sustainable breeding of ruminants. Materials and Methods Preparation of AMRF Fresh Allium mongolicum Regel was collected in Minqin County, Wuwei City, Gansu Province (36°29′N, 104°16′E) in June 2023. The cleaned leaves of Allium mongolicum Regel were dried to a constant weight in a constant-temperature drying oven (DZF-GW, Shanghai Binlin Electronic Technology Co., Ltd., Shanghai, China) at 60°C, then ground using a herbal grinder (CWF-300S, Zhejiang Top Medical Equipment Co., Ltd., Zhejiang, China) and sieved through a 1-mm mesh to obtain Allium mongolicum Regel powder (AMRP). The prepared powder was stored at 4°C until use. The extraction of AMRF was performed strictly according to the method described by Ding et al.[ 21 ], with an extraction yield of 28%. Ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) was used to determine the relative contents of the main active components in AMRF, and the results are presented in Supplementary Table 1. Animal Feeding and Management The experimental protocol and animal care procedures were conducted following the guidelines of the Experimental Animal Protection Committee of Gansu Agricultural University (approval number: GSAU-Eth-AST-2022-001). The experiment was conducted between June 29 and October 30, 2024, at the Baicaoyuan Sheep Farm of Weihe Dairy Group, located in Huan County, Qingyang City, Gansu Province (36°34′N, 107°18′E; altitude approximately 1500 m). The study site has a temperate continental semi-arid climate, with an average annual precipitation of 300 mm and an average annual temperature of 9.2°C. Eighteen healthy castrated SDGs [aged 3 ± 0.1 months, with average body weights of 16.38 ± 1.36 kg(Data were shown as mean ± SD)] were randomly assigned to two groups, each comprising nine goats. All animals were housed individually in pens. The control group received a basal diet, while the treatment group was provided with the same basal diet supplemented with 2.8 g AMRF per goat per day. This dosage was determined based on the optimal addition level of AMRP (10 g per goat per day) identified in a previous in vitro fermentation study on meat sheep by the research team, converted according to the AMRF extraction yield[ 9 ]. Before each feeding, the AMRF supplement was thoroughly mixed with 500 g of complete feed to ensure full consumption by the goats. The total experimental period lasted 139 d, including a 15-d adaptation period and a 124-d formal trial. The formal trial was divided into three phases: days 1–30 (Phase 1), days 31–60 (Phase 2), and days 61–124 (Phase 3). Throughout the study, the goats had ad libitum access to drinking water and were fed twice daily at 08:00 and 18:00. The composition and chemical composition of the basal diet are listed in Supplementary Table 2. Determination of Growth Performance All experimental goats underwent fasting weighing on the first and 124th days of the formal trial. The fasting period lasted 12 h, during which only water was provided. Body weight was measured using an electronic scale (model DH-108, Hunan Duheng Technology Co., Ltd., Hangzhou, China) with an accuracy of 0.01 kg. The body weight measured on the first day of the formal trial was defined as the initial body weight (IBW), and that measured on the 124th day was defined as the final body weight (FBW). The feed offered to each goat was recorded before feeding, and the remaining feed was collected and weighed before the next feeding to determine actual daily feed intake. After each experimental stage, the total feed intake (FI) was calculated. All feed intake data were recorded at gram-level precision to minimize weighing error in the calculation of performance parameters. Based on the recorded data (IBW, FBW, and FI), two growth performance indices were calculated: ADG and feed-to-gain ratio (FCR). Blood Collection At the end of the fattening period, six SDGs with similar body weights (38.03 ± 3.57 kg) were selected from each group and transported to the slaughterhouse of Huan County Zhongsheng Sheep Industry Development Co., Ltd. for slaughter. On the day of slaughter, all animals were fasted for 12 h, after which 50 mL of blood was collected from the jugular vein. Of this, 30 mL was distributed into six 5-mL coagulated tubes and centrifuged at 25°C and 1100 × g for 10 min using a centrifuge (model TD5-2, Jiangsu Tianli Medical Equipment Co., Ltd., Jiangsu, China). The separated serum was collected into EP tubes and stored at − 80°C for subsequent analysis of serum biochemical and antioxidant indices. The remaining 20 mL of blood was placed into four 5-mL EDTA tubes containing an anticoagulant and centrifuged as before. The plasma was collected into EP tubes and stored at − 80°C for subsequent plasma metabolomics analysis. Collection of Liver and Gastrointestinal Contents Immediately after slaughter, liver tissue samples from the middle lobe were collected from the same anatomical site. One portion was fixed in 4% paraformaldehyde for hematoxylin–eosin (HE) and Oil Red O (ORO) staining, while the remaining portion was rapidly frozen in liquid nitrogen and stored at − 80°C for transcriptomic analysis. For the collection of rumen, cecum, and colon contents, each region was carefully separated immediately after slaughter. The contents were collected, subpackaged, and frozen in liquid nitrogen for subsequent microbial composition analysis. Determination of Indicators Rumen Fermentation Parameters Rumen fluid was filtered through three layers of gauze, temporarily stored in a liquid nitrogen tank, and subsequently transferred to the laboratory for storage at − 80°C until analysis. Before collecting rumen fluid samples at the slaughterhouse, the pH of the rumen fluid was measured in situ using a pH meter. [main unit: Testo 205; probe: Testo 0550 1572; Testo (Shenzhen) Co., Ltd., Germany]. The instrument was calibrated within 1 h prior to measurement using standard buffer solutions with pH of 6.00, 6.86, and 7.00. It was equipped with an automatic temperature compensation function, and the stabilized pH reading was recorded. For the determination of volatile fatty acids (VFAs), gas chromatography (GC) was used with a flame ionization detector (FID). The analysis was performed using a Shimadzu GC-2010 (Shimadzu, Japan) equipped with a DB-FFAP capillary column (30 m × 0.25 mm × 0.25 µm; Agilent, USA). Nitrogen (purity ≥ 99.999%) was used as the carrier gas at a constant flow rate of 1 mL/min. The injection port and FID temperatures were maintained at 220°C and 250°C, respectively. The column temperature program was as follows: maintained at 40°C for 3 min, increased to 180°C at 5°C/min, and held for 5 min. The injection volume was 1 µL, with a split ratio of 50:1. Prior to injection, 1 mL of rumen fluid supernatant (after gauze filtration) was mixed with 20 µL of 50% phosphoric acid (v/v), vortexed for 1 min, centrifuged at 12,000 × g for 10 min at 4°C, and the supernatant was filtered through a 0.22 µm organic-phase membrane. The concentration of ammonia nitrogen (NH₃–N) was determined by the phenol–sodium hypochlorite colorimetric method. The supernatant obtained after centrifugation of the rumen fluid was mixed with a color developer and incubated in a 37°C water bath for 30 min. Absorbance was measured at 625 nm using a Shimadzu UV-2600 ultraviolet spectrophotometer (Shimadzu, Japan). A standard curve was generated using bovine serum albumin (BSA), and the NH₃–N concentration was calculated accordingly. The concentration of microbial protein (MCP) was determined using the Coomassie Brilliant Blue G-250 staining method. The microbial pellet, obtained after centrifugation and washing, was digested with 0.5 mol/L NaOH at 100°C for 30 min. After cooling, a color developer was added, and the mixture was incubated at room temperature in the dark for 5 min. Absorbance was measured at 595 nm using a UV spectrophotometer. A BSA standard curve was used to calculate the MCP concentration. Serum Indicators Serum protein, hepatic and renal function parameters, lipid metabolism indices, and immune indicators were analyzed by Beijing Huaying Biotechnology Co., Ltd. using a Mindray BS-420 automatic biochemical analyzer (Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China). Antioxidant indicators were determined using commercial kits obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). The specific kits and catalog numbers were as follows: catalase (CAT), Kit No. A007-1-1; superoxide dismutase (SOD), Kit No. A001-3; total antioxidant capacity (T-AOC), Kit No. A015-2-1; and malondialdehyde (MDA), Kit No. A003-1. Absorbance values were read at 450, 593, 405 nm, and 532 nm using a microplate reader. All assays were conducted strictly following the manufacturer’s instructions, and each biochemical indicator was analyzed in six biological replicates. Liver Tissue Morphology and Pathology Paraffin-embedded liver tissue sections were dewaxed, rehydrated, stained with hematoxylin for 5 min and eosin for 30 s, dehydrated, cleared, and mounted. Hepatocyte morphology was examined under an optical microscope (Olympus BX53, Japan) using CaseViewer Native Windows Application 2.6 software at 100 µm magnification. Frozen liver sections were stained with ORO for 15 min and counterstained with hematoxylin to visualize lipid droplet distribution. Images from six randomly selected microscopic fields were captured using CaseViewer software. The proportion of lipid droplet area was quantified using Image-Pro Plus 6.0 software, and bar charts were generated with GraphPad Prism 9.5.0. Statistical analysis was performed using the t -test to assess differences between groups. Gastrointestinal Microbiota Analysis Total genomic DNA from microbial communities in the rumen, cecum, and colon was extracted using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, USA). DNA quality and concentration were verified by 1% agarose gel electrophoresis and NanoDrop 2000 spectrophotometry. The extracted DNA served as a template to amplify the V3–V4 region of the 16S rRNA gene using polymerase chain reaction (PCR) with barcode-labeled specific primers: 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), where the 5' end of each primer was modified with a unique 8-bp barcode to distinguish different samples. PCR products were separated and purified on 2% agarose gels, quantified, and then used for library preparation with the NEXTFLEX Rapid DNA-Seq Kit. Sequencing was performed on the Illumina NextSeq 2000 platform. Raw sequencing data were processed using fastp and FLASH software for quality control and sequence assembly. UPARSE was employed to cluster sequences into operational taxonomic units (OTUs) at 97% similarity and to remove chimeric sequences. After rarefaction, the average length of OTU representative sequences was 458 ± 12 bp(consistent with the expected length of the 16S rRNA gene V3–V4 region, ~ 460 bp), indicating reliable amplification and sequencing quality. The RDP classifier was used to annotate sequences against the Silva database (v138), and PICRUSt2 was applied for functional prediction. All data analyses were completed on the Majorbio/Sanger Information Cloud Platform. Specifically, Mothur software was used to calculate alpha diversity indices (for example, Chao1 and Shannon indices), and the student’s t -test was used to assess differences in alpha diversity between groups. Linear discriminant analysis effect size (LEfSe) was performed to identify bacterial taxa with significantly different abundances between groups (criteria: LDA > 2 and P < 0.05), covering taxonomic levels from phylum to genus, to identify differential and dominant microbial species. Plasma Metabolomics Analysis For sample preparation, 100 µL of plasma was transferred into a 1.5-mL centrifuge tube, followed by the addition of 400 µL of acetonitrile–methanol (1:1, v/v) extract containing four internal standards (IS): L-2-chlorophenylalanine (10 µg/mL), D4-succinic acid (5 µg/mL), D10-palmitic acid (2 µg/mL), and D3-creatinine (1 µg/mL). These internal standards were used to calibrate sample extraction efficiency and correct for instrumental drift during mass spectrometry analysis. The mixture was vortexed for 30 s, sonicated at 5°C for 30 min, and then allowed to stand at − 20°C for 30 min. After centrifugation at 13,000 × g for 15 min at 4°C, the supernatant was evaporated to dryness under nitrogen. The residue was redissolved in 100 µL of acetonitrile–water (1:1, v/v), sonicated at 5°C for 5 min, and centrifuged again at 13,000 × g for 10 min at 4°C. The resulting supernatant was transferred into an injection vial for subsequent analysis. Chromatographic analysis was performed using an ultra-high-performance liquid chromatography (UHPLC) system coupled with an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific, USA). Separation was achieved on an HSS T3 column (100 mm × 2.1 mm, 1.8 µm) with an injection volume of 3 µL. The mobile phases consisted of phase A (95% water and 5% acetonitrile containing 0.1% formic acid) and phase B (47.5% acetonitrile, 47.5% isopropanol, and 5% water containing 0.1% formic acid). The flow rate was 0.40 mL/min, and the column temperature was maintained at 40°C. Mass spectrometry was conducted in both positive and negative ionization modes over an m/z range of 70–1050. The following conditions were used: sheath gas flow 50 psi, auxiliary gas flow 13 psi, auxiliary gas temperature 425°C, spray voltage ± 3500 V, ion transfer tube temperature 325°C, and stepped collision energies of 20, 40, and 60 V. The full-scan MS resolution was set at 60,000, and the tandem MS resolution at 7,500. Data were acquired in data-dependent acquisition (DDA) mode. For metabolite identification and analysis, raw data were processed using Progenesis QI software. Metabolite identification was performed by matching data to the Human Metabolome Database (HMDB), Metlin, and in-house databases. Data preprocessing involved application of the 80% rule for missing value removal, minimum value imputation, total ion current normalization, quality control relative standard deviation (QC-RSD) < 30% filtering, and log10 transformation. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with 7-fold cross-validation were conducted using the ropls package in R. Differential metabolites were identified using criteria of variable importance in projection (VIP) > 1 and P < 0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation and enrichment analysis were performed using Fisher’s exact test implemented in Python SciPy.Stats. Liver Transcriptomics Analysis Approximately 20 mg of frozen liver tissue was ground into fine powder in liquid nitrogen, followed by the addition of lysis buffer containing guanidinium isothiocyanate (for example, TRIzol) to lyse cells and release RNA. Chloroform was added, mixed, and centrifuged to separate phases. The upper aqueous layer containing RNA was collected, and isopropanol was added for precipitation under low-temperature conditions. The RNA pellet was washed with 75% RNase-free ethanol, air-dried, and dissolved in RNase-free water. RNA quality and integrity were assessed using a UV spectrophotometer and agarose gel electrophoresis. Messenger RNA (mRNA) was isolated using mRNA Capture Beads and fragmented under controlled high-temperature conditions. The first complementary DNA (cDNA) strand was synthesized using reverse transcriptase with fragmented mRNA as the template. During the synthesis of the second cDNA strand, end repair and A-tailing were performed simultaneously. Adaptors were then ligated to the cDNA fragments, and Hieff NGS® DNA Selection Beads were used for fragment purification and size selection. The library was amplified by PCR, and sequencing was performed on the Illumina NovaSeq X Plus platform. Data Statistical Analysis All experimental data were initially processed using Microsoft Excel 2019 and analyzed by an independent samples t -test with SPSS 26.0 software. The results ARE expressed as mean and Standard Error of the Mean (SEM), with statistical significance defined as P < 0.05 and extreme significance as P < 0.01. Graphs were generated using GraphPad Prism 8.0. Based on the analytical results, Spearman correlation analysis was performed to assess relationships between phenotypic and multi-omics data, and Mantel correlation analysis was used to evaluate associations among different omics datasets. These analyses were conducted to elucidate the mechanisms by which AMRF regulates antioxidant and immune capacities in the animals. In the results, significance levels are denoted as follows: * P < 0.05, ** P < 0.01, and *** P < 0.001. Results Effect of Dietary AMRF Supplementation on Growth Performance of SDGs As shown in Figs. 1 a and 1 b, the ADG and FBW of group A were significantly higher than those of group C ( P < 0.05). Effect of Dietary AMRF Supplementation on Liver Morphology and Histopathology of SDGs As shown in Figs. 2 a and 2 b, compared with group A, the liver morphological sections of group C exhibited loosely arranged hepatocytes, increased intercellular gaps (blue circles), slight tissue congestion (red circles), and swelling or degeneration (yellow circles). Figures 2 c and 2 d present the Oil Red O staining results of hepatic lipid droplets in groups A and C. Quantification of lipid droplet-stained area revealed no significant difference, with detailed data provided in Supplementary Fig. 1. Collectively, these findings indicate that 2.8 g/goat/day of AMRF did not induce liver damage. Effect of Dietary AMRF Supplementation on Rumen Fermentation Parameters of SDGs As shown in Table 1 , compared with group C, the pH( P = 0.044), MCP content( P = 0.029), and valeric acid concentration( P = 0.042) in group A were significantly increased, while the NH₃–N content was significantly decreased ( P = 0.041). Table 1 Effects of AMRF on rumen fermentation parameters in fattening SDGs Items Groups SEM P- value C A pH 6.76 b 6.91 a 0.07 0.044 NH 3 -N (mg/dL) 34.30 a 30.69 b 3.62 0.041 MCP (mg/dL) 25.41 b 29.46 a 1.60 0.029 Acetate acid (mmol/L) 18.19 15.33 1.28 0.490 Propionate acid (mmol/L) 8.98 9.24 0.46 0.676 Isobutyrate acid (mmol/L) 0.55 0.59 0.03 0.294 Butyrate acid (mmol/L) 3.21 3.47 0.59 0.682 Isovalerate acid (mmol/L) 0.62 0.66 0.12 0.783 Valerate acid (mmol/L) 1.21 b 1.53 a 0.10 0.042 TVFA (mmol/L) 32.76 30.82 4.60 0.698 Acetate acid /Propionate acid 2.05 1.64 0.44 0.392 Data were shown as mean and SEM. (n = 6). The data in the same row with different superscripts differ significantly at P < 0.05. Group definitions: C = control group (basal diet), A = AMRF-treated group (basal diet + 2.8 g/goat/d AMRF). NH₃-N: Ammonia nitrogen, MCP: Microbial protein, TVFA: Total volatile fatty acids. Effect of Dietary AMRF Supplementation on Serum Biochemical Indicators of SDGs As shown in Table 2 , compared with group C, the TP content ( P = 0.037) and IgA content ( P = 0.028) in group A were significantly increased, while the TC concentration ( P = 0.011) and GLU concentration ( P = 0.049) were significantly decreased. Table 2 Effects of AMRF on serum biochemical indicators in fattening SDGs Items Groups SEM P- value C A TP (g/L) 63.68 b 67.51 a 1.75 0.037 ALB (g/L) 28.31 30.47 0.22 0.108 TC (mmol/L) 2.61 a 2.14 b 0.15 0.011 TG (mmol/L) 0.25 0.27 0.07 0.725 AST (U/L) 95.56 95.63 8.06 0.993 ALT (U/L) 22.10 23.85 4.07 0.677 LDH (U/L) 374.50 416.57 30.66 0.200 CK (U/L) 171.12 224.11 35.06 0.162 ALP (U/L) 451.39 396.17 79.59 0.504 γ -GT (U/L) 38.92 43.16 3.78 0.289 HDL (mmol/L) 1.12 1.14 0.15 0.890 LDL (mmol/L) 0.99 1.12 0.11 0.272 CREA (µmol/L) 64.76 58.29 3.51 0.095 Ca (mmol/L) 2.44 2.66 0.11 0.080 P (mmol/L) 1.98 2.26 0.15 0.099 GLU (mmol/L) 3.21 a 2.78 b 0.19 0.049 IgA (g/L) 1.01 b 1.52 a 0.17 0.028 IgG (g/L) 17.64 17.80 1.23 0.897 IgM (g/L) 0.77 0.73 0.09 0.678 Data were shown as mean and SEM. (n = 6). The data in the same row with different superscripts differ significantly at P < 0.05. Group definitions: C = control group (basal diet), A = AMRF-treated group (basal diet + 2.8 g/goat/d AMRF). TP: Total protein, ALB: Albumin, TC: Total cholesterol, TG: Triglyceride, AST: Aspartate transaminase, ALT: Alanine transaminase, LDH: Lactate dehydrogenase, CK: Creatine kinase, ALP: Alkaline phosphatase, γ -GT: γ -Glutamyl transferase, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, CREA: Creatinine, GLU: Glucose, IgA: Immunoglobulin A, IgG: Immunoglobulin G, IgM: Immunoglobulin M. Effect of Dietary AMRF Supplementation on Serum Antioxidant Indicators of SDGs As shown in Table 3 , compared with group C, group A exhibited a significant increase in T-AOC by 0.59 mmol/L ( P = 0.001) and a significant decrease in MDA concentration by 1.83 nmol/L ( P = 0.030). Table 3 Effects of AMRF on serum antioxidant indicators in fattening SDGs Items Groups SEM P- value C A CAT (U/mL) 2.74 3.35 0.52 0.264 SOD (U/mL) 14.61 16.08 1.26 0.269 T-AOC (mmol/L) 2.13 B 2.72 A 0.13 0.001 MDA (nmol/L) 5.31 a 3.48 b 0.55 0.030 Data were shown as mean and SEM. (n = 6). Within the same row, different uppercase letter superscripts indicate extremely significant differences in the data of that row ( P < 0.01), while different lowercase lowercase letter superscripts indicate significant differences in the data of that row ( P < 0.05). Group definitions: C = control group (basal diet), A = AMRF-treated group (basal diet + 2.8 g/goat/d AMRF). CAT: Catalase, SOD: Superoxide dismutase, T-AOC: Total antioxidant capacity, MDA: Malondialdehyde. Analysis of Rumen, Cecum, and Colon Microbiota Alpha and Beta Diversity Analysis The inter-group comparison of alpha diversity indices (Supplementary Figs. 2a–c) showed no significant differences in microbial diversity among the rumen, cecum, and colon ( P > 0.05). To evaluate beta diversity, the Bray–Curtis index was calculated and visualized using a principal coordinate analysis (P-CoA) plot. As shown in Supplementary Figs. 3a–c, no significant separation was observed between the groups in the rumen, cecum, or colon. Inter-Group Comparison using Student's t -test Normality analysis indicated that bacterial abundance data at both the phylum and genus levels followed a normal distribution in the rumen ( P = 0.272), cecum ( P = 0.795), and colon ( P = 0.698). At the phylum level, no significant differences in microbial abundance were observed between groups in the rumen. However, in the rumen (Fig. 3a), four genera exhibited differential abundances: Alloprevotella ( P = 0.030, upregulated), Eubacterium_ruminantium_group ( P = 0.023, upregulated), Coprococcus ( P = 0.046, downregulated), and Escherichia – Shigella ( P = 0.031, downregulated). In the cecum (Fig. 3b), two phyla showed significant differences: Firmicutes were downregulated ( P = 0.029), while Bacteroidota were upregulated ( P = 0.049). At the genus level, significant differences were detected across all three intestinal regions. In the cecum (Fig. 3c), four genera differed significantly between groups: Christensenellaceae_R7_group ( P = 0.026, downregulated), Parvibacter ( P = 0.049, downregulated), Pseudobutyrivibrio ( P = 0.004, upregulated), and Lachnospira ( P = 0.016, upregulated). In the colon (Fig. 3d), although no phylum-level differences were observed, five genera showed significant variations: Alistipes ( P = 0.044, upregulated), Papillibacter ( P = 0.007, downregulated), Parvibacter ( P = 0.029, downregulated), Anaerofustis ( P = 0.039, downregulated), and Oscillospira ( P = 0.021, upregulated). At the phylum level, Firmicutes were significantly downregulated and Bacteroidota upregulated in the cecum. At the genus level, differential abundances were identified in the rumen ( Alloprevotella, Eubacterium _ ruminantium _ group, Coprococcus , and Escherichia–Shigella ), cecum ( Christensenellaceae _ R7_group, Parvibacter, Pseudobutyrivibrio , and Lachnospira ), and colon ( Alistipes, Papillibacter, Parvibacter, Anaerofustis , and Oscillospira ). Plasma Metabolomics Analysis OPLS-DA Score Plot, Model Validation, and Differential Metabolite Analysis The OPLS-DA model results showed a clear separation of metabolic profiles between groups C and A (Fig. 4 a). The model parameters were R²X(cum) = 0.534, R²Y(cum) = 0.996, and Q²(cum) = 0.541. The permutation test confirmed the absence of model overfitting, indicating good inter-group discrimination and reliable predictive performance (Q² >0.5) (Fig. 4 b). As shown in Fig. 4 c, the identified differential metabolites were classified into 11 categories, including 174 organic acids and derivatives (29.4%), 163 lipids and lipid-like molecules (27.6%), 78 organic heterocyclic compounds (13.2%), 57 benzene compounds (9.6%), 42 organic oxygen compounds (7.1%), 34 phenylpropanoids and polyketides (5.8%), 23 nucleosides, nucleotides, and analogs (3.9%), 11 compounds with unclassified information (1.9%), 5 lignans, neolignans, and related compounds (0.8%), 3 alkaloids and derivatives (0.5%), and 1 hydrocarbon (0.2%). As shown in Fig. 4 d, a total of 18 metabolites were significantly upregulated ( P < 0.05), while 107 metabolites were significantly downregulated ( P < 0.05). Metabolic Pathway and Key Differential Metabolite Analysis As shown in Fig. 5 a, eight metabolic pathways were significantly enriched in the KEGG analysis ( P < 0.05), including the arginine biosynthesis pathway, glutamatergic synapse pathway, long-term depression pathway, glutathione metabolism pathway, AA metabolism pathway, proximal tubule bicarbonate reclamation pathway, nitrogen metabolism pathway, and ovarian steroidogenesis pathway. Among the top 30 differential metabolites (Fig. 5 b) and the top 30 metabolites with VIP > 1 identified by the OPLS-DA model (Fig. 5 c), three metabolites were co-enriched across these eight pathways. These were identified as key differential metabolites, namely 12-HETE (upregulated), α-D-glucose (upregulated), and thromboxane B₂ (downregulated). Liver Transcriptomics Analysis PCA and Differential Gene Statistics As shown in Fig. 6a, PCA revealed a clear separation between groups C and A along the PC1 axis (48%), indicating substantial differences in liver transcriptomic profiles between the two groups. As shown in Fig. 6b, a total of 257 genes were significantly upregulated ( P < 0.05) and 231 genes were significantly downregulated ( P < 0.05). Screening of Key Differential Genes The KEGG pathway enrichment analysis is presented in Fig. 6c. Based on the criterion of P 40 in either group and |log₂FC| >0.6. TPM is Transcripts Per Kilobase of exon model per Million mapped reads, a standardized expression index obtained by "correcting gene length + correcting sequencing depth". The nine key differential genes were ND6 (upregulated), LOC100860813 (upregulated), CCNG1 (upregulated), LOC102168228 (upregulated), CSF1R (downregulated), FAM96A (upregulated), PTGS1 (downregulated), TCEAL8 (upregulated), and ITIH1 (downregulated). Correlation Analysis Correlation between Differential Phenotypes, Gastrointestinal Microorganisms, Key Plasma Metabolites, and Key Liver Genes As shown in Fig. 7 , several significant correlations were observed among differential phenotypes, gastrointestinal microbiota, plasma metabolites, and liver transcriptomic genes. Within the rumen microbiota, RF - g__Alloprevotella exhibited a moderate positive correlation with pH RF ( P = 0.0174, ρ = 0.6690) and a strong negative correlation with GLU ( P = 0.0153, ρ = −0.6783). In contrast, RF- g __ Escherichia – Shigella showed strong negative correlations with TP ( P = 0.0023, ρ = −0.7882) and IgA ( P = 0.0070, ρ = −0.7301), but a moderate positive correlation with GLU ( P = 0.0275, ρ = 0.6320). In the cecal microbiota, CEC- g __ Pseudobutyrivibrio had an extremely strong positive correlation with VA ( P = 0.0006, ρ = 0.8423) and a strong negative correlation with TC ( P = 0.0069, ρ = −0.7315). CEC- g __ Christensenellaceae _ R-7 _ group showed a strong positive correlation with GLU ( P = 0.0045, ρ = 0.7552) and a strong negative correlation with VA ( P = 0.0132, ρ = −0.6891). In addition, CEC- g __ Parvibacter displayed moderate positive correlations with TC ( P = 0.0171, ρ = 0.6702) and NH₃–N ( P = 0.0350, ρ = 0.6105). Regarding the colonic microbiota, COL- g __ Oscillospira was strongly positively correlated with MCP ( P = 0.0029, ρ = 0.7777), while COL- g __ Papillibacter showed strong negative correlations with VA ( P = 0.0091, ρ = −0.7138) and pH RF ( P = 0.0101, ρ = −0.7075). COL- g __ Parvibacter had a strong positive correlation with NH₃–N ( P = 0.0092, ρ = 0.7133), whereas COL- g __ Alistipes showed a moderate negative correlation with GLU ( P = 0.0118, ρ = −0.6970) and COL- g __ Anaerofustis had a moderate negative correlation with IgA ( P = 0.0308, ρ = −0.6222). Among plasma metabolites, α -D-glucose exhibited the strongest overall associations with phenotypic traits. It was extremely negatively correlated with IgA ( P = 0.0004, ρ = −0.8531), strongly negatively correlated with VA ( P = 0.0050, ρ = −0.7492) and TP ( P = 0.0058, ρ = −0.7413), and moderately negatively correlated with MCP ( P = 0.0240, ρ = −0.6434), suggesting a central role in immunity and nutrition-related regulation. Thromboxane B₂ showed moderate positive correlations with IgA ( P = 0.0153, ρ = 0.6783) and VA ( P = 0.0466, ρ = 0.5831), while 12-HETE displayed a moderate positive correlation with IgA ( P = 0.0479, ρ = 0.5804), both contributing to immune modulation. Correlations among liver transcriptomic genes further reflected the interaction between immunity, nutrient metabolism, and inflammation. CSF1R showed strong negative correlations with IgA ( P = 0.0014, ρ = −0.8112) and VA ( P = 0.0047, ρ = −0.7527), indicating its core role in immune and vitamin metabolism regulation. IL10RA demonstrated a strong negative correlation with VA ( P = 0.0047, ρ = −0.7527), a strong positive correlation with GLU ( P = 0.0065, ρ = 0.7343), and moderate negative correlations with TP ( P = 0.0126, ρ = −0.6923) and IgA ( P = 0.0283, ρ = −0.6294). Similarly, PTGS1 was strongly negatively correlated with VA ( P = 0.0031, ρ = −0.7739) and IgA ( P = 0.0102, ρ = −0.7063). CCNG1 had a strong negative correlation with NH₃–N ( P = 0.0022, ρ = −0.7902) and a strong positive correlation with VA ( P = 0.0069, ρ = 0.7315). Additionally, LOC102168228 exhibited a moderate negative correlation with TC ( P = 0.0126, ρ = −0.6923), while ND6 showed a moderate positive correlation with MCP ( P = 0.0202, ρ = 0.6573). Together, these genes form part of a coordinated regulatory network linking nutrient metabolism, antioxidant defense, and immune response. Mantel-test between Differential Gastrointestinal Microorganisms, Key Plasma Metabolites, and Key Liver Genes As shown in Fig. 8a, several strong associations were observed between key plasma metabolites and gastrointestinal microorganisms. 12-HETE exhibited an extremely significant and strong correlation with CEC -p__Firmicutes (Mantel’s r = 0.758, P = 0.001) and an extremely significant correlation with CEC -p__Bacteroidota (Mantel’s r = 0.647, P = 0.002). It also showed significant correlations with CEC -g__Pseudobutyrivibrio (Mantel’s r = 0.423, P = 0.011) and COL -g__Alistipes (Mantel’s r = 0.297, P = 0.029). α -D-glucose displayed extremely significant correlations with CEC -p__Firmicutes (Mantel’s r = 0.602, P = 0.004), CEC -p__Bacteroidota (Mantel’s r = 0.488, P = 0.009), and CEC -g__Pseudobutyrivibrio (Mantel’s r = 0.486, P = 0.010), as well as a significant correlation with COL -g__Alistipes (Mantel’s r = 0.303, P = 0.045). Similarly, thromboxane B₂ showed extremely significant correlations with CEC -p__Firmicutes (Mantel’s r = 0.496, P = 0.009) and significant correlations with CEC -p__Bacteroidota (Mantel’s r = 0.356, P = 0.016), CEC -g__Pseudobutyrivibrio (Mantel’s r = 0.316, P = 0.036), and COL -g__Alistipes (Mantel’s r = 0.263, P = 0.046). These results suggest that Firmicutes and Bacteroidota in the cecum were closely associated with plasma metabolic alterations. As shown in Fig. 8b, several genes also exhibited significant associations with gastrointestinal microorganisms. PTGS1 showed an extremely significant correlation with CEC- g__Christensenellaceae_R-7_group (Mantel’s r = 0.626, P = 0.010) and significant correlations with CEC -p__Firmicutes (Mantel’s r = 0.335, P = 0.041) and CEC -p__Bacteroidota (Mantel’s r = 0.343, P = 0.042). CSF1R was significantly correlated with RF -g__Escherichia – Shigella (Mantel’s r = 0.284, P = 0.043), COL -g__Papillibacter (Mantel’s r = 0.399, P = 0.013), and COL -g__Anaerofustis (Mantel’s r = 0.356, P = 0.016). IL10RA exhibited an extremely significant correlation with CEC -g__Christensenellaceae_R-7_group (Mantel’s r = 0.604, P = 0.002) and significant correlations with CEC -p__Firmicutes (Mantel’s r = 0.336, P = 0.036), CEC -p__Bacteroidota (Mantel’s r = 0.308, P = 0.044), and COL -g__Papillibacter (Mantel’s r = 0.402, P = 0.016). CCNG1 demonstrated extremely significant correlations with RF -g__Alloprevotella (Mantel’s r = 0.477, P = 0.006), RF -g__Eubacterium_ruminantium_group (Mantel’s r = 0.523, P = 0.005), CEC -g__Pseudobutyrivibrio (Mantel’s r = 0.423, P = 0.010), and COL -g__Papillibacter (Mantel’s r = 0.529, P = 0.004), as well as significant correlations with RF -g__Escherichia – Shigella (Mantel’s r = 0.328, P = 0.022) and COL -g__Oscillospira (Mantel’s r = 0.336, P = 0.019). These findings indicate a strong multi-omics linkage between intestinal microbial composition, plasma metabolites, and hepatic gene expression, highlighting key microbial taxa such as Firmicutes , Bacteroidota , and Christensenellaceae_R-7_group as central regulators in the AMRF-mediated metabolic network. Discussion In intensive farming systems, oxidative–antioxidative imbalance and immune suppression restrict ruminant production efficiency. As a characteristic plant extract in northwestern China, AMRF have been reported to possess antioxidant, anti-inflammatory, and intestinal regulatory properties. However, the systematic mechanism underlying their effects on dairy male goats remains unclear. In this study, the synergistic regulatory effects of AMRF on the rumen, cecal, and colonic microbiota, plasma metabolism, and liver gene expression of dairy male goats were systematically analyzed using gastrointestinal 16S rRNA sequencing, untargeted metabolomics, and transcriptomics approaches. The objective was to elucidate how AMRF enhance antioxidant and immune capacities and thereby improve growth performance. Effects of AMRF on Growth Performance, Liver Morphology, Histopathology, and Rumen Fermentation Previous studies have shown that flavanol glycosides are rapidly fermented in the rumen, producing large amounts of acetic acid and propionic acid, which serve as essential energy substrates for ruminants[ 20 ]. Similarly, other studies have demonstrated that plant-derived flavonoid extracts can increase total VFA production and modulate the acetate-to-propionate ratio, thereby improving energy utilization efficiency. It has been reported that supplementation with plant bioactive substances (such as flavonoids) reduces rumen NH₃–N accumulation and promotes the conversion of nitrogen to MCP, suggesting that AMRF may enhance nitrogen metabolism by reshaping the rumen microbial community[ 22 ]. From the perspective of rumen fermentation regulation, AMRF supplementation significantly improved both the fermentation environment and its efficiency. The observed increase in rumen fluid pH helped sustain anaerobic homeostasis, supported the proliferation of fiber-degrading bacteria (for instance, the elevated abundance of Alloprevotella ), and alleviated acid-induced epithelial injury. Furthermore, research indicates that an increase in rumen pH following AMRF supplementation may be linked to elevated VFA concentrations, particularly propionic and butyric acids, which further enhance energy utilization. The notable reduction in NH₃–N content and concurrent increase in MCP concentration were closely associated with the upregulation of Alloprevotella and Eubacterium_ruminantium_group . These genera efficiently degrade dietary carbohydrates and proteins, capture more NH₃–N for microbial protein synthesis, and supply 50–80% of the host’s protein requirement while reducing nitrogen waste[ 23 ]. Similar findings were reported in studies showing that mulberry leaf flavonoids reduce rumen NH₃–N and enhance MCP production[ 24 ], consistent with the present results. However, some flavonoids, such as myricetin, may reduce dry matter degradability and microbial protein synthesis efficiency[ 25 ]. The positive effects of AMRF observed here may be attributed to their specific components (for example, quercetin analogs), which exhibit no inhibitory effects on fermentation and have even been shown to suppress methane production in in vitro studies[ 24 ]. The significant increase in valeric acid concentration observed in the AMRF group is of particular importance. Valeric acid not only serves as a major energy substrate for rumen epithelial cells but also activates the expression of intestinal barrier-related genes (for example, Occludin, a tight junction protein)[ 26 , 27 ]. Meanwhile, it inhibits pathogenic bacteria (for example, Escherichia – Shigella , which was downregulated) and lactic acid-producing bacteria (for example, Coprococcus , which was downregulated), thereby reducing the risk of rumen acidosis[ 28 ]. Optimization of rumen fermentation directly contributes to improved growth performance. Both FBW and ADG were significantly higher in the AMRF group than in the control group[ 29 ]. The increase in MCP provided an adequate protein source, while the elevated energy supply from VFAs[ 30 ], particularly valeric acid, supported the metabolic and energetic demands of growth[ 31 ]. In addition, the reduction in pathogenic bacteria such as Escherichia–Shigella lowered the risk of intestinal infection[ 32 ], and the upregulation of intestinal β -defensin genes (for example, sBD-2) induced by AMRF further strengthened the mucosal immune barrier[ 33 ]. Together, these effects reduced immune stress, conserved energy otherwise diverted to immune defense, and enabled greater nutrient allocation toward growth[ 34 ]. Concurrently, AMRF protected liver tissue morphology through their antioxidant and anti-inflammatory activities. Reports indicate that AMRF can inhibit reactive oxygen species (ROS) formation by chelating metal ions (for example, Fe²⁺) and activate glutathione reductase, maintaining the recycling of glutathione (GSH). These mechanisms lead to a marked decrease in serum malondialdehyde (MDA), a biomarker of oxidative injury, and a significant increase in T-AOC, thereby preventing lipid peroxidation and protecting hepatocytes from oxidative damage[ 28 ]. Furthermore, AMRF inhibit cyclooxygenase-2 (COX-2) activity and the NF-κB signaling pathway, reducing the release of pro-inflammatory cytokines (for example, TNF-α and IL-6). This mitigates hepatocellular inflammation and concurrently upregulates CSF1R, which promotes the differentiation of M2-type macrophages that facilitate hepatocyte repair. Such hepatoprotective effects maintain a healthy metabolic environment conducive to protein synthesis and lipid metabolism, ultimately supporting improved growth performance[ 35 ]. In summary, AMRF indirectly enhance the growth performance of Saanen dairy male goats by optimizing rumen fermentation patterns. They promote the proliferation of beneficial fibrolytic bacteria such as Alloprevotella and Eubacterium _ ruminantium _ group , increase the production of VFAs (notably valeric acid), and suppress harmful microorganisms, including Escherichia – Shigella and Coprococcus . Collectively, these synergistic effects improve fermentation efficiency and contribute to the observed increases in FBW and ADG. Effects of AMRF on Serum Indicators The significant increase in TP observed in the AMRF group reflects an enhanced protein synthesis capacity, consistent with the nutritional support provided by increased rumen MCP. Meanwhile, the significant decreases in TC and GLU indicate that AMRF may reduce oxidative stress by inhibiting lipid synthesis and promoting glycolysis. Previous studies have confirmed that quercetin, a major component of onions, improves lipid metabolism and reduces oxidative stress induced by high-fat diets, which aligns with the observed decrease in MDA in the present study[ 36 ]. The substantial increase in IgA suggests enhanced mucosal immune function, which is closely linked to the anti-inflammatory activity of AMRF. The underlying mechanism involves the direct inhibition of pro-inflammatory cytokine release (for example, TNF-α and IL-6)[ 37 ]. In lipopolysaccharide (LPS)-induced inflammation models, pretreatment with AMRF significantly reduces both mRNA expression and protein levels of these cytokines, while stimulating the secretion of the anti-inflammatory cytokine IL-10[ 37 ], thereby protecting immune cells from inflammatory damage. Similarly, onion-derived flavonoids have been shown to suppress inflammation by downregulating the NF-κB pathway[ 38 ]. Enhancement of antioxidant capacity effectively mitigates oxidative damage, resulting in a synergistic “antioxidant–immune enhancement” effect. AMRF directly neutralize ROS through metal ion chelation and free radical scavenging, rather than relying on enzymatic antioxidant systems, as reflected by the unchanged activities of SOD and CAT. Likewise, mulberry leaf flavonoids have been reported to reduce oxidative stress markers such as MDA in dairy cows, alleviate heat stress, and improve systemic defense capacity[ 39 ]. Notably, AMRF also upregulate the expression of intestinal defensin genes (for example, sBD-1 and sBD-2), thereby strengthening the non-specific immune barrier[ 38 ]. In summary, AMRF enhance protein synthesis in dairy male goats by increasing rumen MCP and serum TP, while concurrently inhibiting lipid synthesis, promoting glycolysis, and reducing TC and GLU concentrations to alleviate oxidative stress. Furthermore, AMRF suppress the release of TNF-α and IL-6, stimulate IL-10 secretion, and elevate IgA levels, jointly contributing to improved antioxidant and immune functions. Effects of AMRF on Gastrointestinal Microbiota In the rumen microbiota, the abundances of Alloprevotella and Eubacterium _ ruminantium _ group were significantly upregulated, whereas those of Coprococcus and the pathogenic Escherichia – Shigella were markedly downregulated. These microbial shifts were consistent with the observed optimization of rumen fermentation parameters. As a core genus within the phylum Bacteroidota , Alloprevotella secretes cellulase and hemicellulase , efficiently degrading complex carbohydrates in feed such as those in corn and alfalfa. The increased abundance of Alloprevotella directly promoted the production of valeric acid, which not only serves as an essential energy source for rumen epithelial cells but also activates intestinal barrier-related genes (for example, Occludin, a tight junction protein), thereby minimizing intestinal leakage[ 40 ]. The genus Eubacterium _ ruminantium _ group participates in the deeper fiber degradation process, enhancing feed digestibility and supplying additional carbon skeletons for MCP synthesis, consistent with the significant rise in MCP observed in this study[ 41 ]. In contrast, excessive proliferation of Coprococcus may cause lactic acid accumulation (its main metabolite), while Escherichia – Shigella is a typical opportunistic pathogen. Their downregulation reduces the risks of rumen acidosis, which is consistent with the elevated rumen pH and intestinal infection[ 42 ]. Within the cecal microbiota, the abundance of Firmicutes was significantly downregulated, whereas Bacteroidota was upregulated. At the genus level, Pseudobutyrivibrio and Lachnospira were significantly upregulated, while Christensenellaceae_R7_group and Parvibacter were downregulated. The increase in Bacteroidota enhances the degradation of complex polysaccharides, reducing the accumulation of undigested residues in the cecum. Both Pseudobutyrivibrio and Lachnospira are major short-chain fatty acid (SCFA) producers[ 43 ]. The former mainly synthesizes butyric acid, while the latter produces acetic and propionic acids. These SCFAs not only lower intestinal pH but also activate host antioxidant pathways such as the Nrf2 pathway via G protein-coupled receptors (GPR41/43), thereby improving serum T-AOC[ 44 ]. Furthermore, Christensenellaceae_R7_group has been linked to fat deposition, and its downregulation may reduce fat absorption in the cecum[ 41 ], consistent with the significant decrease in serum TC, thereby confirming the role of cecal microbiota in host lipid metabolism regulation[ 45 ]. As the terminal region of digestion, the colon exhibited upregulation of Alistipes and Oscillospira , and downregulation of Papillibacter , Parvibacter , and Anaerofustis . Alistipes exerts anti-inflammatory effects, with increased abundance leading to the suppression of pro-inflammatory cytokine release (for example, IL-6 and TNF-α), thereby mitigating chronic intestinal inflammation. Oscillospira contributes to mucus layer integrity, and its metabolites stimulate goblet cell mucus secretion, strengthening the intestinal physical barrier and limiting endotoxin (LPS) entry into the bloodstream[ 40 ]. These findings align with the absence of serum inflammatory marker elevation. The downregulation of Papillibacter may reduce the production of putrefactive metabolites (for example, indoles and amines), decreasing the hepatic detoxification burden and indirectly protecting hepatocyte morphology, consistent with HE staining, which showed compact hepatocyte arrangement and the absence of swelling or degeneration in the AMRF group[ 41 ]. The gastrointestinal microbiota regulation exerted by AMRF exhibits segment-specific characteristics. In the rumen, AMRF upregulate fermentative functional bacteria ( Alloprevotella and Eubacterium _ ruminantium _ group ) to enhance fiber degradation and MCP synthesis, while downregulating Coprococcus and Escherichia–Shigella to reduce acidosis and infection risks, thus improving nutrient utilization efficiency. In the cecum and colon, AMRF upregulate Bacteroidota , Pseudobutyrivibrio, and Alistipes to promote SCFA production, activate antioxidant signaling, and exert anti-inflammatory and barrier-protective functions, while suppressing harmful bacteria to minimize the production of toxic metabolites[ 46 ]. Collectively, these region-specific responses form a microecological network that underpins the antioxidant and immune-enhancing mechanisms of AMRF in ruminants[ 47 ]. In summary, AMRF regulate the gastrointestinal microbiota of dairy male goats in a region-specific manner. In the rumen, AMRF enhance fermentation efficiency through the enrichment of Alloprevotella and Eubacterium _ ruminantium _ group and the suppression of Coprococcus and Escherichia – Shigella . In the cecum and colon, AMRF promote SCFA-producing bacteria such as Bacteroidota and Pseudobutyrivibrio , reinforcing antioxidant capacity, immune stability, and intestinal barrier function. Effects of AMRF on Plasma Metabolites In meat sheep fattening experiments, plasma metabolomics offers distinct advantages over serum metabolomics. Its preparation does not require coagulation, thereby improving efficiency and minimizing metabolite degradation during sample handling[ 48 ]. Plasma samples also retain coagulation-related metabolites, providing more comprehensive metabolic profiles that strengthen the correlation between nutritional and physiological states during the fattening phase[ 49 ]. Moreover, by using anticoagulants to prevent coagulation reactions, plasma analysis eliminates the influence of individual coagulation variability, resulting in greater metabolite stability, higher reproducibility, and a profile that more accurately reflects the in vivo metabolic state, making it ideal for controlled experimental studies[ 50 ]. Changes in the plasma metabolome reveal the regulatory effects of AMRF on both metabolic and defense systems. The differential metabolites identified in this study were mainly enriched in eight key pathways, including glutathione metabolism, AA metabolism, and arginine biosynthesis. Among these, three key metabolites (12-HETE, Thromboxane B₂, and Alpha-D-glucose) showed strong associations with serum antioxidant and immune indicators, elucidating the molecular mechanisms through which AMRF enhance host health at the metabolic level. Within the core metabolic pathways, activation of the glutathione metabolism pathway plays a central role in the antioxidant enhancement mediated by AMRF. Glutathione (GSH) serves as a critical non-enzymatic antioxidant, directly scavenging ROS and repairing oxidatively damaged proteins[ 51 ]. Although no significant changes were observed in precursor metabolites for GSH synthesis (for example, L-cysteine and glutamic acid), the significant decrease in serum MDA and the increase in T-AOC suggest that AMRF enhance antioxidant capacity primarily by promoting GSH recycling rather than increasing its de novo synthesis. This is consistent with the known mechanism of flavonoids. Quercetin, a major AMRF constituent, inhibits ROS generation through metal ion chelation (for example, Fe²⁺ and Cu²⁺)[ 52 ] and simultaneously activates glutathione reductase (GR) to convert oxidized glutathione (GSSG) back to reduced glutathione (GSH), thereby maintaining the GSH/GSSG redox balance and minimizing lipid peroxidation (MDA accumulation)[ 53 ]. Regulation of the AA metabolism pathway forms the biochemical basis of the anti-inflammatory effects of AMRF. AA serves as the precursor for inflammatory mediators such as prostaglandins and leukotrienes, and the relative abundance of its downstream metabolites directly influences immune homeostasis. In this study, 12-HETE was significantly upregulated, whereas Thromboxane B₂ was downregulated. 12-HETE, a product of the lipoxygenase (LOX) branch of AA metabolism, suppresses neutrophil chemotaxis and mitigates intestinal mucosal inflammation, while Thromboxane B₂, a cyclooxygenase (COX) pathway product, exerts pro-inflammatory and vasoconstrictive effects[ 54 ]. Thus, the coordinated upregulation of 12-HETE and downregulation of Thromboxane B₂ effectively attenuate inflammatory responses, aligning with the observed increase in serum IgA. As IgA represents the principal mucosal antibody, its elevated secretion depends on a reduction in intestinal inflammation, since inflammatory stress impairs B-cell differentiation into plasma cells. Moreover, isorhamnetin, another major AMRF flavonoid, has been shown to inhibit COX-2 activity, a key enzyme in prostaglandin synthesis, thereby reinforcing the notion that the AA metabolism pathway constitutes a core target of AMRF’s anti-inflammatory mechanism[ 55 ]. The enrichment of the arginine biosynthesis pathway is closely linked to the optimization of nitrogen metabolism. Arginine functions not only as an essential amino acid for protein synthesis but also as a precursor of nitric oxide (NO), which is produced through nitric oxide synthase (NOS) to regulate vasodilation and immune cell activity[ 56 ]. In this study, significant changes were detected in the metabolic levels of arginine precursors, including ornithine and citrulline. Combined with the significant reduction in rumen NH₃-N and the increase in MCP, these results suggest that AMRF enhance nitrogen utilization efficiency by improving rumen microecology, ultimately promoting protein synthesis. This aligns with the observed increase in serum TP by 3.83 g/L, indicating that AMRF optimize host nitrogen metabolism while reducing nitrogen waste. In addition, the key metabolite Alpha-D-glucose was significantly upregulated, whereas serum GLU was significantly downregulated. This inverse relationship between plasma metabolites and serum biochemical indicators implies that AMRF promote glucose transport and utilization within tissues. Alpha-D-glucose, the active form of glucose, reflects intestinal glucose absorption efficiency; its elevated plasma level indicates that more glucose enters the circulation, while the simultaneous decline in serum GLU suggests that target organs such as the liver and muscle actively increase glucose consumption for energy metabolism and antioxidant synthesis[ 57 ]. This metabolic adaptation alleviates glucose overload, mitigates oxidative stress, and prevents ROS overproduction caused by advanced glycation end products (AGEs) under hyperglycemic conditions. In conclusion, AMRF realigns the plasma metabolic network by modulating three key pathways: glutathione metabolism (enhancing GSH cycling), AA metabolism (upregulating 12-HETE and downregulating Thromboxane B₂), and arginine biosynthesis (improving nitrogen metabolism efficiency). These coordinated shifts redirect metabolic flux toward beneficial processes such as glutathione regeneration and anti-inflammatory mediator production, while reducing the accumulation of pro-inflammatory metabolites like Thromboxane B₂. Furthermore, by enhancing glucose utilization through Alpha-D-glucose regulation, AMRF provide comprehensive metabolic support that strengthens the host’s antioxidant defenses and immune function. Effects of AMRF on Liver Gene Expression As the metabolic and detoxification center of the body, the liver plays a central role in regulating overall physiological functions. Therefore, changes in liver gene expression represent a key mechanism through which AMRF exert systemic regulatory effects. The differential expression of PTGS1 , CSF1R , ND6 , and CCNG1 showed direct associations with plasma metabolic pathways such as AA metabolism, as well as with serum biochemical indicators including TC and TP, thereby revealing the molecular basis of AMRF’s integrated regulation of metabolism and immunity at the gene level. From the perspective of anti-inflammatory gene regulation, prostaglandin-endoperoxide synthase 1 ( PTGS1 ) was significantly upregulated. PTGS1 is an isoenzyme of cyclooxygenase (COX) involved in the synthesis of physiological prostaglandins, such as prostaglandin E₂ (PGE₂). Unlike the pro-inflammatory PTGS2 (COX-2), PGE₂ produced via PTGS1 maintains gastrointestinal mucosal protection and supports immune homeostasis[ 58 ]. In this study, the upregulation of PTGS1 , together with the downregulation of Thromboxane B₂ in plasma, suggests that AMRF modulate prostaglandin metabolism through a dual mechanism: inhibition of PTGS2 activity and enhancement of PTGS1 expression. Such coordinated regulation effectively reduces inflammatory responses. This mechanism aligns with the observed increase in serum IgA and decrease in intestinal pathogenic bacteria (for example, Escherichia – Shigella ), indicating that the liver indirectly supports intestinal mucosal immune function through gene-level regulation. In addition, interleukin 10 receptor alpha (IL10RA) was significantly upregulated. As the receptor for the anti-inflammatory cytokine IL-10, increased IL-10RA expression enhances hepatic sensitivity to IL-10, which suppresses the release of pro-inflammatory mediators such as TNF-α and IL-6. Together, the PTGS1–IL10RA axis constitutes a synergistic anti-inflammatory network that modulates immune responses and maintains hepatic immune balance[ 59 ]. From the perspective of immune-regulatory genes, colony-stimulating factor 1 receptor ( CSF1R ) was also upregulated. CSF1R , a critical receptor for macrophage activation, interacts with its ligand CSF1 to promote monocyte differentiation into M2-type (anti-inflammatory) macrophages[ 60 ]. These M2 macrophages secrete IL-10 and transforming growth factor β (TGF- β ), both of which attenuate inflammation and contribute to tissue repair, including hepatocyte regeneration[ 61 ]. In this study, the upregulation of CSF1R , combined with histological evidence that hepatocytes in the AMRF group exhibited no swelling or degeneration in HE staining, indicates that AMRF may activate the CSF1R signaling pathway, thereby enhancing the anti-inflammatory and repair capacities of Kupffer cells and reducing oxidative stress-induced liver damage. This interpretation is further supported by the finding that serum liver function enzymes, aspartate transaminase (AST), alanine transaminase (ALT), and gamma-glutamyl transferase ( γ -GT), showed no significant variations between groups, suggesting that AMRF exert a protective and stabilizing effect on hepatic function. From the perspective of energy metabolism–related genes, mitochondrial NADH dehydrogenase 6 ( ND6 ) was significantly downregulated. ND6 is a core subunit of mitochondrial respiratory chain complex I, and its expression level directly influences the efficiency of oxidative phosphorylation, which determines cellular energy production[ 62 ]. The downregulation of ND6 implies that AMRF may moderately reduce mitochondrial metabolic rates, thereby limiting ROS generation caused by respiratory chain electron leakage[ 63 ]. This mechanism is consistent with the observed decrease in serum MDA, indicating that the liver reduces endogenous ROS production through mitochondrial gene regulation, ultimately enhancing the body’s antioxidant defense capacity. In addition, cyclin G1 ( CCNG1 ) was significantly upregulated. CCNG1 participates in cell cycle regulation and can reduce energy expenditure by limiting unnecessary cell proliferation. At the same time, it activates the AMP-activated protein kinase (AMPK) pathway, which serves as an intracellular energy sensor that promotes fatty acid oxidation[ 64 ]. This activation is consistent with the reduction in serum TC by 0.47 mmol/L, suggesting that AMRF stimulate fatty acid catabolism and lipid utilization in the liver via the CCNG1 –AMPK signaling axis, thereby reducing hepatic fat accumulation and improving lipid metabolism efficiency. From the perspective of protein synthesis–related genes, alpha - 1-antitrypsin heavy chain ( ITIH1 ) was significantly upregulated. ITIH1 , primarily synthesized by the liver and abundantly expressed in normal hepatic tissue, belongs to the acute-phase protein family. Its expression is typically downregulated in hepatocellular carcinoma (HCC), with the degree of suppression inversely correlated with disease progression, implying a tumor-suppressive function. As a liver-derived acute-phase protein, ITIH1 contributes to protease inhibition (thereby reducing protein degradation) and promotes extracellular matrix synthesis and tissue repair[ 65 ]. The upregulation of ITIH1 observed here corresponds with the increase in serum TP, suggesting that AMRF enhance hepatic ITIH1 expression to reduce protein degradation and concurrently stimulate the synthesis of functional proteins, such as immunoglobulins and antioxidant enzymes. This effect establishes a “nitrogen source–protein synthesis” synergistic network, linking the increase in rumen MCP and activation of the arginine biosynthesis pathway to the enhancement of hepatic protein metabolism. These coordinated regulatory actions further substantiate the role of AMRF in optimizing systemic nitrogen utilization and protein synthesis efficiency. In conclusion, by modulating key hepatic genes associated with immunity ( PTGS1 , IL10RA , CSF1R ), energy metabolism ( ND6 , CCNG1 ), and protein synthesis ( ITIH1 ), AMRF strengthen the liver’s antioxidant and anti-inflammatory capacities, maintain protein and lipid metabolic homeostasis, and suppress pro-damaging inflammatory processes, thereby supporting overall metabolic stability and health in dairy male goats. This study used Spearman and Mantel correlation analyses to clarify the "microbe–metabolite–gene" multi-omics synergistic mechanism by which AMRF regulate phenotypic traits in Saanen dairy rams. At the microbial level, differential gastrointestinal bacteria showed region-specific associations: Rumen Alloprevotella correlated positively with rumen pH and MCP, and negatively with GLU, favoring rumen fermentation optimization; Cecal Pseudobutyrivibrio and colonic Oscillospira correlated positively with valeric acid and MCP, respectively, enhancing nutrient utilization and mucosal health; Escherichia – Shigella correlated negatively with TP and IgA, indicating AMRF reduce intestinal infections by suppressing pathogens. At the metabolite level: α-D-glucose strongly negatively correlated with IgA, valeric acid (VA), TP, and moderately with MCP, reflecting glucose metabolism balance and influencing immunity/protein synthesis via nutrient redistribution; 12-HETE positively correlated with IgA, while Thromboxane B₂ negatively correlated with IgA, confirming AMRF modulate the AA pathway to mitigate inflammation and enhance mucosal immunity. At the genetic level: Hepatic CSF1R strongly negatively correlated with IgA and VA, maintaining immune balance; PTGS1 negatively correlated with VA and IgA; CCNG1 negatively correlated with NH₃-N and positively with VA. These genes coordinate anti-inflammatory signaling and nitrogen metabolism stability, translating microbial/metabolic cues into improved antioxidant capacity, immunity, and growth. In conclusion, AMRF reshape gastrointestinal microecology by upregulating beneficial bacteria and suppressing harmful ones, creating a healthier environment for plasma metabolism. This drives key metabolites to modulate hepatic gene expression, forming a regulatory loop of microbial optimization, metabolic signaling, and gene integration—providing multi-omics evidence for AMRF's synergistic enhancement of host antioxidant capacity, immunity, and growth. Conclusion This study demonstrated that dietary supplementation of 2.8 g/goat/d AMRF significantly increased the FBW and ADG of SDGs, optimized rumen fermentation (with increased pH, MCP and valeric acid, and decreased NH₃-N), improved serum indicators (with increased TP and IgA, and decreased TC and GLU), and enhanced antioxidant capacity (with increased T-AOC and decreased MDA). Multi-omics analysis showed that AMRF upregulated beneficial microorganisms in the gastrointestinal tract, such as the genus Alloprevotella , phylum Bacteroidota , and genus Alistipes , while downregulating harmful microorganisms like Escherichia – Shigella . This provided a healthy substrate environment for plasma metabolism, drove the production of key plasma metabolites (12-HETE and α -D-glucose), reduced thromboxane B₂, activated the arginine biosynthesis and glutathione metabolism pathways, and further regulated the expression of key hepatic genes ( PTGS1 , CSF1R , and ND6 ). Ultimately, these effects synergistically enhanced the antioxidant and immune capacities of SDGs, further improved their growth performance, and provided a theoretical basis for the healthy breeding of ruminants. Abbreviations SDG Saanen dairy goat AMRF Allium mongolicum Regel flavonoids AMRP Allium mongolicum Regel powder FCR Feed-to-gain ratio HMDB Human Metabolome Database IBW Initial body weight Ig Immunoglobulin IL Interleukin Lefse Linear discriminant analysis effect size OPLS-DA Orthogonal partial least squares discriminant analysis PCA Principal component analysis SEM Standard Error of the Mean TPM Transcripts Per Kilobase of exon model per Million mapped reads VA Valerate VIP Variable importance in projection Declarations Author contributions XL: Writing-original draft, Validation, Formal analysis, Conceptualization. YAH: Writing-review & editing. XYD: Writing-review & editing. YRX: Software, Methodology. TWL: Software, Methodology. SCX: Software, Methodology. ZX: Visualization, Investigation. GBB: Visualization, Investigation. LZJ: Visualization, Investigation. FSC: Software, Validation. YQL: Software, Validation. LYT: Software, Validation. LWJ: Writing-review & editing, Supervision, Funding acquisition, Conceptualization. LZM: Writing-review & editing, Funding acquisition, Conceptualization. Acknowledgements The authors thank Weihe Dairy Co., Ltd., for providing access to the experimental site. Thank all those who have contributed to this experiment. Funding This work was supported by China Agriculture Research System (grant no. CARS-38); the National Natural Science Foundation of China (grant no. 32260846; 32402789); Agricultural Biological Breeding Major Program (grant no. 2022ZD04014); Science and Technology Support Project for Modern Cold and Arid Agriculture Seed Industry Breakthrough (grant no. ZYGG-2025-15); Gansu Provincial Science and Technology Major Project (grant no. 25ZDNA008); Gansu Provincial Department of Education, Industrial Support Program Project (grant no. 2024CYZC-36); Discipline Team Project of Gansu Agricultural University (grant no. GAU-XKTD-2022-22) Ethics approval and consent to participate The experimental protocol and animal care procedures were conducted following the guidelines of the Experimental Animal Protection Committee of Gansu Agricultural University (approval number: GSAU-Eth-AST-2022-001), in compliance with the ARRIVE 2.0 guidelines. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China. 2 Huanxian County Animal Husbandry and Veterinary Bureau, Qingyang, Gansu, 745700, China References Jin H, Liu J, Wang D. Antioxidant Potential of Exosomes in Animal Nutrition. 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05:40:44","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":243764,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/db9bf4a27ce34421e19d98f3.html"},{"id":96045995,"identity":"1eac4dc3-5ffe-45ad-becf-d27a0b3cfbff","added_by":"auto","created_at":"2025-11-17 05:40:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58142,"visible":true,"origin":"","legend":"\u003cp\u003eGroup definitions: \u003cstrong\u003eC\u003c/strong\u003e = control group (basal diet), \u003cstrong\u003eA\u003c/strong\u003e = AMRF-treated group (basal diet + 2.8 g/goat/d AMRF). Bar chart showing growth performance of SDGs. (\u003cstrong\u003ea\u003c/strong\u003e) Average daily gain (ADG), (\u003cstrong\u003eb\u003c/strong\u003e) Final body weight (FBW), (\u003cstrong\u003ec\u003c/strong\u003e) Feed conversion ratio (FCR), (\u003cstrong\u003ed\u003c/strong\u003e) Initial body weight (IBW). *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/7fc0a868c46dae19c78c347f.png"},{"id":96046032,"identity":"3d3671d7-0951-4c63-8c37-886af743038a","added_by":"auto","created_at":"2025-11-17 05:40:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1295441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b\u003c/strong\u003e: Liver tissue sections stained with hematoxylin-eosin (HE), used for observing liver tissue structure and hepatocyte morphology. The observation magnification is 100 μm. \u003cstrong\u003ec-d\u003c/strong\u003e: Frozen liver sections stained with Oil Red O (ORO) (lipid droplets appear red, and cell nuclei are counterstained with hematoxylin to appear blue). Group C is the control group fed with a basal diet; Group A is the treatment group fed with a basal diet supplemented with 2.8 g/goat/day AMRF.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/33ea3f1202a5de7bb4837768.png"},{"id":96046006,"identity":"10769797-ce06-4b7c-9bc1-e24f3ab9a9a6","added_by":"auto","created_at":"2025-11-17 05:40:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a\u003c/strong\u003e) Rumen (C, A) microbiota at the genus level, (\u003cstrong\u003eb\u003c/strong\u003e) Cecal (C_CEC, A_CEC) microbiota at the phylum level, (\u003cstrong\u003ec\u003c/strong\u003e) Cecal (C_CEC, A_CEC) microbiota at the genus level, (\u003cstrong\u003ed\u003c/strong\u003e) Colonic (C_COL, A_COL) microbiota at the genus level.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/e850323dd2ea36bca815c96b.png"},{"id":96046000,"identity":"7baae0e7-9aaa-4ad3-8091-78605b43c7c2","added_by":"auto","created_at":"2025-11-17 05:40:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":186198,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) score plot, showing distinct separation of metabolic profiles between the two groups; (\u003cstrong\u003eb\u003c/strong\u003e) OPLS-DA permutation test plot, with model parameters R²X(cum)=0.534, R²Y(cum)=0.996, Q²(cum)=0.541; permutation test (R²=0~0.9907, Q²=0~0.0572) confirmed no model overfitting; (\u003cstrong\u003ec\u003c/strong\u003e) Classification plot of differential metabolites (based on HMDB database); (d) Volcano plot of differential metabolites (VIP \u0026gt; 1 and \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05), with 18 metabolites significantly upregulated and 107 significantly downregulated. Sample size: n=6 (6 plasma samples per group).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/9b89e82f781990dfa3ca22aa.png"},{"id":96046015,"identity":"a6bddf0a-1eed-4851-8a02-19cc1fdad00e","added_by":"auto","created_at":"2025-11-17 05:40:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":242350,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) KEGG pathway enrichment bubble plot (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05), with 8 significantly enriched pathways including arginine biosynthesis, glutathione metabolism, and arachidonic acid metabolism (bubble size represents the number of differential metabolites; color represents \u003cem\u003eP\u003c/em\u003e-value); (\u003cstrong\u003eb\u003c/strong\u003e) Clustering heatmap of the top 30 differential metabolites, showing expression differences between the two groups (red = upregulated, blue = downregulated); (\u003cstrong\u003ec\u003c/strong\u003e) VIP value plot of the top 30 metabolites with VIP\u0026gt;1 in the OPLS-DA model. Sample size: n=6.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/a4c1b2849eec7ac27cf8ca73.png"},{"id":96247534,"identity":"5ed31b09-0963-4ae1-a0d2-3f1cfe0ed4b4","added_by":"auto","created_at":"2025-11-19 07:27:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":204402,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Principal Component Analysis (PCA) score plot, with PC1 axis (48%) showing significant separation of liver transcriptomic profiles between groups C and A; (\u003cstrong\u003eb\u003c/strong\u003e) Volcano plot of differential genes (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, FC\u0026gt;1.5); (\u003cstrong\u003ec\u003c/strong\u003e) KEGG pathway enrichment plot (top 20 pathways with \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05); (\u003cstrong\u003ed\u003c/strong\u003e) Radar plot of the top 30 differential genes. TPM = Transcripts Per Kilobase Million. Sample size: n=6.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/dda6e4a04c711c4bc39a64d1.png"},{"id":96046009,"identity":"1648bbd3-17c1-4175-9acd-854dcea6a74c","added_by":"auto","created_at":"2025-11-17 05:40:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":79871,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap showing associations among differential phenotypes, rumen (RF-g), cecal (CEC-p, g), and colonic (COL-g) bacteria, key plasma metabolites, and key liver transcriptomic genes. GLU: Glucose, NH₃-N: Ammonia nitrogen, TC: Total cholesterol, VA: Valeric acid, MCP: Microbial protein, TP: Total protein, IgA: Immunoglobulin A. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/b897680b0550779cf9ff2a0e.png"},{"id":96046045,"identity":"f3675f1a-dba3-4915-86b6-37e1e603de45","added_by":"auto","created_at":"2025-11-17 05:40:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":524646,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Mantel test between differential bacteria in the rumen, cecum, and colon and key plasma metabolites; (\u003cstrong\u003eb\u003c/strong\u003e) Mantel test between differential bacteria in the rumen, cecum, and colon and key differential genes in liver transcriptomics. 12-HETE: 12-Hydroxyeicosatetraenoic acid.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/edc2b54decef7dc9116742ce.png"},{"id":96046034,"identity":"7e54be4f-7660-4334-b759-4f62c88c5c2f","added_by":"auto","created_at":"2025-11-17 05:40:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":599656,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential mechanism of action of AMRF in the body. Red arrows indicate increased outcomes, and blue arrows indicate decreased outcomes.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/fd3733c8f3c5928b9117c145.png"},{"id":96256041,"identity":"850254b1-e524-4816-bed0-d1e46f1944bc","added_by":"auto","created_at":"2025-11-19 07:49:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5013828,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/7592fcfa-3ee4-496f-9dfc-78f376370963.pdf"},{"id":96045997,"identity":"0b3955da-9f5b-4fe3-b15a-0fe1227e1b69","added_by":"auto","created_at":"2025-11-17 05:40:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3711474,"visible":true,"origin":"","legend":"","description":"","filename":"Attachedfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/b46cce7c5724b747c8a8c4c6.docx"},{"id":96046001,"identity":"d91957f1-fdbc-4d90-a75a-eb43d2abc96b","added_by":"auto","created_at":"2025-11-17 05:40:40","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":622167,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-7997729/v1/170b22c55d6f7cdf67395946.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics insights into the effects of Allium mongolicum Regel flavonoids on growth, antioxidant capacity, and immune regulation in Saanen dairy male goats","fulltext":[{"header":"Background","content":"\u003cp\u003eUnder intensive farming systems, factors such as high-energy diets and environmental stress often disrupt the oxidative\u0026ndash;antioxidative balance in animals, leading to immune suppression. The coordination between oxidation, antioxidation, and immune response forms the foundation for efficient growth and overall health in livestock. Oxidative stress can impair cell integrity by inducing lipid peroxidation of membranes and inhibiting key enzymes, thereby reducing the digestion and absorption efficiency of nutrients. Similarly, weakened immune function forces the diversion of more energy toward pathogen defense rather than growth and development[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. During the fattening stage, ruminants are exposed to high temperatures, crowding, and energy-dense diets, all of which intensify metabolic pressure and energy imbalance[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These stressors trigger oxidative stress, such as increased malondialdehyde (MDA) levels and immune suppression, such as reduced immunoglobulin G (IgG) levels through multiple pathways, ultimately increasing the risk of diarrhea and respiratory infections[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies indicate that oxidative stress reduces feed conversion efficiency and average daily gain (ADG) in ruminants and markedly raises the incidence of disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, it is essential to identify safe and effective natural feed additives to counter these adverse effects.\u003c/p\u003e\u003cp\u003eFlavonoids, the principal bioactive components of plant-derived feed additives, play a vital role in alleviating oxidative damage under stress conditions. They improve immune responses and enhance production performance through their antioxidant, immunomodulatory, and intestinal health\u0026ndash;promoting functions. These plant-derived phytochemicals are divided into several subclasses, among which isoflavones (for example, daidzein) and flavanones (for example, citrus flavonoids) have attracted particular attention because of their strong bioavailability and widespread biological activities. Flavonoids exert their protective effects by donating electrons to neutralize free radicals and by stimulating antioxidant enzyme systems[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Previous research also demonstrates that flavonoids such as quercetin and kaempferol modulate immune cell activity (for example, by reducing the heterophil-to-lymphocyte ratio), reinforce intestinal barrier integrity, and suppress inflammatory cytokine release[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, supplementation with flavonoid-based additives has been shown to increase milk yield, milk fat, and protein content in dairy cows, while promoting weight gain and improving feed efficiency in beef cattle[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eAllium mongolicum\u003c/em\u003e Regel is a characteristic wild plant native to the arid and semi-arid regions of northwestern China. Its extracts are rich in active components such as flavonoids and polyphenols, which exhibit multiple biological activities, including antioxidation, anti-inflammation, and regulation of intestinal microecology[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have shown that dietary supplementation with \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel flavonoids (AMRF) in Small-tailed Han sheep can significantly improve production performance and overall health through two complementary mechanisms. First, AMRF directly modulates immune pathways by activating intestinal \u003cem\u003eβ\u003c/em\u003e-defensin genes (for example, sBD-2 expression increased by up to 171%) and pro-inflammatory cytokines (IL-1\u003cem\u003eβ\u003c/em\u003e, IL-6, and TNF-\u003cem\u003eα\u003c/em\u003e), thereby strengthening the mucosal immune barrier. Second, AMRF indirectly optimizes nutrient allocation, reduces immune stress losses, increases ADG by approximately 41%, and decreases the feed-to-gain ratio by 23.6%[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Further studies confirmed that the effective dietary dose of AMRF is 2.8 g per goat per day, which markedly enhances growth performance and serum antioxidant function in meat sheep. This improvement is closely associated with the modulation of the intestinal microbiota[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Compared with synthetic antioxidants, natural flavonoids have the advantages of high compatibility, low residue, and broader physiological regulation. They can influence functions of the intestine, liver, and immune system. Such regulatory potential has been partly demonstrated in poultry[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], pigs[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and meat sheep[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, grape seed extract rich in polyphenols has been shown to increase beneficial intestinal bacteria such as lactic acid bacteria, decrease pathogenic bacteria such as Clostridium, and improve intestinal morphology by increasing the villus height-to-crypt depth ratio[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another study reported that flavonoids such as glycyrrhiza flavonoids suppress the synthesis of pro-inflammatory mediators, including prostaglandins (for example, PGE₂) and leukotrienes (for example, LTB₄ and LTC₄) by inhibiting key enzymes in arachidonic acid (AA) metabolism, such as cyclooxygenase (COX) and lipoxygenase (LOX), thereby reducing inflammatory responses[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, AA and its metabolites can regulate voltage-gated ion channels (for example, calcium and potassium channels), influencing cell excitability and energy distribution[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Flavonoids may indirectly affect these ion channels by modulating AA metabolism, thereby influencing energy metabolism. However, systematic studies evaluating the above mechanisms in fattening dairy male goats remain limited. Therefore, we hypothesize that AMRF may regulate the gastrointestinal microbiota of dairy goats, optimize the rumen microecological environment, alter blood metabolites, and enrich metabolic pathways. In turn, the differential metabolites transported to the liver may further influence the expression of nutrient metabolism\u0026ndash;related genes, ultimately improving the growth performance of the animal. Additionally, the antioxidative and anti-inflammatory activities of AMRF may enhance the antioxidant and immune capacities, thereby indirectly promoting the growth of the dairy goat.\u003c/p\u003e\u003cp\u003eAs newborn ruminants, the rumen of SDG is physiologically immature, and their intestinal microbiota is highly susceptible to external influences. Although the early rumen microbial community is relatively simple, colonization begins rapidly after birth and reaches relative stability within 3\u0026ndash;4 months[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This developmental phase represents a \u0026ldquo;golden window\u0026rdquo; for nutritional and microbial intervention, as the microbial ecosystem exhibits high plasticity and the benefits of early intervention can persist into adulthood[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, this stage provides an ideal model for investigating the regulatory effects of non-antibiotic feed additives. In this study, AMRF was supplemented in the diet of SDGs, and an integrated multi-omics approach combining 16S rRNA sequencing, untargeted metabolomics, and transcriptomics was employed to systematically evaluate its effects on intestinal microbiota composition, plasma metabolites, and hepatic gene expression. Through this approach, we aimed to elucidate how AMRF regulates growth performance, antioxidant capacity, and immune function in dairy male goats. The findings provide a theoretical basis for the use of natural plant extracts in promoting the healthy and sustainable breeding of ruminants.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePreparation of AMRF\u003c/h2\u003e\u003cp\u003eFresh \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel was collected in Minqin County, Wuwei City, Gansu Province (36\u0026deg;29\u0026prime;N, 104\u0026deg;16\u0026prime;E) in June 2023. The cleaned leaves of \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel were dried to a constant weight in a constant-temperature drying oven (DZF-GW, Shanghai Binlin Electronic Technology Co., Ltd., Shanghai, China) at 60\u0026deg;C, then ground using a herbal grinder (CWF-300S, Zhejiang Top Medical Equipment Co., Ltd., Zhejiang, China) and sieved through a 1-mm mesh to obtain \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel powder (AMRP). The prepared powder was stored at 4\u0026deg;C until use.\u003c/p\u003e\u003cp\u003eThe extraction of AMRF was performed strictly according to the method described by Ding et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with an extraction yield of 28%. Ultra-high-performance liquid chromatography\u0026ndash;electrospray ionization\u0026ndash;tandem mass spectrometry (UPLC\u0026ndash;ESI\u0026ndash;MS/MS) was used to determine the relative contents of the main active components in AMRF, and the results are presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnimal Feeding and Management\u003c/h3\u003e\n\u003cp\u003eThe experimental protocol and animal care procedures were conducted following the guidelines of the Experimental Animal Protection Committee of Gansu Agricultural University (approval number: GSAU-Eth-AST-2022-001). The experiment was conducted between June 29 and October 30, 2024, at the Baicaoyuan Sheep Farm of Weihe Dairy Group, located in Huan County, Qingyang City, Gansu Province (36\u0026deg;34\u0026prime;N, 107\u0026deg;18\u0026prime;E; altitude approximately 1500 m). The study site has a temperate continental semi-arid climate, with an average annual precipitation of 300 mm and an average annual temperature of 9.2\u0026deg;C.\u003c/p\u003e\u003cp\u003eEighteen healthy castrated SDGs [aged 3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 months, with average body weights of 16.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 kg(Data were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)] were randomly assigned to two groups, each comprising nine goats. All animals were housed individually in pens. The control group received a basal diet, while the treatment group was provided with the same basal diet supplemented with 2.8 g AMRF per goat per day. This dosage was determined based on the optimal addition level of AMRP (10 g per goat per day) identified in a previous in vitro fermentation study on meat sheep by the research team, converted according to the AMRF extraction yield[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Before each feeding, the AMRF supplement was thoroughly mixed with 500 g of complete feed to ensure full consumption by the goats.\u003c/p\u003e\u003cp\u003eThe total experimental period lasted 139 d, including a 15-d adaptation period and a 124-d formal trial. The formal trial was divided into three phases: days 1\u0026ndash;30 (Phase 1), days 31\u0026ndash;60 (Phase 2), and days 61\u0026ndash;124 (Phase 3). Throughout the study, the goats had ad libitum access to drinking water and were fed twice daily at 08:00 and 18:00. The composition and chemical composition of the basal diet are listed in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\n\u003ch3\u003eDetermination of Growth Performance\u003c/h3\u003e\n\u003cp\u003eAll experimental goats underwent fasting weighing on the first and 124th days of the formal trial. The fasting period lasted 12 h, during which only water was provided. Body weight was measured using an electronic scale (model DH-108, Hunan Duheng Technology Co., Ltd., Hangzhou, China) with an accuracy of 0.01 kg. The body weight measured on the first day of the formal trial was defined as the initial body weight (IBW), and that measured on the 124th day was defined as the final body weight (FBW). The feed offered to each goat was recorded before feeding, and the remaining feed was collected and weighed before the next feeding to determine actual daily feed intake. After each experimental stage, the total feed intake (FI) was calculated. All feed intake data were recorded at gram-level precision to minimize weighing error in the calculation of performance parameters. Based on the recorded data (IBW, FBW, and FI), two growth performance indices were calculated: ADG and feed-to-gain ratio (FCR).\u003c/p\u003e\n\u003ch3\u003eBlood Collection\u003c/h3\u003e\n\u003cp\u003eAt the end of the fattening period, six SDGs with similar body weights (38.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57 kg) were selected from each group and transported to the slaughterhouse of Huan County Zhongsheng Sheep Industry Development Co., Ltd. for slaughter. On the day of slaughter, all animals were fasted for 12 h, after which 50 mL of blood was collected from the jugular vein. Of this, 30 mL was distributed into six 5-mL coagulated tubes and centrifuged at 25\u0026deg;C and 1100 \u0026times; \u003cem\u003eg\u003c/em\u003e for 10 min using a centrifuge (model TD5-2, Jiangsu Tianli Medical Equipment Co., Ltd., Jiangsu, China). The separated serum was collected into EP tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent analysis of serum biochemical and antioxidant indices. The remaining 20 mL of blood was placed into four 5-mL EDTA tubes containing an anticoagulant and centrifuged as before. The plasma was collected into EP tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent plasma metabolomics analysis.\u003c/p\u003e\n\u003ch3\u003eCollection of Liver and Gastrointestinal Contents\u003c/h3\u003e\n\u003cp\u003eImmediately after slaughter, liver tissue samples from the middle lobe were collected from the same anatomical site. One portion was fixed in 4% paraformaldehyde for hematoxylin\u0026ndash;eosin (HE) and Oil Red O (ORO) staining, while the remaining portion was rapidly frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for transcriptomic analysis. For the collection of rumen, cecum, and colon contents, each region was carefully separated immediately after slaughter. The contents were collected, subpackaged, and frozen in liquid nitrogen for subsequent microbial composition analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDetermination of Indicators\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eRumen Fermentation Parameters\u003c/h2\u003e\u003cp\u003eRumen fluid was filtered through three layers of gauze, temporarily stored in a liquid nitrogen tank, and subsequently transferred to the laboratory for storage at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis. Before collecting rumen fluid samples at the slaughterhouse, the pH of the rumen fluid was measured in situ using a pH meter. [main unit: Testo 205; probe: Testo 0550 1572; Testo (Shenzhen) Co., Ltd., Germany]. The instrument was calibrated within 1 h prior to measurement using standard buffer solutions with pH of 6.00, 6.86, and 7.00. It was equipped with an automatic temperature compensation function, and the stabilized pH reading was recorded. For the determination of volatile fatty acids (VFAs), gas chromatography (GC) was used with a flame ionization detector (FID). The analysis was performed using a Shimadzu GC-2010 (Shimadzu, Japan) equipped with a DB-FFAP capillary column (30 m \u0026times; 0.25 mm \u0026times; 0.25 \u0026micro;m; Agilent, USA). Nitrogen (purity\u0026thinsp;\u0026ge;\u0026thinsp;99.999%) was used as the carrier gas at a constant flow rate of 1 mL/min. The injection port and FID temperatures were maintained at 220\u0026deg;C and 250\u0026deg;C, respectively. The column temperature program was as follows: maintained at 40\u0026deg;C for 3 min, increased to 180\u0026deg;C at 5\u0026deg;C/min, and held for 5 min. The injection volume was 1 \u0026micro;L, with a split ratio of 50:1. Prior to injection, 1 mL of rumen fluid supernatant (after gauze filtration) was mixed with 20 \u0026micro;L of 50% phosphoric acid (v/v), vortexed for 1 min, centrifuged at 12,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 10 min at 4\u0026deg;C, and the supernatant was filtered through a 0.22 \u0026micro;m organic-phase membrane. The concentration of ammonia nitrogen (NH₃\u0026ndash;N) was determined by the phenol\u0026ndash;sodium hypochlorite colorimetric method. The supernatant obtained after centrifugation of the rumen fluid was mixed with a color developer and incubated in a 37\u0026deg;C water bath for 30 min. Absorbance was measured at 625 nm using a Shimadzu UV-2600 ultraviolet spectrophotometer (Shimadzu, Japan). A standard curve was generated using bovine serum albumin (BSA), and the NH₃\u0026ndash;N concentration was calculated accordingly. The concentration of microbial protein (MCP) was determined using the Coomassie Brilliant Blue G-250 staining method. The microbial pellet, obtained after centrifugation and washing, was digested with 0.5 mol/L NaOH at 100\u0026deg;C for 30 min. After cooling, a color developer was added, and the mixture was incubated at room temperature in the dark for 5 min. Absorbance was measured at 595 nm using a UV spectrophotometer. A BSA standard curve was used to calculate the MCP concentration.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eSerum Indicators\u003c/h3\u003e\n\u003cp\u003eSerum protein, hepatic and renal function parameters, lipid metabolism indices, and immune indicators were analyzed by Beijing Huaying Biotechnology Co., Ltd. using a Mindray BS-420 automatic biochemical analyzer (Shenzhen Mindray Biomedical Electronics Co., Ltd., Shenzhen, China). Antioxidant indicators were determined using commercial kits obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). The specific kits and catalog numbers were as follows: catalase (CAT), Kit No. A007-1-1; superoxide dismutase (SOD), Kit No. A001-3; total antioxidant capacity (T-AOC), Kit No. A015-2-1; and malondialdehyde (MDA), Kit No. A003-1. Absorbance values were read at 450, 593, 405 nm, and 532 nm using a microplate reader. All assays were conducted strictly following the manufacturer\u0026rsquo;s instructions, and each biochemical indicator was analyzed in six biological replicates.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLiver Tissue Morphology and Pathology\u003c/h2\u003e\u003cp\u003eParaffin-embedded liver tissue sections were dewaxed, rehydrated, stained with hematoxylin for 5 min and eosin for 30 s, dehydrated, cleared, and mounted. Hepatocyte morphology was examined under an optical microscope (Olympus BX53, Japan) using CaseViewer Native Windows Application 2.6 software at 100 \u0026micro;m magnification. Frozen liver sections were stained with ORO for 15 min and counterstained with hematoxylin to visualize lipid droplet distribution. Images from six randomly selected microscopic fields were captured using CaseViewer software. The proportion of lipid droplet area was quantified using Image-Pro Plus 6.0 software, and bar charts were generated with GraphPad Prism 9.5.0. Statistical analysis was performed using the \u003cem\u003et\u003c/em\u003e-test to assess differences between groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGastrointestinal Microbiota Analysis\u003c/h2\u003e\u003cp\u003eTotal genomic DNA from microbial communities in the rumen, cecum, and colon was extracted using the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-Tek, USA). DNA quality and concentration were verified by 1% agarose gel electrophoresis and NanoDrop 2000 spectrophotometry. The extracted DNA served as a template to amplify the V3\u0026ndash;V4 region of the 16S rRNA gene using polymerase chain reaction (PCR) with barcode-labeled specific primers: 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), where the 5' end of each primer was modified with a unique 8-bp barcode to distinguish different samples. PCR products were separated and purified on 2% agarose gels, quantified, and then used for library preparation with the NEXTFLEX Rapid DNA-Seq Kit. Sequencing was performed on the Illumina NextSeq 2000 platform.\u003c/p\u003e\u003cp\u003eRaw sequencing data were processed using fastp and FLASH software for quality control and sequence assembly. UPARSE was employed to cluster sequences into operational taxonomic units (OTUs) at 97% similarity and to remove chimeric sequences. After rarefaction, the average length of OTU representative sequences was 458\u0026thinsp;\u0026plusmn;\u0026thinsp;12 bp(consistent with the expected length of the 16S rRNA gene V3\u0026ndash;V4 region, ~\u0026thinsp;460 bp), indicating reliable amplification and sequencing quality. The RDP classifier was used to annotate sequences against the Silva database (v138), and PICRUSt2 was applied for functional prediction. All data analyses were completed on the Majorbio/Sanger Information Cloud Platform. Specifically, Mothur software was used to calculate alpha diversity indices (for example, Chao1 and Shannon indices), and the student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used to assess differences in alpha diversity between groups. Linear discriminant analysis effect size (LEfSe) was performed to identify bacterial taxa with significantly different abundances between groups (criteria: LDA\u0026thinsp;\u0026gt;\u0026thinsp;2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), covering taxonomic levels from phylum to genus, to identify differential and dominant microbial species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePlasma Metabolomics Analysis\u003c/h2\u003e\u003cp\u003eFor sample preparation, 100 \u0026micro;L of plasma was transferred into a 1.5-mL centrifuge tube, followed by the addition of 400 \u0026micro;L of acetonitrile\u0026ndash;methanol (1:1, v/v) extract containing four internal standards (IS): L-2-chlorophenylalanine (10 \u0026micro;g/mL), D4-succinic acid (5 \u0026micro;g/mL), D10-palmitic acid (2 \u0026micro;g/mL), and D3-creatinine (1 \u0026micro;g/mL). These internal standards were used to calibrate sample extraction efficiency and correct for instrumental drift during mass spectrometry analysis. The mixture was vortexed for 30 s, sonicated at 5\u0026deg;C for 30 min, and then allowed to stand at \u0026minus;\u0026thinsp;20\u0026deg;C for 30 min. After centrifugation at 13,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 15 min at 4\u0026deg;C, the supernatant was evaporated to dryness under nitrogen. The residue was redissolved in 100 \u0026micro;L of acetonitrile\u0026ndash;water (1:1, v/v), sonicated at 5\u0026deg;C for 5 min, and centrifuged again at 13,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 10 min at 4\u0026deg;C. The resulting supernatant was transferred into an injection vial for subsequent analysis.\u003c/p\u003e\u003cp\u003eChromatographic analysis was performed using an ultra-high-performance liquid chromatography (UHPLC) system coupled with an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific, USA). Separation was achieved on an HSS T3 column (100 mm \u0026times; 2.1 mm, 1.8 \u0026micro;m) with an injection volume of 3 \u0026micro;L. The mobile phases consisted of phase A (95% water and 5% acetonitrile containing 0.1% formic acid) and phase B (47.5% acetonitrile, 47.5% isopropanol, and 5% water containing 0.1% formic acid). The flow rate was 0.40 mL/min, and the column temperature was maintained at 40\u0026deg;C.\u003c/p\u003e\u003cp\u003eMass spectrometry was conducted in both positive and negative ionization modes over an m/z range of 70\u0026ndash;1050. The following conditions were used: sheath gas flow 50 psi, auxiliary gas flow 13 psi, auxiliary gas temperature 425\u0026deg;C, spray voltage\u0026thinsp;\u0026plusmn;\u0026thinsp;3500 V, ion transfer tube temperature 325\u0026deg;C, and stepped collision energies of 20, 40, and 60 V. The full-scan MS resolution was set at 60,000, and the tandem MS resolution at 7,500. Data were acquired in data-dependent acquisition (DDA) mode.\u003c/p\u003e\u003cp\u003eFor metabolite identification and analysis, raw data were processed using Progenesis QI software. Metabolite identification was performed by matching data to the Human Metabolome Database (HMDB), Metlin, and in-house databases. Data preprocessing involved application of the 80% rule for missing value removal, minimum value imputation, total ion current normalization, quality control relative standard deviation (QC-RSD)\u0026thinsp;\u0026lt;\u0026thinsp;30% filtering, and log10 transformation. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with 7-fold cross-validation were conducted using the ropls package in R. Differential metabolites were identified using criteria of variable importance in projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation and enrichment analysis were performed using Fisher\u0026rsquo;s exact test implemented in Python SciPy.Stats.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLiver Transcriptomics Analysis\u003c/h2\u003e\u003cp\u003eApproximately 20 mg of frozen liver tissue was ground into fine powder in liquid nitrogen, followed by the addition of lysis buffer containing guanidinium isothiocyanate (for example, TRIzol) to lyse cells and release RNA. Chloroform was added, mixed, and centrifuged to separate phases. The upper aqueous layer containing RNA was collected, and isopropanol was added for precipitation under low-temperature conditions. The RNA pellet was washed with 75% RNase-free ethanol, air-dried, and dissolved in RNase-free water. RNA quality and integrity were assessed using a UV spectrophotometer and agarose gel electrophoresis. Messenger RNA (mRNA) was isolated using mRNA Capture Beads and fragmented under controlled high-temperature conditions. The first complementary DNA (cDNA) strand was synthesized using reverse transcriptase with fragmented mRNA as the template. During the synthesis of the second cDNA strand, end repair and A-tailing were performed simultaneously. Adaptors were then ligated to the cDNA fragments, and Hieff NGS\u0026reg; DNA Selection Beads were used for fragment purification and size selection. The library was amplified by PCR, and sequencing was performed on the Illumina NovaSeq X Plus platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eData Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll experimental data were initially processed using Microsoft Excel 2019 and analyzed by an independent samples \u003cem\u003et\u003c/em\u003e-test with SPSS 26.0 software. The results ARE expressed as mean and Standard Error of the Mean (SEM), with statistical significance defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and extreme significance as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Graphs were generated using GraphPad Prism 8.0.\u003c/p\u003e\u003cp\u003eBased on the analytical results, Spearman correlation analysis was performed to assess relationships between phenotypic and multi-omics data, and Mantel correlation analysis was used to evaluate associations among different omics datasets. These analyses were conducted to elucidate the mechanisms by which AMRF regulates antioxidant and immune capacities in the animals. In the results, significance levels are denoted as follows: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Dietary AMRF Supplementation on Growth Performance of SDGs\u003c/h2\u003e\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, the ADG and FBW of group A were significantly higher than those of group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Dietary AMRF Supplementation on Liver Morphology and Histopathology of SDGs\u003c/h2\u003e\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, compared with group A, the liver morphological sections of group C exhibited loosely arranged hepatocytes, increased intercellular gaps (blue circles), slight tissue congestion (red circles), and swelling or degeneration (yellow circles). Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed present the Oil Red O staining results of hepatic lipid droplets in groups A and C. Quantification of lipid droplet-stained area revealed no significant difference, with detailed data provided in Supplementary Fig.\u0026nbsp;1. Collectively, these findings indicate that 2.8 g/goat/day of AMRF did not induce liver damage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Dietary AMRF Supplementation on Rumen Fermentation Parameters of SDGs\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, compared with group C, the pH(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), MCP content(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), and valeric acid concentration(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) in group A were significantly increased, while the NH₃\u0026ndash;N content was significantly decreased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of AMRF on rumen fermentation parameters in fattening SDGs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCP (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcetate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePropionate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsobutyrate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eButyrate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsovalerate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValerate acid (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTVFA (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcetate acid /Propionate acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData were shown as mean and SEM. (n\u0026thinsp;=\u0026thinsp;6). The data in the same row with different superscripts differ significantly at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Group definitions: \u003cb\u003eC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;control group (basal diet), \u003cb\u003eA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;AMRF-treated group (basal diet\u0026thinsp;+\u0026thinsp;2.8 g/goat/d AMRF). NH₃-N: Ammonia nitrogen, MCP: Microbial protein, TVFA: Total volatile fatty acids.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Dietary AMRF Supplementation on Serum Biochemical Indicators of SDGs\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, compared with group C, the TP content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) and IgA content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) in group A were significantly increased, while the TC concentration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and GLU concentration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) were significantly decreased.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of AMRF on serum biochemical indicators in fattening SDGs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.68\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e374.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e451.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eγ\u003c/em\u003e-GT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCREA (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLU (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgA (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgM (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData were shown as mean and SEM. (n\u0026thinsp;=\u0026thinsp;6). The data in the same row with different superscripts differ significantly at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Group definitions: \u003cb\u003eC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;control group (basal diet), \u003cb\u003eA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;AMRF-treated group (basal diet\u0026thinsp;+\u0026thinsp;2.8 g/goat/d AMRF). TP: Total protein, ALB: Albumin, TC: Total cholesterol, TG: Triglyceride, AST: Aspartate transaminase, ALT: Alanine transaminase, LDH: Lactate dehydrogenase, CK: Creatine kinase, ALP: Alkaline phosphatase, \u003cem\u003eγ\u003c/em\u003e-GT: \u003cem\u003eγ\u003c/em\u003e-Glutamyl transferase, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, CREA: Creatinine, GLU: Glucose, IgA: Immunoglobulin A, IgG: Immunoglobulin G, IgM: Immunoglobulin M.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Dietary AMRF Supplementation on Serum Antioxidant Indicators of SDGs\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, compared with group C, group A exhibited a significant increase in T-AOC by 0.59 mmol/L (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and a significant decrease in MDA concentration by 1.83 nmol/L (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of AMRF on serum antioxidant indicators in fattening SDGs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroups\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAT (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOD (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-AOC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.13\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.72\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDA (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.48\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData were shown as mean and SEM. (n\u0026thinsp;=\u0026thinsp;6). Within the same row, different uppercase letter superscripts indicate extremely significant differences in the data of that row (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while different lowercase lowercase letter superscripts indicate significant differences in the data of that row (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Group definitions: \u003cb\u003eC\u003c/b\u003e\u0026thinsp;=\u0026thinsp;control group (basal diet), \u003cb\u003eA\u003c/b\u003e\u0026thinsp;=\u0026thinsp;AMRF-treated group (basal diet\u0026thinsp;+\u0026thinsp;2.8 g/goat/d AMRF). CAT: Catalase, SOD: Superoxide dismutase, T-AOC: Total antioxidant capacity, MDA: Malondialdehyde.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of Rumen, Cecum, and Colon Microbiota\u003c/h2\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eAlpha and Beta Diversity Analysis\u003c/h2\u003e\u003cp\u003eThe inter-group comparison of alpha diversity indices (Supplementary Figs.\u0026nbsp;2a\u0026ndash;c) showed no significant differences in microbial diversity among the rumen, cecum, and colon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). To evaluate beta diversity, the Bray\u0026ndash;Curtis index was calculated and visualized using a principal coordinate analysis (P-CoA) plot. As shown in Supplementary Figs.\u0026nbsp;3a\u0026ndash;c, no significant separation was observed between the groups in the rumen, cecum, or colon.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInter-Group Comparison using Student's\u003c/b\u003e \u003cb\u003et\u003c/b\u003e\u003cb\u003e-test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNormality analysis indicated that bacterial abundance data at both the phylum and genus levels followed a normal distribution in the rumen (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.272), cecum (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.795), and colon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.698). At the phylum level, no significant differences in microbial abundance were observed between groups in the rumen. However, in the rumen (Fig.\u0026nbsp;3a), four genera exhibited differential abundances: \u003cem\u003eAlloprevotella\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030, upregulated), \u003cem\u003eEubacterium_ruminantium_group\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, upregulated), \u003cem\u003eCoprococcus\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046, downregulated), and \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031, downregulated). In the cecum (Fig.\u0026nbsp;3b), two phyla showed significant differences: Firmicutes were downregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), while Bacteroidota were upregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). At the genus level, significant differences were detected across all three intestinal regions. In the cecum (Fig.\u0026nbsp;3c), four genera differed significantly between groups: \u003cem\u003eChristensenellaceae_R7_group\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, downregulated), \u003cem\u003eParvibacter\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, downregulated), \u003cem\u003ePseudobutyrivibrio\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, upregulated), and \u003cem\u003eLachnospira\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, upregulated). In the colon (Fig.\u0026nbsp;3d), although no phylum-level differences were observed, five genera showed significant variations: \u003cem\u003eAlistipes\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, upregulated), \u003cem\u003ePapillibacter\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, downregulated), \u003cem\u003eParvibacter\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029, downregulated), \u003cem\u003eAnaerofustis\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039, downregulated), and \u003cem\u003eOscillospira\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, upregulated).\u003c/p\u003e\u003cp\u003eAt the phylum level, Firmicutes were significantly downregulated and \u003cem\u003eBacteroidota\u003c/em\u003e upregulated in the cecum. At the genus level, differential abundances were identified in the rumen (\u003cem\u003eAlloprevotella, Eubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup, Coprococcus\u003c/em\u003e, and \u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e), cecum (\u003cem\u003eChristensenellaceae\u003c/em\u003e_\u003cem\u003eR7_group, Parvibacter, Pseudobutyrivibrio\u003c/em\u003e, and \u003cem\u003eLachnospira\u003c/em\u003e), and colon (\u003cem\u003eAlistipes, Papillibacter, Parvibacter, Anaerofustis\u003c/em\u003e, and \u003cem\u003eOscillospira\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003ePlasma Metabolomics Analysis\u003c/h2\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eOPLS-DA Score Plot, Model Validation, and Differential Metabolite Analysis\u003c/h2\u003e\u003cp\u003eThe OPLS-DA model results showed a clear separation of metabolic profiles between groups C and A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The model parameters were R\u0026sup2;X(cum)\u0026thinsp;=\u0026thinsp;0.534, R\u0026sup2;Y(cum)\u0026thinsp;=\u0026thinsp;0.996, and Q\u0026sup2;(cum)\u0026thinsp;=\u0026thinsp;0.541. The permutation test confirmed the absence of model overfitting, indicating good inter-group discrimination and reliable predictive performance (Q\u0026sup2; \u0026gt;0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, the identified differential metabolites were classified into 11 categories, including 174 organic acids and derivatives (29.4%), 163 lipids and lipid-like molecules (27.6%), 78 organic heterocyclic compounds (13.2%), 57 benzene compounds (9.6%), 42 organic oxygen compounds (7.1%), 34 phenylpropanoids and polyketides (5.8%), 23 nucleosides, nucleotides, and analogs (3.9%), 11 compounds with unclassified information (1.9%), 5 lignans, neolignans, and related compounds (0.8%), 3 alkaloids and derivatives (0.5%), and 1 hydrocarbon (0.2%). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, a total of 18 metabolites were significantly upregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while 107 metabolites were significantly downregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eMetabolic Pathway and Key Differential Metabolite Analysis\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, eight metabolic pathways were significantly enriched in the KEGG analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including the arginine biosynthesis pathway, glutamatergic synapse pathway, long-term depression pathway, glutathione metabolism pathway, AA metabolism pathway, proximal tubule bicarbonate reclamation pathway, nitrogen metabolism pathway, and ovarian steroidogenesis pathway. Among the top 30 differential metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and the top 30 metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 identified by the OPLS-DA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), three metabolites were co-enriched across these eight pathways. These were identified as key differential metabolites, namely 12-HETE (upregulated), α-D-glucose (upregulated), and thromboxane B₂ (downregulated).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eLiver Transcriptomics Analysis\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section4\"\u003e\u003ch2\u003ePCA and Differential Gene Statistics\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;6a, PCA revealed a clear separation between groups C and A along the PC1 axis (48%), indicating substantial differences in liver transcriptomic profiles between the two groups. As shown in Fig.\u0026nbsp;6b, a total of 257 genes were significantly upregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and 231 genes were significantly downregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eScreening of Key Differential Genes\u003c/h2\u003e\u003cp\u003eThe KEGG pathway enrichment analysis is presented in Fig.\u0026nbsp;6c. Based on the criterion of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the top 30 differential genes were further screened. As shown in the radar chart (Fig.\u0026nbsp;6d), nine key differential genes were identified according to the selection criteria of TPM\u0026thinsp;\u0026gt;\u0026thinsp;40 in either group and |log₂FC| \u0026gt;0.6. TPM is Transcripts Per Kilobase of exon model per Million mapped reads, a standardized expression index obtained by \"correcting gene length\u0026thinsp;+\u0026thinsp;correcting sequencing depth\". The nine key differential genes were \u003cem\u003eND6\u003c/em\u003e (upregulated), \u003cem\u003eLOC100860813\u003c/em\u003e (upregulated), \u003cem\u003eCCNG1\u003c/em\u003e (upregulated), \u003cem\u003eLOC102168228\u003c/em\u003e (upregulated), \u003cem\u003eCSF1R\u003c/em\u003e (downregulated), \u003cem\u003eFAM96A\u003c/em\u003e (upregulated), \u003cem\u003ePTGS1\u003c/em\u003e (downregulated), \u003cem\u003eTCEAL8\u003c/em\u003e (upregulated), and \u003cem\u003eITIH1\u003c/em\u003e (downregulated).\u003c/p\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation between Differential Phenotypes, Gastrointestinal Microorganisms, Key Plasma Metabolites, and Key Liver Genes\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e, several significant correlations were observed among differential phenotypes, gastrointestinal microbiota, plasma metabolites, and liver transcriptomic genes. Within the rumen microbiota, \u003cem\u003eRF\u003c/em\u003e-\u003cem\u003eg__Alloprevotella\u003c/em\u003e exhibited a moderate positive correlation with pH\u003csub\u003eRF\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0174, ρ\u0026thinsp;=\u0026thinsp;0.6690) and a strong negative correlation with GLU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0153, ρ = \u0026minus;0.6783). In contrast, RF-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e showed strong negative correlations with TP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0023, ρ = \u0026minus;0.7882) and IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0070, ρ = \u0026minus;0.7301), but a moderate \u003cem\u003epositive\u003c/em\u003e correlation with GLU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0275, ρ\u0026thinsp;=\u0026thinsp;0.6320).\u003c/p\u003e\u003cp\u003eIn the cecal microbiota, CEC-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003ePseudobutyrivibrio\u003c/em\u003e had an extremely strong positive correlation with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006, ρ\u0026thinsp;=\u0026thinsp;0.8423) and a strong negative correlation with TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0069, ρ = \u0026minus;0.7315). CEC-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eChristensenellaceae\u003c/em\u003e_\u003cem\u003eR-7\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e showed a strong positive correlation with GLU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0045, ρ\u0026thinsp;=\u0026thinsp;0.7552) and a strong negative correlation with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0132, ρ = \u0026minus;0.6891). In addition, CEC-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eParvibacter\u003c/em\u003e displayed moderate positive correlations with TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0171, ρ\u0026thinsp;=\u0026thinsp;0.6702) and NH₃\u0026ndash;N (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0350, ρ\u0026thinsp;=\u0026thinsp;0.6105).\u003c/p\u003e\u003cp\u003eRegarding the colonic microbiota, COL-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eOscillospira\u003c/em\u003e was strongly positively correlated with MCP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029, ρ\u0026thinsp;=\u0026thinsp;0.7777), while COL-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003ePapillibacter\u003c/em\u003e showed strong negative correlations with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0091, ρ = \u0026minus;0.7138) and pH\u003csub\u003eRF\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0101, ρ = \u0026minus;0.7075). COL-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eParvibacter\u003c/em\u003e had a strong positive correlation with NH₃\u0026ndash;N (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0092, ρ\u0026thinsp;=\u0026thinsp;0.7133), whereas COL-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eAlistipes\u003c/em\u003e showed a moderate negative correlation with GLU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0118, ρ = \u0026minus;0.6970) and COL-\u003cem\u003eg\u003c/em\u003e__\u003cem\u003eAnaerofustis\u003c/em\u003e had a moderate negative correlation with IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0308, ρ = \u0026minus;0.6222).\u003c/p\u003e\u003cp\u003eAmong plasma metabolites, \u003cem\u003eα\u003c/em\u003e-D-glucose exhibited the strongest overall associations with phenotypic traits. It was extremely negatively correlated with IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004, ρ = \u0026minus;0.8531), strongly negatively correlated with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0050, ρ = \u0026minus;0.7492) and TP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0058, ρ = \u0026minus;0.7413), and moderately negatively correlated with MCP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0240, ρ = \u0026minus;0.6434), suggesting a central role in immunity and nutrition-related regulation. Thromboxane B₂ showed moderate positive correlations with IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0153, ρ\u0026thinsp;=\u0026thinsp;0.6783) and VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0466, ρ\u0026thinsp;=\u0026thinsp;0.5831), while 12-HETE displayed a moderate positive correlation with IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0479, ρ\u0026thinsp;=\u0026thinsp;0.5804), both contributing to immune modulation.\u003c/p\u003e\u003cp\u003eCorrelations among liver transcriptomic genes further reflected the interaction between immunity, nutrient metabolism, and inflammation. \u003cem\u003eCSF1R\u003c/em\u003e showed strong negative correlations with IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0014, ρ = \u0026minus;0.8112) and VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0047, ρ = \u0026minus;0.7527), indicating its core role in immune and vitamin metabolism regulation. \u003cem\u003eIL10RA\u003c/em\u003e demonstrated a strong negative correlation with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0047, ρ = \u0026minus;0.7527), a strong positive correlation with GLU (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0065, ρ\u0026thinsp;=\u0026thinsp;0.7343), and moderate negative correlations with TP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0126, ρ = \u0026minus;0.6923) and IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0283, ρ = \u0026minus;0.6294). Similarly, \u003cem\u003ePTGS1\u003c/em\u003e was strongly negatively correlated with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0031, ρ = \u0026minus;0.7739) and IgA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0102, ρ = \u0026minus;0.7063). \u003cem\u003eCCNG1\u003c/em\u003e had a strong negative correlation with NH₃\u0026ndash;N (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0022, ρ = \u0026minus;0.7902) and a strong positive correlation with VA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0069, ρ\u0026thinsp;=\u0026thinsp;0.7315). Additionally, \u003cem\u003eLOC102168228\u003c/em\u003e exhibited a moderate negative correlation with TC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0126, ρ = \u0026minus;0.6923), while \u003cem\u003eND6\u003c/em\u003e showed a moderate positive correlation with MCP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0202, ρ\u0026thinsp;=\u0026thinsp;0.6573). Together, these genes form part of a coordinated regulatory network linking nutrient metabolism, antioxidant defense, and immune response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eMantel-test between Differential Gastrointestinal Microorganisms, Key Plasma Metabolites, and Key Liver Genes\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;8a, several strong associations were observed between key plasma metabolites and gastrointestinal microorganisms. 12-HETE exhibited an extremely significant and strong correlation with CEC\u003cem\u003e-p__Firmicutes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.758, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and an extremely significant correlation with CEC\u003cem\u003e-p__Bacteroidota\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.647, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). It also showed significant correlations with CEC\u003cem\u003e-g__Pseudobutyrivibrio\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.423, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and COL\u003cem\u003e-g__Alistipes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.297, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e\u003cp\u003e\u003cem\u003eα\u003c/em\u003e-D-glucose displayed extremely significant correlations with CEC\u003cem\u003e-p__Firmicutes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.602, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), CEC\u003cem\u003e-p__Bacteroidota\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.488, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and CEC\u003cem\u003e-g__Pseudobutyrivibrio\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.486, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), as well as a significant correlation with COL\u003cem\u003e-g__Alistipes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.303, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). Similarly, thromboxane B₂ showed extremely significant correlations with CEC\u003cem\u003e-p__Firmicutes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.496, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and significant correlations with CEC\u003cem\u003e-p__Bacteroidota\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.356, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), CEC\u003cem\u003e-g__Pseudobutyrivibrio\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.316, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), and COL\u003cem\u003e-g__Alistipes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.263, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). These results suggest that Firmicutes and Bacteroidota in the cecum were closely associated with plasma metabolic alterations.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;8b, several genes also exhibited significant associations with gastrointestinal microorganisms. \u003cem\u003ePTGS1\u003c/em\u003e showed an extremely significant correlation with CEC-\u003cem\u003eg__Christensenellaceae_R-7_group\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.626, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and significant correlations with CEC\u003cem\u003e-p__Firmicutes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.335, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) and CEC\u003cem\u003e-p__Bacteroidota\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.343, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). \u003cem\u003eCSF1R\u003c/em\u003e was significantly correlated with RF\u003cem\u003e-g__Escherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.284, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), COL\u003cem\u003e-g__Papillibacter\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.399, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), and COL\u003cem\u003e-g__Anaerofustis\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.356, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e\u003cp\u003e\u003cem\u003eIL10RA\u003c/em\u003e exhibited an extremely significant correlation with CEC\u003cem\u003e-g__Christensenellaceae_R-7_group\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.604, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and significant correlations with CEC\u003cem\u003e-p__Firmicutes\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), CEC\u003cem\u003e-p__Bacteroidota\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.308, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), and COL\u003cem\u003e-g__Papillibacter\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.402, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). \u003cem\u003eCCNG1\u003c/em\u003e demonstrated extremely significant correlations with RF\u003cem\u003e-g__Alloprevotella\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), RF\u003cem\u003e-g__Eubacterium_ruminantium_group\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.523, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), CEC\u003cem\u003e-g__Pseudobutyrivibrio\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.423, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), and COL\u003cem\u003e-g__Papillibacter\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.529, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), as well as significant correlations with RF\u003cem\u003e-g__Escherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.328, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and COL\u003cem\u003e-g__Oscillospira\u003c/em\u003e (Mantel\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019).\u003c/p\u003e\u003cp\u003eThese findings indicate a strong multi-omics linkage between intestinal microbial composition, plasma metabolites, and hepatic gene expression, highlighting key microbial taxa such as \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, and \u003cem\u003eChristensenellaceae_R-7_group\u003c/em\u003e as central regulators in the AMRF-mediated metabolic network.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn intensive farming systems, oxidative\u0026ndash;antioxidative imbalance and immune suppression restrict ruminant production efficiency. As a characteristic plant extract in northwestern China, AMRF have been reported to possess antioxidant, anti-inflammatory, and intestinal regulatory properties. However, the systematic mechanism underlying their effects on dairy male goats remains unclear. In this study, the synergistic regulatory effects of AMRF on the rumen, cecal, and colonic microbiota, plasma metabolism, and liver gene expression of dairy male goats were systematically analyzed using gastrointestinal 16S rRNA sequencing, untargeted metabolomics, and transcriptomics approaches. The objective was to elucidate how AMRF enhance antioxidant and immune capacities and thereby improve growth performance.\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eEffects of AMRF on Growth Performance, Liver Morphology, Histopathology, and Rumen Fermentation\u003c/h2\u003e\u003cp\u003ePrevious studies have shown that flavanol glycosides are rapidly fermented in the rumen, producing large amounts of acetic acid and propionic acid, which serve as essential energy substrates for ruminants[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, other studies have demonstrated that plant-derived flavonoid extracts can increase total VFA production and modulate the acetate-to-propionate ratio, thereby improving energy utilization efficiency. It has been reported that supplementation with plant bioactive substances (such as flavonoids) reduces rumen NH₃\u0026ndash;N accumulation and promotes the conversion of nitrogen to MCP, suggesting that AMRF may enhance nitrogen metabolism by reshaping the rumen microbial community[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom the perspective of rumen fermentation regulation, AMRF supplementation significantly improved both the fermentation environment and its efficiency. The observed increase in rumen fluid pH helped sustain anaerobic homeostasis, supported the proliferation of fiber-degrading bacteria (for instance, the elevated abundance of \u003cem\u003eAlloprevotella\u003c/em\u003e), and alleviated acid-induced epithelial injury. Furthermore, research indicates that an increase in rumen pH following AMRF supplementation may be linked to elevated VFA concentrations, particularly propionic and butyric acids, which further enhance energy utilization. The notable reduction in NH₃\u0026ndash;N content and concurrent increase in MCP concentration were closely associated with the upregulation of \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eEubacterium_ruminantium_group\u003c/em\u003e. These genera efficiently degrade dietary carbohydrates and proteins, capture more NH₃\u0026ndash;N for microbial protein synthesis, and supply 50\u0026ndash;80% of the host\u0026rsquo;s protein requirement while reducing nitrogen waste[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similar findings were reported in studies showing that mulberry leaf flavonoids reduce rumen NH₃\u0026ndash;N and enhance MCP production[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], consistent with the present results. However, some flavonoids, such as myricetin, may reduce dry matter degradability and microbial protein synthesis efficiency[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The positive effects of AMRF observed here may be attributed to their specific components (for example, quercetin analogs), which exhibit no inhibitory effects on fermentation and have even been shown to suppress methane production in in vitro studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significant increase in valeric acid concentration observed in the AMRF group is of particular importance. Valeric acid not only serves as a major energy substrate for rumen epithelial cells but also activates the expression of intestinal barrier-related genes (for example, Occludin, a tight junction protein)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Meanwhile, it inhibits pathogenic bacteria (for example, \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e, which was downregulated) and lactic acid-producing bacteria (for example, \u003cem\u003eCoprococcus\u003c/em\u003e, which was downregulated), thereby reducing the risk of rumen acidosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Optimization of rumen fermentation directly contributes to improved growth performance. Both FBW and ADG were significantly higher in the AMRF group than in the control group[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The increase in MCP provided an adequate protein source, while the elevated energy supply from VFAs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], particularly valeric acid, supported the metabolic and energetic demands of growth[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, the reduction in pathogenic bacteria such as \u003cem\u003eEscherichia\u0026ndash;Shigella\u003c/em\u003e lowered the risk of intestinal infection[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and the upregulation of intestinal \u003cem\u003eβ\u003c/em\u003e-defensin genes (for example, sBD-2) induced by AMRF further strengthened the mucosal immune barrier[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Together, these effects reduced immune stress, conserved energy otherwise diverted to immune defense, and enabled greater nutrient allocation toward growth[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConcurrently, AMRF protected liver tissue morphology through their antioxidant and anti-inflammatory activities. Reports indicate that AMRF can inhibit reactive oxygen species (ROS) formation by chelating metal ions (for example, Fe\u0026sup2;⁺) and activate glutathione reductase, maintaining the recycling of glutathione (GSH). These mechanisms lead to a marked decrease in serum malondialdehyde (MDA), a biomarker of oxidative injury, and a significant increase in T-AOC, thereby preventing lipid peroxidation and protecting hepatocytes from oxidative damage[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, AMRF inhibit cyclooxygenase-2 (COX-2) activity and the NF-κB signaling pathway, reducing the release of pro-inflammatory cytokines (for example, TNF-α and IL-6). This mitigates hepatocellular inflammation and concurrently upregulates CSF1R, which promotes the differentiation of M2-type macrophages that facilitate hepatocyte repair. Such hepatoprotective effects maintain a healthy metabolic environment conducive to protein synthesis and lipid metabolism, ultimately supporting improved growth performance[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, AMRF indirectly enhance the growth performance of Saanen dairy male goats by optimizing rumen fermentation patterns. They promote the proliferation of beneficial fibrolytic bacteria such as \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e, increase the production of VFAs (notably valeric acid), and suppress harmful microorganisms, including \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e and \u003cem\u003eCoprococcus\u003c/em\u003e. Collectively, these synergistic effects improve fermentation efficiency and contribute to the observed increases in FBW and ADG.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEffects of AMRF on Serum Indicators\u003c/h3\u003e\n\u003cp\u003eThe significant increase in TP observed in the AMRF group reflects an enhanced protein synthesis capacity, consistent with the nutritional support provided by increased rumen MCP. Meanwhile, the significant decreases in TC and GLU indicate that AMRF may reduce oxidative stress by inhibiting lipid synthesis and promoting glycolysis. Previous studies have confirmed that quercetin, a major component of onions, improves lipid metabolism and reduces oxidative stress induced by high-fat diets, which aligns with the observed decrease in MDA in the present study[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe substantial increase in IgA suggests enhanced mucosal immune function, which is closely linked to the anti-inflammatory activity of AMRF. The underlying mechanism involves the direct inhibition of pro-inflammatory cytokine release (for example, TNF-α and IL-6)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In lipopolysaccharide (LPS)-induced inflammation models, pretreatment with AMRF significantly reduces both mRNA expression and protein levels of these cytokines, while stimulating the secretion of the anti-inflammatory cytokine IL-10[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], thereby protecting immune cells from inflammatory damage. Similarly, onion-derived flavonoids have been shown to suppress inflammation by downregulating the NF-κB pathway[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnhancement of antioxidant capacity effectively mitigates oxidative damage, resulting in a synergistic \u0026ldquo;antioxidant\u0026ndash;immune enhancement\u0026rdquo; effect. AMRF directly neutralize ROS through metal ion chelation and free radical scavenging, rather than relying on enzymatic antioxidant systems, as reflected by the unchanged activities of SOD and CAT. Likewise, mulberry leaf flavonoids have been reported to reduce oxidative stress markers such as MDA in dairy cows, alleviate heat stress, and improve systemic defense capacity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Notably, AMRF also upregulate the expression of intestinal defensin genes (for example, sBD-1 and sBD-2), thereby strengthening the non-specific immune barrier[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, AMRF enhance protein synthesis in dairy male goats by increasing rumen MCP and serum TP, while concurrently inhibiting lipid synthesis, promoting glycolysis, and reducing TC and GLU concentrations to alleviate oxidative stress. Furthermore, AMRF suppress the release of TNF-α and IL-6, stimulate IL-10 secretion, and elevate IgA levels, jointly contributing to improved antioxidant and immune functions.\u003c/p\u003e\n\u003ch3\u003eEffects of AMRF on Gastrointestinal Microbiota\u003c/h3\u003e\n\u003cp\u003eIn the rumen microbiota, the abundances of \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e were significantly upregulated, whereas those of \u003cem\u003eCoprococcus\u003c/em\u003e and the pathogenic \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e were markedly downregulated. These microbial shifts were consistent with the observed optimization of rumen fermentation parameters. As a core genus within the phylum \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e secretes cellulase and \u003cem\u003ehemicellulase\u003c/em\u003e, efficiently degrading complex carbohydrates in feed such as those in corn and alfalfa. The increased abundance of \u003cem\u003eAlloprevotella\u003c/em\u003e directly promoted the production of valeric acid, which not only serves as an essential energy source for rumen epithelial cells but also activates intestinal barrier-related genes (for example, Occludin, a tight junction protein), thereby minimizing intestinal leakage[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The genus \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e participates in the deeper fiber degradation process, enhancing feed digestibility and supplying additional carbon skeletons for MCP synthesis, consistent with the significant rise in MCP observed in this study[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast, excessive proliferation of \u003cem\u003eCoprococcus\u003c/em\u003e may cause lactic acid accumulation (its main metabolite), while \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e is a typical opportunistic pathogen. Their downregulation reduces the risks of rumen acidosis, which is consistent with the elevated rumen pH and intestinal infection[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWithin the cecal microbiota, the abundance of Firmicutes was significantly downregulated, whereas \u003cem\u003eBacteroidota\u003c/em\u003e was upregulated. At the genus level, \u003cem\u003ePseudobutyrivibrio\u003c/em\u003e and \u003cem\u003eLachnospira\u003c/em\u003e were significantly upregulated, while \u003cem\u003eChristensenellaceae_R7_group\u003c/em\u003e and \u003cem\u003eParvibacter\u003c/em\u003e were downregulated. The increase in \u003cem\u003eBacteroidota\u003c/em\u003e enhances the degradation of complex polysaccharides, reducing the accumulation of undigested residues in the cecum. Both Pseudobutyrivibrio and \u003cem\u003eLachnospira\u003c/em\u003e are major short-chain fatty acid (SCFA) producers[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The former mainly synthesizes butyric acid, while the latter produces acetic and propionic acids. These SCFAs not only lower intestinal pH but also activate host antioxidant pathways such as the Nrf2 pathway via G protein-coupled receptors (GPR41/43), thereby improving serum T-AOC[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, \u003cem\u003eChristensenellaceae_R7_group\u003c/em\u003e has been linked to fat deposition, and its downregulation may reduce fat absorption in the cecum[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], consistent with the significant decrease in serum TC, thereby confirming the role of cecal microbiota in host lipid metabolism regulation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs the terminal region of digestion, the colon exhibited upregulation of \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eOscillospira\u003c/em\u003e, and downregulation of \u003cem\u003ePapillibacter\u003c/em\u003e, \u003cem\u003eParvibacter\u003c/em\u003e, and \u003cem\u003eAnaerofustis\u003c/em\u003e. \u003cem\u003eAlistipes\u003c/em\u003e exerts anti-inflammatory effects, with increased abundance leading to the suppression of pro-inflammatory cytokine release (for example, IL-6 and TNF-α), thereby mitigating chronic intestinal inflammation. \u003cem\u003eOscillospira\u003c/em\u003e contributes to mucus layer integrity, and its metabolites stimulate goblet cell mucus secretion, strengthening the intestinal physical barrier and limiting endotoxin (LPS) entry into the bloodstream[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These findings align with the absence of serum inflammatory marker elevation. The downregulation of \u003cem\u003ePapillibacter\u003c/em\u003e may reduce the production of putrefactive metabolites (for example, indoles and amines), decreasing the hepatic detoxification burden and indirectly protecting hepatocyte morphology, consistent with HE staining, which showed compact hepatocyte arrangement and the absence of swelling or degeneration in the AMRF group[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe gastrointestinal microbiota regulation exerted by AMRF exhibits segment-specific characteristics. In the rumen, AMRF upregulate fermentative functional bacteria (\u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e) to enhance fiber degradation and MCP synthesis, while downregulating \u003cem\u003eCoprococcus\u003c/em\u003e and Escherichia\u0026ndash;Shigella to reduce acidosis and infection risks, thus improving nutrient utilization efficiency. In the cecum and colon, AMRF upregulate \u003cem\u003eBacteroidota\u003c/em\u003e, Pseudobutyrivibrio, and \u003cem\u003eAlistipes\u003c/em\u003e to promote SCFA production, activate antioxidant signaling, and exert anti-inflammatory and barrier-protective functions, while suppressing harmful bacteria to minimize the production of toxic metabolites[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Collectively, these region-specific responses form a microecological network that underpins the antioxidant and immune-enhancing mechanisms of AMRF in ruminants[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, AMRF regulate the gastrointestinal microbiota of dairy male goats in a region-specific manner. In the rumen, AMRF enhance fermentation efficiency through the enrichment of \u003cem\u003eAlloprevotella\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e_\u003cem\u003eruminantium\u003c/em\u003e_\u003cem\u003egroup\u003c/em\u003e and the suppression of \u003cem\u003eCoprococcus\u003c/em\u003e and \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e. In the cecum and colon, AMRF promote SCFA-producing bacteria such as \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003ePseudobutyrivibrio\u003c/em\u003e, reinforcing antioxidant capacity, immune stability, and intestinal barrier function.\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eEffects of AMRF on Plasma Metabolites\u003c/h2\u003e\u003cp\u003eIn meat sheep fattening experiments, plasma metabolomics offers distinct advantages over serum metabolomics. Its preparation does not require coagulation, thereby improving efficiency and minimizing metabolite degradation during sample handling[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Plasma samples also retain coagulation-related metabolites, providing more comprehensive metabolic profiles that strengthen the correlation between nutritional and physiological states during the fattening phase[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Moreover, by using anticoagulants to prevent coagulation reactions, plasma analysis eliminates the influence of individual coagulation variability, resulting in greater metabolite stability, higher reproducibility, and a profile that more accurately reflects the in vivo metabolic state, making it ideal for controlled experimental studies[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChanges in the plasma metabolome reveal the regulatory effects of AMRF on both metabolic and defense systems. The differential metabolites identified in this study were mainly enriched in eight key pathways, including glutathione metabolism, AA metabolism, and arginine biosynthesis. Among these, three key metabolites (12-HETE, Thromboxane B₂, and Alpha-D-glucose) showed strong associations with serum antioxidant and immune indicators, elucidating the molecular mechanisms through which AMRF enhance host health at the metabolic level.\u003c/p\u003e\u003cp\u003eWithin the core metabolic pathways, activation of the glutathione metabolism pathway plays a central role in the antioxidant enhancement mediated by AMRF. Glutathione (GSH) serves as a critical non-enzymatic antioxidant, directly scavenging ROS and repairing oxidatively damaged proteins[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although no significant changes were observed in precursor metabolites for GSH synthesis (for example, L-cysteine and glutamic acid), the significant decrease in serum MDA and the increase in T-AOC suggest that AMRF enhance antioxidant capacity primarily by promoting GSH recycling rather than increasing its de novo synthesis. This is consistent with the known mechanism of flavonoids. Quercetin, a major AMRF constituent, inhibits ROS generation through metal ion chelation (for example, Fe\u0026sup2;⁺ and Cu\u0026sup2;⁺)[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and simultaneously activates glutathione reductase (GR) to convert oxidized glutathione (GSSG) back to reduced glutathione (GSH), thereby maintaining the GSH/GSSG redox balance and minimizing lipid peroxidation (MDA accumulation)[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegulation of the AA metabolism pathway forms the biochemical basis of the anti-inflammatory effects of AMRF. AA serves as the precursor for inflammatory mediators such as prostaglandins and leukotrienes, and the relative abundance of its downstream metabolites directly influences immune homeostasis. In this study, 12-HETE was significantly upregulated, whereas Thromboxane B₂ was downregulated. 12-HETE, a product of the lipoxygenase (LOX) branch of AA metabolism, suppresses neutrophil chemotaxis and mitigates intestinal mucosal inflammation, while Thromboxane B₂, a cyclooxygenase (COX) pathway product, exerts pro-inflammatory and vasoconstrictive effects[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Thus, the coordinated upregulation of 12-HETE and downregulation of Thromboxane B₂ effectively attenuate inflammatory responses, aligning with the observed increase in serum IgA. As IgA represents the principal mucosal antibody, its elevated secretion depends on a reduction in intestinal inflammation, since inflammatory stress impairs B-cell differentiation into plasma cells. Moreover, isorhamnetin, another major AMRF flavonoid, has been shown to inhibit COX-2 activity, a key enzyme in prostaglandin synthesis, thereby reinforcing the notion that the AA metabolism pathway constitutes a core target of AMRF\u0026rsquo;s anti-inflammatory mechanism[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe enrichment of the arginine biosynthesis pathway is closely linked to the optimization of nitrogen metabolism. Arginine functions not only as an essential amino acid for protein synthesis but also as a precursor of nitric oxide (NO), which is produced through nitric oxide synthase (NOS) to regulate vasodilation and immune cell activity[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, significant changes were detected in the metabolic levels of arginine precursors, including ornithine and citrulline. Combined with the significant reduction in rumen NH₃-N and the increase in MCP, these results suggest that AMRF enhance nitrogen utilization efficiency by improving rumen microecology, ultimately promoting protein synthesis. This aligns with the observed increase in serum TP by 3.83 g/L, indicating that AMRF optimize host nitrogen metabolism while reducing nitrogen waste.\u003c/p\u003e\u003cp\u003eIn addition, the key metabolite Alpha-D-glucose was significantly upregulated, whereas serum GLU was significantly downregulated. This inverse relationship between plasma metabolites and serum biochemical indicators implies that AMRF promote glucose transport and utilization within tissues. Alpha-D-glucose, the active form of glucose, reflects intestinal glucose absorption efficiency; its elevated plasma level indicates that more glucose enters the circulation, while the simultaneous decline in serum GLU suggests that target organs such as the liver and muscle actively increase glucose consumption for energy metabolism and antioxidant synthesis[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This metabolic adaptation alleviates glucose overload, mitigates oxidative stress, and prevents ROS overproduction caused by advanced glycation end products (AGEs) under hyperglycemic conditions.\u003c/p\u003e\u003cp\u003eIn conclusion, AMRF realigns the plasma metabolic network by modulating three key pathways: glutathione metabolism (enhancing GSH cycling), AA metabolism (upregulating 12-HETE and downregulating Thromboxane B₂), and arginine biosynthesis (improving nitrogen metabolism efficiency). These coordinated shifts redirect metabolic flux toward beneficial processes such as glutathione regeneration and anti-inflammatory mediator production, while reducing the accumulation of pro-inflammatory metabolites like Thromboxane B₂. Furthermore, by enhancing glucose utilization through Alpha-D-glucose regulation, AMRF provide comprehensive metabolic support that strengthens the host\u0026rsquo;s antioxidant defenses and immune function.\u003c/p\u003e\u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\u003ch2\u003eEffects of AMRF on Liver Gene Expression\u003c/h2\u003e\u003cp\u003eAs the metabolic and detoxification center of the body, the liver plays a central role in regulating overall physiological functions. Therefore, changes in liver gene expression represent a key mechanism through which AMRF exert systemic regulatory effects. The differential expression of \u003cem\u003ePTGS1\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, \u003cem\u003eND6\u003c/em\u003e, and \u003cem\u003eCCNG1\u003c/em\u003e showed direct associations with plasma metabolic pathways such as AA metabolism, as well as with serum biochemical indicators including TC and TP, thereby revealing the molecular basis of AMRF\u0026rsquo;s integrated regulation of metabolism and immunity at the gene level.\u003c/p\u003e\u003cp\u003eFrom the perspective of anti-inflammatory gene regulation, prostaglandin-endoperoxide synthase 1 (\u003cem\u003ePTGS1\u003c/em\u003e) was significantly upregulated. \u003cem\u003ePTGS1\u003c/em\u003e is an isoenzyme of cyclooxygenase (COX) involved in the synthesis of physiological prostaglandins, such as prostaglandin E₂ (PGE₂). Unlike the pro-inflammatory \u003cem\u003ePTGS2\u003c/em\u003e (COX-2), PGE₂ produced via \u003cem\u003ePTGS1\u003c/em\u003e maintains gastrointestinal mucosal protection and supports immune homeostasis[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In this study, the upregulation of \u003cem\u003ePTGS1\u003c/em\u003e, together with the downregulation of Thromboxane B₂ in plasma, suggests that AMRF modulate prostaglandin metabolism through a dual mechanism: inhibition of \u003cem\u003ePTGS2\u003c/em\u003e activity and enhancement of \u003cem\u003ePTGS1\u003c/em\u003e expression. Such coordinated regulation effectively reduces inflammatory responses. This mechanism aligns with the observed increase in serum IgA and decrease in intestinal pathogenic bacteria (for example, \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e), indicating that the liver indirectly supports intestinal mucosal immune function through gene-level regulation. In addition, interleukin 10 receptor alpha (IL10RA) was significantly upregulated. As the receptor for the anti-inflammatory cytokine IL-10, increased IL-10RA expression enhances hepatic sensitivity to IL-10, which suppresses the release of pro-inflammatory mediators such as TNF-α and IL-6. Together, the PTGS1\u0026ndash;IL10RA axis constitutes a synergistic anti-inflammatory network that modulates immune responses and maintains hepatic immune balance[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom the perspective of immune-regulatory genes, colony-stimulating factor 1 receptor (\u003cem\u003eCSF1R\u003c/em\u003e) was also upregulated. \u003cem\u003eCSF1R\u003c/em\u003e, a critical receptor for macrophage activation, interacts with its ligand CSF1 to promote monocyte differentiation into M2-type (anti-inflammatory) macrophages[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These M2 macrophages secrete IL-10 and transforming growth factor \u003cem\u003eβ\u003c/em\u003e (TGF-\u003cem\u003eβ\u003c/em\u003e), both of which attenuate inflammation and contribute to tissue repair, including hepatocyte regeneration[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In this study, the upregulation of \u003cem\u003eCSF1R\u003c/em\u003e, combined with histological evidence that hepatocytes in the AMRF group exhibited no swelling or degeneration in HE staining, indicates that AMRF may activate the \u003cem\u003eCSF1R\u003c/em\u003e signaling pathway, thereby enhancing the anti-inflammatory and repair capacities of Kupffer cells and reducing oxidative stress-induced liver damage. This interpretation is further supported by the finding that serum liver function enzymes, aspartate transaminase (AST), alanine transaminase (ALT), and gamma-glutamyl transferase (\u003cem\u003eγ\u003c/em\u003e-GT), showed no significant variations between groups, suggesting that AMRF exert a protective and stabilizing effect on hepatic function.\u003c/p\u003e\u003cp\u003eFrom the perspective of energy metabolism\u0026ndash;related genes, mitochondrial \u003cem\u003eNADH dehydrogenase\u003c/em\u003e 6 (\u003cem\u003eND6\u003c/em\u003e) was significantly downregulated. \u003cem\u003eND6\u003c/em\u003e is a core subunit of mitochondrial respiratory chain complex I, and its expression level directly influences the efficiency of oxidative phosphorylation, which determines cellular energy production[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The downregulation of \u003cem\u003eND6\u003c/em\u003e implies that AMRF may moderately reduce mitochondrial metabolic rates, thereby limiting ROS generation caused by respiratory chain electron leakage[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This mechanism is consistent with the observed decrease in serum MDA, indicating that the liver reduces endogenous ROS production through mitochondrial gene regulation, ultimately enhancing the body\u0026rsquo;s antioxidant defense capacity. In addition, cyclin G1 (\u003cem\u003eCCNG1\u003c/em\u003e) was significantly upregulated. \u003cem\u003eCCNG1\u003c/em\u003e participates in cell cycle regulation and can reduce energy expenditure by limiting unnecessary cell proliferation. At the same time, it activates the AMP-activated protein kinase (AMPK) pathway, which serves as an intracellular energy sensor that promotes fatty acid oxidation[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This activation is consistent with the reduction in serum TC by 0.47 mmol/L, suggesting that AMRF stimulate fatty acid catabolism and lipid utilization in the liver via the \u003cem\u003eCCNG1\u003c/em\u003e\u0026ndash;AMPK signaling axis, thereby reducing hepatic fat accumulation and improving lipid metabolism efficiency.\u003c/p\u003e\u003cp\u003eFrom the perspective of protein synthesis\u0026ndash;related genes, \u003cem\u003ealpha\u003c/em\u003e-\u003cem\u003e1-antitrypsin heavy chain\u003c/em\u003e (\u003cem\u003eITIH1\u003c/em\u003e) was significantly upregulated. \u003cem\u003eITIH1\u003c/em\u003e, primarily synthesized by the liver and abundantly expressed in normal hepatic tissue, belongs to the acute-phase protein family. Its expression is typically downregulated in hepatocellular carcinoma (HCC), with the degree of suppression inversely correlated with disease progression, implying a tumor-suppressive function. As a liver-derived acute-phase protein, \u003cem\u003eITIH1\u003c/em\u003e contributes to protease inhibition (thereby reducing protein degradation) and promotes extracellular matrix synthesis and tissue repair[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The upregulation of ITIH1 observed here corresponds with the increase in serum TP, suggesting that AMRF enhance hepatic \u003cem\u003eITIH1\u003c/em\u003e expression to reduce protein degradation and concurrently stimulate the synthesis of functional proteins, such as immunoglobulins and antioxidant enzymes. This effect establishes a \u0026ldquo;nitrogen source\u0026ndash;protein synthesis\u0026rdquo; synergistic network, linking the increase in rumen MCP and activation of the arginine biosynthesis pathway to the enhancement of hepatic protein metabolism. These coordinated regulatory actions further substantiate the role of AMRF in optimizing systemic nitrogen utilization and protein synthesis efficiency. In conclusion, by modulating key hepatic genes associated with immunity (\u003cem\u003ePTGS1\u003c/em\u003e, \u003cem\u003eIL10RA\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e), energy metabolism (\u003cem\u003eND6\u003c/em\u003e, \u003cem\u003eCCNG1\u003c/em\u003e), and protein synthesis (\u003cem\u003eITIH1\u003c/em\u003e), AMRF strengthen the liver\u0026rsquo;s antioxidant and anti-inflammatory capacities, maintain protein and lipid metabolic homeostasis, and suppress pro-damaging inflammatory processes, thereby supporting overall metabolic stability and health in dairy male goats.\u003c/p\u003e\u003cp\u003eThis study used Spearman and Mantel correlation analyses to clarify the \"microbe\u0026ndash;metabolite\u0026ndash;gene\" multi-omics synergistic mechanism by which AMRF regulate phenotypic traits in Saanen dairy rams. At the microbial level, differential gastrointestinal bacteria showed region-specific associations: Rumen \u003cem\u003eAlloprevotella\u003c/em\u003e correlated positively with rumen pH and MCP, and negatively with GLU, favoring rumen fermentation optimization; Cecal \u003cem\u003ePseudobutyrivibrio\u003c/em\u003e and colonic \u003cem\u003eOscillospira\u003c/em\u003e correlated positively with valeric acid and MCP, respectively, enhancing nutrient utilization and mucosal health; \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e correlated negatively with TP and IgA, indicating AMRF reduce intestinal infections by suppressing pathogens. At the metabolite level: α-D-glucose strongly negatively correlated with IgA, valeric acid (VA), TP, and moderately with MCP, reflecting glucose metabolism balance and influencing immunity/protein synthesis via nutrient redistribution; 12-HETE positively correlated with IgA, while Thromboxane B₂ negatively correlated with IgA, confirming AMRF modulate the AA pathway to mitigate inflammation and enhance mucosal immunity. At the genetic level: Hepatic \u003cem\u003eCSF1R\u003c/em\u003e strongly negatively correlated with IgA and VA, maintaining immune balance; \u003cem\u003ePTGS1\u003c/em\u003e negatively correlated with VA and IgA; \u003cem\u003eCCNG1\u003c/em\u003e negatively correlated with NH₃-N and positively with VA. These genes coordinate anti-inflammatory signaling and nitrogen metabolism stability, translating microbial/metabolic cues into improved antioxidant capacity, immunity, and growth.\u003c/p\u003e\u003cp\u003eIn conclusion, AMRF reshape gastrointestinal microecology by upregulating beneficial bacteria and suppressing harmful ones, creating a healthier environment for plasma metabolism. This drives key metabolites to modulate hepatic gene expression, forming a regulatory loop of microbial optimization, metabolic signaling, and gene integration\u0026mdash;providing multi-omics evidence for AMRF's synergistic enhancement of host antioxidant capacity, immunity, and growth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that dietary supplementation of 2.8 g/goat/d AMRF significantly increased the FBW and ADG of SDGs, optimized rumen fermentation (with increased pH, MCP and valeric acid, and decreased NH₃-N), improved serum indicators (with increased TP and IgA, and decreased TC and GLU), and enhanced antioxidant capacity (with increased T-AOC and decreased MDA). Multi-omics analysis showed that AMRF upregulated beneficial microorganisms in the gastrointestinal tract, such as the genus \u003cem\u003eAlloprevotella\u003c/em\u003e, phylum \u003cem\u003eBacteroidota\u003c/em\u003e, and genus \u003cem\u003eAlistipes\u003c/em\u003e, while downregulating harmful microorganisms like \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e. This provided a healthy substrate environment for plasma metabolism, drove the production of key plasma metabolites (12-HETE and \u003cem\u003eα\u003c/em\u003e-D-glucose), reduced thromboxane B₂, activated the arginine biosynthesis and glutathione metabolism pathways, and further regulated the expression of key hepatic genes (\u003cem\u003ePTGS1\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, and \u003cem\u003eND6\u003c/em\u003e). Ultimately, these effects synergistically enhanced the antioxidant and immune capacities of SDGs, further improved their growth performance, and provided a theoretical basis for the healthy breeding of ruminants.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSDG\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Saanen dairy goat\u003c/p\u003e\n\u003cp\u003eAMRF\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel flavonoids\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAMRP\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel powder\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFCR\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Feed-to-gain ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHMDB\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Human Metabolome Database\u003c/p\u003e\n\u003cp\u003eIBW\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Initial body weight\u003c/p\u003e\n\u003cp\u003eIg\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immunoglobulin\u003c/p\u003e\n\u003cp\u003eIL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Interleukin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLefse\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Linear discriminant analysis effect size\u003c/p\u003e\n\u003cp\u003eOPLS-DA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Orthogonal partial least squares discriminant analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEM\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Error of the Mean\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTPM\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcripts Per Kilobase of exon model per Million mapped reads\u003c/p\u003e\n\u003cp\u003eVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Valerate\u003c/p\u003e\n\u003cp\u003eVIP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Variable importance in projection\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXL: Writing-original draft, Validation, Formal analysis, Conceptualization. YAH: Writing-review \u0026amp; editing. XYD: Writing-review \u0026amp; editing. YRX: Software, Methodology. TWL: Software, Methodology. SCX: Software, Methodology. ZX: Visualization, Investigation. GBB: Visualization, Investigation. LZJ: Visualization, Investigation. FSC: Software, Validation. YQL: Software, Validation. LYT: Software, Validation. LWJ: Writing-review \u0026amp; editing, Supervision, Funding acquisition, Conceptualization. LZM: Writing-review \u0026amp; editing, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Weihe Dairy Co., Ltd., for providing access to the experimental site.\u003c/p\u003e\n\u003cp\u003eThank all those who have contributed to this experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by China Agriculture Research System (grant no. CARS-38); the National Natural Science Foundation of China (grant no. 32260846; 32402789); Agricultural Biological Breeding Major Program (grant no. 2022ZD04014); Science and Technology Support Project for Modern Cold and Arid Agriculture Seed Industry Breakthrough (grant no. ZYGG-2025-15); Gansu Provincial Science and Technology Major Project (grant no. 25ZDNA008); Gansu Provincial Department of Education, Industrial Support Program Project (grant no. 2024CYZC-36); Discipline Team Project of Gansu Agricultural University (grant no. GAU-XKTD-2022-22)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol and animal care procedures were conducted following the guidelines of the Experimental Animal Protection Committee of Gansu Agricultural University (approval number: GSAU-Eth-AST-2022-001), in compliance with the ARRIVE 2.0 guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Huanxian County Animal Husbandry and Veterinary Bureau, Qingyang, Gansu, 745700, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJin H, Liu J, Wang D. 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Adv Sci. 2024;11(42):e2401013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.http://doi.org/10.1002/advs.202401013\u003c/span\u003e\u003cspan address=\".10.1002/advs.202401013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Allium mongolicum Regel flavonoids, Saanen dairy male goats, growth performance, antioxidant, immunity, multi-omics","lastPublishedDoi":"10.21203/rs.3.rs-7997729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7997729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn intensive farming systems, oxidative stress and immune suppression often limit the production performance of ruminants. \u003cem\u003eAllium mongolicum\u003c/em\u003e Regel flavonoids (AMRF), a characteristic plant-derived bioactive compound found in Northwest China, have shown potential antioxidant, anti-inflammatory, and intestinal microecological regulatory effects. However, their mechanism of action in Saanen dairy goat (SDG) remains unclear. This study investigated the regulatory effects of AMRF on the growth performance, antioxidant capacity, and immune function of SDGs using multi-omics approaches.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEighteen healthy castrated SDGs (3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 months old) with similar body weights (16.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 kg) were selected and randomly assigned to two groups (n\u0026thinsp;=\u0026thinsp;9 each), with all animals housed in individual pens. The control group received a basal diet, while the treatment group received 2.8 g AMRF per goat per day. The experimental period lasted 139 d, including a 15-d adaptation and a 124-d formal trial. Compared with the control group, dietary supplementation of AMRF significantly increased final body weight and average daily gain in SDGs. Among rumen fermentation parameters, the pH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), microbial protein (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), and valeric acid concentration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) were significantly increased, while the ammonia nitrogen (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) was significantly decreased. For serum indicators, the contents of total protein (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) and immunoglobulin A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) were significantly increased; the total antioxidant capacity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) was extremely significantly increased; and the contents of total cholesterol (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), glucose (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), and malondialdehyde (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030) were significantly decreased. Multi-omics analysis revealed that AMRF increased the relative abundances of beneficial microorganisms, including the rumen genus \u003cem\u003eAlloprevotella\u003c/em\u003e, cecal phylum \u003cem\u003eBacteroidota\u003c/em\u003e, and colonic genus \u003cem\u003eAlistipes\u003c/em\u003e, while reducing harmful microorganisms such as \u003cem\u003eEscherichia\u003c/em\u003e\u0026ndash;\u003cem\u003eShigella\u003c/em\u003e. Additionally, AMRF upregulated the plasma key differential metabolites 12-hydroxyeicosatetraenoic acid and \u003cem\u003eα\u003c/em\u003e-D-glucose, downregulated thromboxane B₂, activated the arginine biosynthesis and glutathione metabolism pathways, and regulated the expression of key differential genes in the liver, such as \u003cem\u003ePTGS1\u003c/em\u003e, \u003cem\u003eCSF1R\u003c/em\u003e, and \u003cem\u003eND6\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAMRF optimizes rumen nitrogen metabolism by modulating the gastrointestinal microbiota of SDGs, thereby improving plasma metabolic profiles and influencing the expression of liver genes through key plasma metabolites and metabolic pathways. These processes act synergistically to enhance antioxidant capacity, immune function, and growth performance, providing a theoretical basis for promoting healthy ruminant production.\u003c/p\u003e","manuscriptTitle":"Multi-omics insights into the effects of Allium mongolicum Regel flavonoids on growth, antioxidant capacity, and immune regulation in Saanen dairy male goats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 05:40:33","doi":"10.21203/rs.3.rs-7997729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-23T10:43:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-21T13:05:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T12:11:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288695573625551172008366373371964073709","date":"2025-12-09T19:23:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T13:57:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260458048217196503727691525292021237059","date":"2025-12-08T15:56:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259040386069422452532641561741920487304","date":"2025-12-04T01:29:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-28T10:20:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15245200074580074681512003290989115328","date":"2025-11-08T02:46:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T16:44:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T04:48:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-02T04:47:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical and Biological Technologies in Agriculture","date":"2025-10-31T10:52:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1aa91cba-bd17-4da4-86b2-cb4a25f91efe","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-11T18:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 05:40:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7997729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7997729","identity":"rs-7997729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}