Astragali Radix was purchased from Lanzhou Yellow River medicine market. Origin: Liupanshan Region, China. The following kits were used in this study: malondialdehyde (MDA) test kit (catalog No. YJ016824), superoxide dismutase (SOD) test kit (catalog No. YJ036559), myeloperoxidase (MPO) test kit (catalog No. YJ300741), lactate dehydrogenase (LDH) test kit (catalog No. YJ520026), immunoglobulin A (IgA) ELISA kit (catalog No. YJ542063), immunoglobulin G (IgG) ELISA kit (catalog No. YJ330698), immunoglobulin M (IgM) ELISA kit (catalog No. YJ627279), Interleukin-2 (IL-2) ELISA kit (catalog No. YJ002498), interleukin-1 β (IL-1 β) ELISA kit (catalog No. YJ064295), interleukin-6 (IL-6) ELISA kit (catalog No. YJ064296) and tumor necrosis factor α (TNF- α) ELISA kit (catalog No. YJ077389), all the above were purchased from Shanghai Meilian Biotechnology Company.

Astragali Radix were mixed with distilled water at a 1:10 (w/v) ratio. The mixture was vigorously boiled, then simmered at low heat for 30 min and filtered through four-layer sterile gauze. The residue underwent re-extraction with an 8-fold volume of distilled water following identical boiling/simmering conditions, followed by gauze filtration. Both filtrates were combined for subsequent experiments.

All experimental cows were obtained from the Gansu Holstein Dairy Cattle Breeding Center and selected as multiparous, mid-lactation individuals (3–9 years old) with comparable body weights. The animals were fed mixed ration (TMR) three times a day at 8:00, 16:00, and 21.30 respectively (Table 1). Mammary health was assessed via SCC and clinical mastitis evaluation. Based on established criteria (13, 14), cows with SCC < 200,000 cells/ml were considered healthy, while those with SCC > 200,000 cells/ml without clinical symptoms were diagnosed with SCBM. Untreated positive cows with SCBM for 3–5 days (n = 24) were randomly assigned to four experimental groups (n = 6 per group): Astragali Radix water decoction High-dose group (0.4 g·kg⁻¹·d⁻¹, AR_H), Astragali Radix water decoction Medium-dose group (0.2 g·kg⁻¹·d⁻¹, AR_M), Astragali Radix water decoction Low-dose group (0.1 g·kg⁻¹·d⁻¹, AR_L). Model group: untreated SCBM controls (MOD). Six additional healthy cows received equivalent volumes of water as negative controls (NC). All ARWD treatments were administered orally via force-feeding for seven consecutive days. Sample Collection: 5 ml of blood were collected from each cow through the tail vein 1 h after feeding on the morning of day 8. The blood samples were then centrifuged at 3,000 r/min for 15 min at 4°C to separate the serum. Rumen fluid was extracted by inserting a rumen sampler via the mouth into the rumen and using a syringe. The first two tubes of rumen fluid were discarded to prevent salivary contamination. Approximately 150 ml of rumen fluid was sampled from each cow. Fecal samples were collected from the rectum using sterile long-arm gloves, 3 h after feeding, and placed in sterile, sealed plastic bags. Fecal and rumen fluid samples were immediately snap frozen in liquid nitrogen and stored at −80°C. Milk samples were collected and transported on ice for somatic cell count (SCC) analysis and milk composition testing.

The LC-MS/MS analysis was performed using a UHPLC-Q Exactive system (Thermo Scientific) equipped with a UPLC BEH C18 column (2.1 × 100 mm i.d., 1.7 μm). The mobile phase consisted of (A) 2% acetonitrile containing 0.1% formic acid and (B) acetonitrile with 0.1% formic acid. Full-scan MS data were acquired in both positive and negative ionization modes over a mass range of 70–1,050 m/z at a resolution of 70,000. Data processing, including peak alignment, extraction, and quantification, was conducted using Compound Discoverer QI v3.0 (WatersCorporation, Milford, USA) software. Metabolite identification was achieved by matching accurate mass and MS/MS spectra against the Majorbio Bio-Pharm Technology Co., Ltd (Shanghai, China) database with (mass accuracy threshold of <10 ppm).

Milking was performed using rotary milking parlors pre- and post-treatment, with individual milk yields recorded for each cow.

SCC and milk composition parameters [fat, protein, lactose, total milk solids (TS), milk urea nitrogen (MUN)] were analyzed using a CombiFoss™ 7 analyzer (Foss Analytical, Denmark).

The biochemical test kit was used to measure the levels of LDH, MPO, MDA, and SOD in serum. All experimental procedures were strictly conducted according to the manufacturer's instructions for the reagents.

The levels of IL-1β, IL-6, IL-2, and TNF-α in serum were measured using ELISA test kits. All experimental procedures were strictly carried out according to the instructions provided by the manufacturer for the reagents.

The levels of IgA, IgM, and IgG in serum were measured using biochemical test kits. All experimental procedures were strictly followed according to the instructions provided by the manufacturer for the reagents.

After drug administration, rumen fluid and rectal content were collected and stored at −80°C. According to the manufacturer's instructions, total microbial genomic DNA was extracted from 18 gastric juice samples and 18 fecal samples using the E.Z.N.A.^® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). The mass and concentration of DNA were determined by 1.0% agarose gel electrophoresis and NanoDrop2000 spectrophotometer (Thermo Scientific, United States), and were stored at −80°C for further use. The hypervariable region V3–V4 of the bacterial 16S rRNA gene were amplified with primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG3′) and 806R (5′-GGACTACHVGGGTWTCTAAT3′) by T100 Thermal Cycler PCR thermocycler (BIO-RAD, USA). The PCR reaction mixture included 4 μl of 5 × Fast Pfu buffer, 2 μl of 2.5 mm dNTPs, 0.8 μl (5 μm) for each primer, 0.4 μl of Fast Pfu polymerase, 10 ng of template DNA, and ddH2O up to a final volume of 20 μl. The PCR amplification cycle conditions are as follows: initial denaturation at 95°C for 3 min, denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, single extension at 72°C for 10 min, and conclusion of 27 cycles at 4°C. The PCR product was extracted from 2% agarose gel and purified using the PCR Clean-Up Kit (Yuhua, Shanghai, China) according to manufacturer's instructions and quantified using Qubit 4.0 (Thermo Fisher Scientific, USA), and the purified amplifiers were aggregated in equal molar amounts. 2 × 300 bp paired-end sequencing was performed on the Illumina Nextseq2000 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

Serum samples from blank control, model, and astragalus intervention groups (n = 6/group) were extracted with methanol:acetonitrile (1:1, v/v). After ultrasonication (5°C, 40 kHz, 30 min) and incubation (−20°C, 30 min), supernatants were collected by centrifugation (13,000 g, 4°C, 15 min), dried under nitrogen, and reconstituted in acetonitrile:water (1:1, v/v). Quality control (QC) samples were processed identically.

Metabolites were analyzed using UHPLC-Q Exactive Focus MS (Thermo Fisher) with an ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 μm). Mobile phases: (A) 95% water/5% acetonitrile (0.1% formic acid); (B) 47.5% acetonitrile/47.5% isopropanol/5% water (0.1% formic acid). Flow rate: 0.40 ml/min, injection volume: 5 μl, column temperature: 40°C. MS parameters: ±3.50 kV spray voltage, 325°C capillary temperature, full scan at 81–1,000 m/z (70,000 resolution), HCD fragmentation (30 eV).

Raw data were processed in Progenesis QI v3.0 for peak alignment. Metabolites were identified by matching MS/MS spectra against HMDB, Metlin, and Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) in-house databases (MS error < 10 ppm, spectral score filtering). Differential metabolites underwent pathway enrichment analysis (P < 0.05).

Spearman's rank correlation analysis was performed to investigate relationships between differential metabolites and gut microbiota among NC, MOD, and AR_H groups. Additionally, pairwise correlations were analyzed between metabolites/gastrointestinal microbiota and dairy parameters (SCC, milk yield, milk composition), inflammatory factors, immune/antioxidant indices.

Gastrointestinal flora alpha diversity was statistically determined using the Kruskal–Wallis test, and beta-diversity was statistically determined using the ANOSIM test. Statistical analyses were performed using one-way ANOVA in GraphPad Prism 8 (GraphPad Software), with significance levels defined as P < 0.05 (significant), P < 0.01 (highly significant), and P > 0.05 (not significant).

Total ion chromatograms were acquired in both positive and negative ion modes. As shown in Figure 1, well-resolved peaks with uniform distribution were observed under the current analytical conditions. Qualitative analysis was performed by matching the mass spectrometry data matrix (retention time, m/z, and peak intensity) against the MJBIOTCM database. Nine compounds were identified, including flavonoids, steroids, and their derivatives (Table 2).

Analysis of milk yield before and after oral administrations revealed significant intergroup differences. Pretreatment milk yield in the MOD group was significantly lower than the NC group (P < 0.01). Post-intervention, all ARWD treated groups (AR_L, AR_M, AR_H) exhibited increased milk yields compared to baseline levels. Notably, the AR_H group showed marked improvement vs. the MOD group (P < 0.01), approaching NC group values. AR_L and AR_M groups demonstrated moderate milk yield increases post-treatment, though these changes lacked statistical significance (P > 0.05) (Figure 2).

Post-treatment SCC analysis demonstrated significant reduction in the AR_H group vs. baseline (P < 0.01) and MOD group (P < 0.01). The AR_M group showed moderate SCC decrease compared to MOD (P < 0.05), while AR_L exhibited no significant change (Figure 3A).

Milk fat content analysis showed that after ARWD intervention, the fat content in the AR_H group was significantly higher than that in the MOD group (P < 0.01). Compared with before treatment, the fat content in the AR_H, AR_M, and AR_L groups increased after ARWD intervention, but the increase was not significant (Figure 3B).

Milk protein content analysis indicated that the protein levels in the AR_H group after treatment were significantly lower than before treatment (P < 0.05). Compared with the MOD group, the protein contents in the AR_H, AR_M, and AR_L groups were all lower than those in the MOD group and lower than before administration (Figure 3C).

Lactose, Total Solids, and Milk Urea Nitrogen analysis revealed that no statistically significant differences in lactose, total solids (TS), or milk urea nitrogen (MUN) were detected among experimental groups relative to NC (P > 0.05). Slight increases in lactose and TS were observed in AR_H and AR_M groups, though these trends did not reach statistical significance (Figures 3D–F).

As shown in Figure 4, IL-6 expression in the AR_H group was significantly reduced post-intervention compared to pre-treatment (P < 0.01). Similarly, IL-1β levels showed marked reduction in AR_H vs. both pre-treatment (P < 0.05) and MOD group (P < 0.05). TNF-α expression in AR_H group was significantly lower than MOD group (P < 0.05). In contrast, AR_M and AR_L groups exhibited no significant alterations in IL-6, IL-1β, or TNF-α levels compared to NC or MOD groups (P > 0.05). IL-2 expression remained unchanged across all experimental phases (Figure 4).

Post-intervention analysis revealed significant increases in SOD activity (P < 0.01) and marked reductions in MDA (P < 0.01), LDH (P < 0.05), and MPO (P < 0.05) levels in the AR_H group. Notably, AR_H group MPO levels were significantly lower than MOD group (P < 0.01). No significant differences were observed between AR_M and AR_L groups (Figure 5). These findings demonstrate that Astragalus supplementation, particularly at high doses, significantly enhanced antioxidant capacity and alleviated oxidative stress, while lower doses exhibited moderate effects.

ARWD exerted significant modulatory effects on the immunoglobulin profiles in experimental groups. In the AR_H group, post-treatment levels of IgA, IgG, and IgM were significantly elevated compared to pre-treatment (P < 0.05). Furthermore, AR_H exhibited marked increases in IgM and IgG vs. the MOD group (P < 0.05). In contrast, AR_M and AR_L groups demonstrated no significant alterations in immunoglobulin levels relative to MOD (P > 0.05) (Figure 6). These findings indicate that high-dose Astragalus supplementation enhanced immune function through immunoglobulin modulation, while medium- and low-dose groups showed marginal efficacy.

Orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to investigate metabolic disparities. As shown in Figure 9A, score plots in both positive and negative ion modes revealed distinct clustering patterns among groups (NC, MOD, and AR_H), with tight intra-group sample aggregation, indicating significant intergroup differences (P < 0.05) and robust data reproducibility. Differential metabolites were screened using criteria of variable importance in projection (VIP) > 1.0 and P < 0.05, identifying 270 metabolites between MOD and NC groups and 198 metabolites between AR_H and MOD groups (Figures 9B, C). The OPLS-DA/PLS-DA models, validated by seven-fold cross-validation, highlighted metabolites critical for group classification via VIP analysis.

MOD vs. NC comparisons showed 24 significantly downregulated and 6 upregulated metabolites. In AR_H vs. MOD, 15 metabolites were downregulated and 15 upregulated. Notably, MOD exhibited marked reductions in 3, 7-dihydroxy-12-oxocholanoic acid, pelanin, and 6-formylpterin compared to NC (P < 0.05), while AR_H restored these metabolites to near-normal levels (P < 0.05). Pathway analysis identified five dysregulated pathways in MOD vs. NC: linoleic acid metabolism (P = 0.003), choline metabolism in cancer (P = 0.007), retrograde endocannabinoid signaling (P = 0.012), cAMP signaling pathway (P = 0.018), and phospholipase D signaling (P = 0.023). AR_H significantly restored these pathways (Figure 9D), suggesting its therapeutic role in modulating lipid-associated inflammation and cellular signaling cascades in SCBM.

In this study, seven differential metabolites significantly associated with rumen microbiota (R > 0.5, P < 0.05) were identified (Figure 10A). Among them, betaine exhibited a positive correlation with norank_f__norank_o__WCHB1-41, cyclohexane with Christensenellaceae_R-7_group, and 3-guanidinopropanoate with Rikenellaceae_RC9_gut_group, suggesting that these metabolites may promote or be linked to the abundance or activity of these bacterial taxa. Conversely, PC (17:0/0:0) and indoxyl showed negative correlations with Christensenellaceae_R-7_group, lauryldiet with norank_f__F082, and betaine with norank_f__Muribaculaceae, indicating potential inhibitory effects on the growth or function of these microbial populations. These findings highlight the complex interactions between distinct metabolites and rumen microbiota, which may play a pivotal role in modulating host rumen microbial communities and providing a foundation for further exploration of underlying mechanisms.

Four differential metabolites displayed strong correlations with fecal microbiota (R > 0.5, P < 0.05) (Figure 10B). Specifically, PC (17:0/0:0) was positively correlated with norank_f__norank_o__RF39, whereas p-tolyl sulfate showed a negative correlation with NK4A214_group, taurohyocholate with Bifidobacterium, and lysoPC (16:1(9Z)/0:0) with unclassified_c__Clostridia. These results suggest that these metabolites may influence the composition and activity of fecal microbiota, underscoring the potential role in regulating the fecal microbial dynamics.

Spearman correlation analysis was performed to evaluate the associations between the relative abundance of gastrointestinal microbiota at the genus level and 18 metrics, including SCC, milk yield, milk composition, and serum parameters.

As illustrated in Figure 11A, 29 OTUs displayed significant correlations with at least one metric. Notably, Acetitomaculum, Ruminococcus_gauvreauii_group, Pseudobutyrivibrio, norank_f__norank_o__RF39, and Monoglobus in the rumen showed significant positive correlations with SCC, whereas UCG-014 exhibited a negative correlation with SCC (P < 0.05), suggesting its potential protective role. Conversely, Pseudobutyrivibrio and Ruminococcus_gauvreauii_group were negatively correlated with milk yield, indicating detrimental associations with lactation performance.

Key correlations with inflammatory markers were also observed: Lachnospiraceae_NK4A136_group, Balautia, unclassified_f_Lachnospiraceae, and Dorea displayed positive correlations with TNF-α, while Balautia, NK4A214_group, Bacteroides, and unclassified_f_Oscillospirales were positively correlated with IL-6 (P < 0.05), highlighting their involvement in inflammatory responses. Intriguingly, Acetitomaculum and Ruminococcus_gauvreauii_group showed positive correlations with IL-1β, LDH, and MPO—markers of inflammation and cellular damage—but negative correlations with SOD, lactose, IgG, and milk fat (P < 0.05), linking these genera to oxidative stress and milk quality deterioration. These findings reveal significant associations between specific rumen microbial taxa and critical clinical/biochemical parameters, identifying potential microbial targets for improving host health and productivity.

In the fecal microbiota (Figure 11B), Eubacterium_ruminantium_group was positively correlated with SCC, whereas Rikenellaceae_RC9_gut_group, norank_f__norank_o__WCHB1-41, norank_f__F082, and Veillonellaceae_UCG-001 exhibited negative correlations with SCC (P < 0.05), implying potential anti-inflammatory properties. Notably, norank_f__UGG-010, norank_f__norank_o__WCHB1-41, and Rikenellaceae_RC9_gut_group demonstrated positive correlations with milk yield, highlighting their potential role in yield enhancement. Norank_f__norank_o__RF39 and Eubacterium_ruminantium_group were positively correlated with LDH, linking them to tissue damage and inflammatory states. Additionally, genera such as Eubacterium_nodatum_group, Eubacterium_hallii_group, norank_f__Eubacterium_coprostanoligenes_group, norank_f__UCG-011, Christensenellaceae_R-7_group, and NK4A214_group showed positive correlations with TNF-α (P < 0.05), emphasizing their participation in systemic inflammation.

The therapeutic efficacy of ARWD in SCBM might largely stem from its ability to modulate gastrointestinal microbiota, particularly by influencing microbial taxa associated with inflammation, milk production, and systemic health. These results underscore the pivotal role of gastrointestinal microbiota in both the pathophysiology and treatment of SCBM.

Correlation analysis between serum metabolites (AR_H vs. MOD) and clinical indicators, milk composition, and serum parameters (Figure 12) revealed significant changes in metabolite levels associated with SCC, serum parameters, and milk composition following ARWD intervention. Notably, serum D-fructose exhibited positive correlations with SCC, LDH, and MDA, but negative correlations with milk yield, fat, and lactose (P < 0.05), suggesting its association with inflammation and impaired milk quality. Furthermore, metabolites such as 6-oxopiperidine-2-carboxylic acid, 2, 3-dihydroxybenzoic acid, L-carnitine, decanedioic acid, and (3S, 5R, 6R, 7E)-3, 5, 6-trihydroxy-7-megastigmen-9-one showed significant positive correlations with SCC, MDA, and MPO, but negative correlations with milk yield, fat, lactose, and IgG (P < 0.05), implicating their roles in inflammatory responses, oxidative stress, and immune dysfunction. Conversely, PE (20:1/0:0) demonstrated a negative correlation with TNF-α and IL-6 (P < 0.05), highlighting the potential anti-inflammatory properties.

These results indicate that AR_H effectively modulates serum metabolites, reduces SCC, alleviates inflammation and oxidative stress, and improves milk yield and immune function. Taken together, these results highlight Astragali Radix as a promising traditional herbal formulation for the treatment of SCBM in dairy cows.