Rhizomes of RH, a natural medicinal herb, were purchased from Liupanshui, Guiyang, China. A voucher specimen (voucher number: dyt-20240410) was identified by Professor Chenggang Hu from Guizhou University of TCM (Figure 1).

Aqueous extract of RH was prepared via decoction: 6000 mL of ultrapure water was heated to boiling, and 600 g of RH rhizomes (solid-to-liquid ratio 1:10, w/v) was added for decoction. After boiling for 3 min, the mixture was filtered through 8 layers of 200-mesh nylon fabric. The residue was re-decocted with 600 mL of boiling water three times. Filtrates from all decoctions were combined, and the residue was discarded. The combined filtrate was concentrated under reduced pressure at 50 °C to a relative density of 2.000, followed by freeze-drying for 3 consecutive days. The resulting extract was stored long-term at −80 °C for animal experiments; prior to administration, it was diluted to the required concentration and stored short-term at 4 °C.

Loperamide hydrochloride was purchased from Sigma-Aldrich (St. Louis, MO, USA; batch number: L4762-5G). Ultra-high performance liquid chromatography-tandem Fourier transform mass spectrometry (UHPLC-Orbitrap Exploris 240) was acquired from Thermo Fisher Scientific (Waltham, MA, USA). An HSS T3 chromatographic column (100 mm × 2.1 mm i.d., 1.8 μm) was purchased from Waters Corporation (Milford, MA, USA).

Instrumentation included: a JXDC-20 nitrogen evaporator (Shanghai Jingxin Industrial Development Co., Ltd., Shanghai, China); an LNG-T88 desktop rapid centrifugal concentrator (Taicang Huamei Biochemical Instrument Factory, Taicang, China); a Wonbio-96c high-throughput tissue homogenizer (Shanghai Wanbai Biotechnology Co., Ltd., Shanghai, China); an SBL-10DT ultrasonic cleaner (300 W, 10 L; Ningbo Xinzhi Biotechnology Co., Ltd., Ningbo, China); a Centrifuge 5430R high-speed refrigerated centrifuge (Eppendorf AG, Hamburg, Germany); and a NewClassic MF MS105DU electronic balance (Mettler Toledo, Greifensee, Switzerland), used as per the manufacturer’s specifications.

UPLC-grade methanol, propanol, water, acetonitrile, and formic acid were purchased from Fisher Scientific (Waltham, MA, USA). ELISA kits for rat Substance P (SP), Motilin (MTL), and Gastrin (GAS) (all batch number: 202505) were purchased from Changzhou Chenjun Chemical Co., Ltd. (Changzhou, China).

Thirty-six specific pathogen-free (SPF) male Sprague–Dawley (SD) rats, weighing 180–220 g, were purchased from Spence (Beijing) Biotechnology Co., Ltd. (license number: SCXK (Jing) 2024-0001). This experiment was approved by the Experimental Animal Management and Ethics Committee of Guizhou University of TCM (approval number: 20251111002). Rats were housed individually in a controlled environment with a temperature of 22 ± 1 °C, relative humidity of 63 ± 5%, and a 12 h light/dark cycle, with ad libitum access to food and water. After 2 weeks of acclimatization, STC was induced in all groups except the control group by intragastric administration of loperamide hydrochloride (5 mg/kg) twice daily for 1 week, followed by once daily for an additional 4 weeks. The rats were randomly divided into 6 groups (n = 6 per group): Normal group (Control): No loperamide hydrochloride; Model group (Model): Loperamide hydrochloride (5 mg/kg); Positive control group (Positive): Mosapride citrate capsules (2 mg/kg) + loperamide hydrochloride (5 mg/kg); Low-dose RH group (Low): RH (aqueous extract of RH) (1,350 mg/kg) + loperamide hydrochloride (5 mg/kg); Medium-dose RH group (Middle): RH (2,700 mg/kg) + loperamide hydrochloride (5 mg/kg); High-dose RH group (High): RH (5,400 mg/kg) + loperamide hydrochloride (5 mg/kg).

To quantitatively evaluate the improvement of intestinal motility by RH in STC rats, an activated carbon suspension was prepared as an intestinal propulsion tracer. Briefly, 2 g of sodium carboxymethylcellulose was dissolved in 100 mL of sterile water and heated to boiling until the solution became transparent. Subsequently, 5 g of activated carbon was added to the mixture, and the suspension was reboiled three times to ensure uniform dispersion. After cooling to room temperature, the volume was adjusted to a final volume of 100 mL with sterile water, yielding a 50 g/L activated carbon suspension. The prepared suspension was stored at 4 °C until use.

To systematically characterize the chemical composition of the RH aqueous extract and identify potential bioactive components (to support subsequent investigations into its material basis and mechanism of regulating intestinal microecology in STC), ultra-high performance liquid chromatography-Fourier transform mass spectrometry (UHPLC-QExactive) was employed for qualitative and quantitative analysis.

Briefly, 100 mg of the extract prepared in Section 2.1 was placed in a 2 mL centrifuge tube containing one 6 mm-diameter grinding bead. Subsequently, 300 μL of extraction solution (methanol:water = 4:1, v/v) supplemented with four internal standards (including L-2-chlorophenylalanine at 0.02 mg/mL) was added to extract metabolites. Analyses were performed on a UHPLC-QExactive system (Thermo Fisher Scientific, Waltham, MA, USA) equipped with an HSS T3 chromatographic column (100 mm × 2.1 mm i.d., 1.8 μm; Waters Corporation, Milford, MA, USA). The column temperature was maintained at 40.0 °C, with a flow rate of 0.400 mL min⁻¹ using a gradient elution mobile phase consisting of solvent A (water-acetonitrile = 95:5, v/v, containing 0.1% formic acid) and solvent B (acetonitrile-isopropanol-water = 47.5:47.5:5, v/v/v, containing 0.1% formic acid). To ensure system stability and analytical reproducibility, quality control (QC) samples were prepared by pooling equal volumes of all test samples, with one QC sample analyzed every 5–10 test samples to verify the reliability of the analytical workflow.

Mass spectrometry conditions: Samples were ionized via electrospray ionization (ESI), and mass spectral signals were acquired in both positive and negative ion modes. Specific parameters were set as follows: scan range = 70–1,050 m/z; sheath gas flow rate = 50 arb; auxiliary gas flow rate = 13 arb; auxiliary gas heating temperature = 450 °C; capillary temperature = 320 °C; spray voltage = 3,500 V (positive mode) and −3,000 V (negative mode); S-Lens voltage = 40; collision energies = 20, 40, and 60%; full scan (Full MS) resolution = 70,000; and MS² resolution = 17,500. Raw data obtained from both ionization modes were combined for subsequent data processing and multivariate statistical analysis.

Following the completion of modeling and the final administration, rats were fasted for 24 h with ad libitum access to water. Each rat was administered an intragastric dose of 5% activated carbon suspension at 1 mL/100 g body weight. Thirty minutes post-administration, rats were anesthetized via intraperitoneal injection of 5% urethane at 0.6 mL/100 g body weight. Immediately following anesthesia, the abdominal cavity was opened to collect blood from the abdominal aorta, and the entire intestinal tract was rapidly excised. Under tension-free conditions, the total length of the intestinal tract and the propulsive length of the activated carbon were measured. The intestinal propulsion rate was calculated using the following formula:

Following the final administration, colonic tissues were collected from 3 randomly selected rats per group. The tissues were fixed in 4% paraformaldehyde and subsequently subjected to precision dissection, dehydration, embedding, sectioning, hematoxylin–eosin (HE) staining, and mounting in accordance with standard pathological protocols. The sections were observed under a light microscope at different magnifications, and detailed records were made of basic pathological changes and inter-sectional variations.

Following the completion of all administrations, 5 mL of abdominal blood was collected from each rat. Blood samples were incubated at 37 °C for 30 min to allow coagulation, then centrifuged at 3500 rpm for 10 min. The supernatant (serum) was aspirated and stored at −80 °C for later use.

One aliquot of the serum was used to quantify serum levels of substance P (SP), gastrin (GAS), and motilin (MTL) in 3 rats per group using commercial ELISA kits following the manufacturer’s protocols. The remaining supernatant was reserved for untargeted metabolomics analysis.

Fecal samples were transported to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) on dry ice for metagenomic analysis. Total genomic DNA of the microbial community was extracted using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s protocols. DNA concentration and purity were quantified, and DNA integrity was evaluated via 1% agarose gel electrophoresis.

Genomic DNA was fragmented using a Covaris M220 system (Gene Company, China), and approximately 350 bp fragments were selected for paired-end (PE) library construction with the NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, USA). Metagenomic sequencing was performed on the Illumina NovaSeq™ X Plus platform (Illumina, San Diego, CA, USA) by Shanghai Majorbio Bio-pharm Technology Co., Ltd.

Bioinformatic analyses, including microbial species annotation, community composition analysis, diversity index calculation, and differential species identification, were performed based on operational taxonomic units (OTUs) to acquire data regarding microbial composition, abundance, and differential alterations. All analytical processes were conducted on the Majorbio Bio-Cloud Platform (https://www.majorbio.com/web/market/activity; Shanghai Majorbio Bio-pharm Technology Co., Ltd.).

Three rats per group were randomly selected for blood sampling. Blood was collected in heparinized anticoagulant tubes, centrifuged at 3500 rpm for 10 min, and the supernatant (plasma) was collected, stored at −80 °C, and subsequently transported to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) on dry ice for metabolomic analysis.

Spearman correlation analysis was conducted to assess the associations between gut microbiota and differential metabolites. The top 20 microbial species and 15 differential metabolites across all groups were chosen for targeted analysis.

Defecation-related parameters were presented as mean ± standard deviation (SD). Normally distributed data were analyzed using one-way analysis of variance (ANOVA) followed by the Bonferroni post-hoc test for multiple group comparisons. The Kruskal–Wallis H test was employed to compare microbial abundance at the order and species levels, and functional modules. Statistical significance was set at p < 0.05.

Untargeted metabolic profiling of the RH aqueous extract was conducted via ultra-high performance liquid chromatography-quadrupole-electrostatic field orbitrap tandem mass spectrometry (UPLC-Q-Exactive Orbitrap-MS/MS). The total ion chromatograms (TICs) in positive ion mode (Figure 2A) and negative ion mode (Figure 2B) both displayed robust technical profiles with: in negative ion mode, representative metabolites including D-ribonolactone and succinic acid were successfully detected, whereas positive ion mode yielded higher signal response intensities and identified bioactive substances such as choline and zeatin. Dual-ion mode detection effectively complemented response data for metabolites with varying polarities, furnishing a comprehensive and robust chromatographic-mass spectrometric foundation for the subsequent qualitative identification of 614 distinct compounds.

Based on the component content distribution pie chart (Figure 2C), lipids (39.79%) and terpenoids (20.59%) represented the core dominant constituents of the RH aqueous extract, collectively contributing 51.38% to the total extract content. In contrast, the component quantity distribution pie chart (Figure 2D) demonstrated that the ‘Others’ category exhibited the highest proportion of total compounds (34.65%), with terpenoids ranking second (20.65%); lipids, however, constituted merely 12.99% of the total compound count.

Subsequent cross-analysis of these two distribution profiles indicated that terpenoids were the sole category ranking among the top tiers for both, thus exhibiting both high content abundance and substantial species diversity. Lipids displayed a marked characteristic of —specifically, this category comprised fewer compound species, yet each individual constituent possessed high abundance. The ‘Others’ category, conversely, was characterized by, which indicated the presence of numerous unclassified trace secondary metabolites within this group.

In summary, the RH aqueous extract exhibits a distinct component distribution pattern characterized by. This finding provides a definitive material foundation for subsequent screening of bioactive components, elucidation of the pharmacodynamic material basis, and investigation into the underlying mechanism of action of RH in relevant biological processes.

Intestinal propulsion rates were measured in all groups (Figure 3). A significant reduction in intestinal propulsion rate was observed in the model group compared to the control group (***p < 0.001), confirming the successful establishment of the STC model. In contrast, the RH low-, medium-, and high-dose groups, as well as the positive control group, exhibited significantly higher intestinal propulsion rates compared to the model group (***p < 0.001), demonstrating that RH is effective in enhancing intestinal motility.

In the control group, the colonic mucosal layer of rats was moderately thick and structurally intact, with no atrophy or edema. Epithelial cells were closely and regularly arranged, crypt depth was normal, and the proliferation and differentiation of crypt epithelial cells maintained a dynamic balance. In contrast, the model group exhibited a significant thinning of the colonic mucosal layer (p < 0.01) and a reduction in crypt depth (p < 0.001), indicating severe pathological damage to the colonic mucosa and confirming successful STC model establishment.

Following drug intervention, the RH medium-dose, high-dose, and positive control groups showed significant restoration of mucosal layer thickness (increased) and crypt depth (enhanced), with values approaching those of the control group (Figure 4). Notably, no significant differences in these pathological parameters were detected between the RH low-dose group and the model group, indicating that RH exerts a dose-dependent reparative effect on colonic mucosal injury.

Following 4 weeks of drug intervention, the positive control group, as well as the RH low-, medium-, and high-dose groups, exhibited a significant increase in serum motilin (MTL) and gastrin (GAS) levels (p < 0.05) and a significant decrease in serum substance P (SP) levels (p < 0.001) compared to the model group (Figure 5), thereby further validating the laxative effect of RH.

Metagenomic analysis, based on high-throughput sequencing of microbial gene sequences, enables precise characterization of gut microbiota composition at the species level. In this study, species annotation of gut microbiota from each group was performed using sequencing-derived gene sequences and relative abundance data. Beta diversity analysis (principal component analysis, PCA; principal coordinate analysis, PCoA) was employed to visualize variations in microbial community structure. Results indicated that microbial clustering in the model group was significantly distinct from that in the control group, demonstrating severe gut microbiota dysbiosis induced by STC modeling. Following RH intervention, microbial clustering in each treatment group gradually approximated that of the control group, confirming that RH effectively reverses gut microbiota imbalance in STC rats (Figures 6A,B).

Further analysis of microbial composition at the order level identified core dominant taxa, including Eubacteriales, Lachnospirales (spirochete-related groups), Bacteroidales, and Lactobacillales. Compared to the control group, the model group exhibited significantly decreased relative abundances of Eubacteriales and Lachnospirales, alongside increased abundances of Bacteroidales and Lactobacillales. RH intervention increased the relative abundances of Lachnospirales and Bacteroidales, decreased the abundance of Lactobacillales, with the most critical restoration observed for Eubacteriales and Lachnospirales (Figure 6C). Functionally, Eubacteriales and Lachnospirales are core producers of intestinal short-chain fatty acids (SCFAs, i.e., butyrate and acetate). Their proportional imbalance in the model group directly led to insufficient SCFA production: butyrate deficiency impaired intestinal epithelial energy supply and barrier function, increased the release of inflammatory factors (e.g., TNF-α), and caused enteric nervous system (ENS) damage; acetate deficiency inhibited GPR43 receptor activation, reduced enteric nerve 5-HT release, and weakened intestinal peristalsis. This SCFA synthesis defect further induced ENS synaptic transmission abnormalities and smooth muscle “contraction-relaxation” imbalance, which, combined with enteric nerve inflammatory damage, formed a vicious cycle of “motility insufficiency–inflammatory injury.” RH intervention (low-, medium-, high-dose, and positive control groups) significantly increased the proportions of Eubacteriales and Lachnospirales, with the medium-dose group exerting the optimal effect: abundances of both taxa were synchronously elevated to near-normal levels. Restoration of SCFA-producing microbiota reestablished acetic acid and butyric acid synthesis homeostasis: acetic acid promoted 5-HT release via GPR43 receptor activation to enhance peristalsis, while butyric acid repaired the intestinal epithelial barrier and inhibited inflammation to reduce ENS damage, ultimately improving ENS synaptic transmission, rebalancing smooth muscle contraction, and restoring intestinal motility.

At the species level, alterations in the relative abundances of core genera further validated these regulatory trends (Figure 6D). Compared to the control and treatment groups, the model group showed significantly reduced relative abundances of Ruminococcus sp., Eubacterium sp., Blautia sp., and Oscillibacter sp. After RH intervention, the abundance of Ruminococcus sp. was significantly increased: this genus exhibits active carbohydrate fermentation, degrading complex starch into small molecules and producing SCFAs (e.g., acetate, propionate) to support intestinal peristalsis. The abundance of Eubacterium sp. was also elevated: as a core butyrate-producing genus, its metabolite butyrate serves as the primary energy source for intestinal epithelial cells, repairs the intestinal barrier, inhibits inflammation, and promotes 5-HT release via GPR43 receptor activation, synergistically enhancing intestinal motility. Meanwhile, the abundance of Blautia sp. was decreased in intervention groups: overproliferation of this genus consumes SCFA precursors (e.g., dietary fiber) or produces motility-inhibiting substances (e.g., branched-chain amino acid derivatives), and its downregulation relieved intestinal motility inhibition, further alleviating constipation.

Linear discriminant analysis (LDA) combined with effect size analysis (LEfSe) was used to screen for microbial biomarkers with biological significance that differed among groups. Core biomarkers were identified via LDA value distribution bar plots and species cladograms (Figures 6E,F). Results showed distinct differentiation of core functional taxa: the control group was dominated by organic matter-degrading Bathyarchaeia, with broad microbiota distribution and high diversity; the model group was characterized by unclassified Nanoarchaeia; the high-dose group enriched diverse Nanoarchaeia; the medium-dose group centered on hydrogenotrophic methanogenic Methanomicrobia; the low-dose group favored acetic acid-type methanogenic Methanofastidiosales; and the positive control group integrated methanogenic and nitrogen-cycling taxa. Except for the control group, all other groups exhibited specialized microbiota structures due to functional taxon enrichment, with diversity concentrated in specific metabolic pathways, further confirming the targeted regulatory effect of RH on microbiota function. The species association network (Figure 6G) further visualized the co-occurrence patterns of core taxa: the control group showed dense, multi-directional species interactions, while the model group exhibited sparse, unidirectional associations; RH intervention (especially medium-dose) restored the complexity of species co-occurrence networks, consistent with the recovery of microbiota diversity.

Multivariate statistical analysis, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and permutation test, was performed on serum metabolomics data to verify the intervention effect of RH on metabolic disorders in STC rats. Results showed that RH exerted a significant regulatory effect on metabolic perturbations, with the medium-dose group exhibiting the most favorable pharmacological activity (Figure 7).

Unsupervised PCA (Figure 7A) revealed distinct separation of sample clusters between the control and model groups, indicating marked disruption of serum metabolic profiles induced by STC modeling. Sample clusters of all treatment groups showed a tendency to cluster toward the control group with good intra-group aggregation, demonstrating that all RH doses effectively reversed STC-associated metabolic abnormalities. Notably, sample point clouds of the medium-dose RH group and positive control group overlapped substantially, preliminarily suggesting similar metabolic restoring effects between the two groups.

Supervised PLS-DA (Figure 7B) further amplified inter-group metabolic differences: sample point clouds of the model and control groups were completely separated, while the medium-dose RH group and positive control group, though closely clustered, displayed subtle distinctions. This not only confirmed the efficacy of RH intervention but also implied both commonalities and specificities in the metabolic regulatory pathways between medium-dose RH and the positive drug.

Permutation test results (Figure 7C) showed that the PLS-DA model yielded R²Y = 0.99 and Q² = 0.743, with Q² values of permuted models distributed near 0 or in the negative range. This indicated no model overfitting and confirmed good stability and reliability of the PLS-DA model, providing robust statistical support for interpreting inter-group metabolic differences.

Collectively, these findings demonstrate that RH alleviates STC symptoms through multi-dimensional regulation of serum metabolic profiles, with the medium-dose group achieving metabolic restoration most closely resembling that of the positive drug (thus representing the optimal dose). The subtle differences in metabolic profiles between the medium-dose RH group and positive control group also provide a basis for exploring the unique “multi-target regulatory” mechanism of RH.

KEGG enrichment analysis of the 15 key differential metabolites identified 40 significantly enriched pathways, with the top 20 pathways visualized using bubble plots (Figures 9A–D). The metabolite-pathway correlation network (Figure 9D) constructed a multi-layered regulatory network centered on L-glutamate, encompassing amino acid metabolism, neurotransmitter signaling, and intestinal motility regulation. As the primary energy substrate for intestinal epithelial cells and a key excitatory neurotransmitter in the ENS, dysregulation of L-glutamate metabolism directly impairs ENS synaptic transmission, leading to intestinal hypoperistalsis. Here, L-glutamate was identified as a central hub linked to alanine-aspartate–glutamate metabolism (map00250), arginine biosynthesis (map00220), and glutamatergic synapse (map04724), highlighting its pivotal role in RH-mediated reversal of STC-associated metabolic dysregulation.

Notably, N-acetylserotonin (a tryptophan metabolite) was associated with tryptophan metabolism (map00380) and serotonergic synapse (map04726), confirming that RH modulates the tryptophan-5-hydroxytryptamine (5-HT) axis to enhance intestinal motility. Given that insufficient 5-HT secretion is a hallmark of STC, RH indirectly elevates intestinal 5-HT levels by facilitating the conversion of tryptophan to N-acetylserotonin, thereby amplifying peristaltic signaling. Furthermore, 4-guanidinobutyric acid was linked to arginine and proline metabolism (map00330), indicating that RH modulates intestinal smooth muscle tone by driving the conversion of arginine to 4-guanidinobutyric acid. As a precursor of γ-aminobutyric acid (GABA), 4-guanidinobutyric acid acts via GABAergic synapses to suppress excessive smooth muscle contraction, alleviating motility retardation in STC rats.

KEGG topological analysis (Figure 9C) prioritized the core regulatory pathways of RH in STC based on pathway centrality (Impact Value) and enrichment significance [−log₁₀(p-value)]. Alanine-aspartate–glutamate metabolism emerged as the most central pathway (highest Impact Value), underscoring its dominant hub role in the global metabolic network. Activation of this pathway enhances intestinal epithelial energy metabolism and barrier integrity by increasing glutamine availability, mitigating epithelial damage in STC rats. Enrichment of arginine biosynthesis further delineated a key motility regulation mechanism: RH enhances arginine biosynthesis to promote nitric oxide (NO) production, which relaxes intestinal smooth muscle and facilitates propulsive motility. Since impaired NO synthesis is a major contributor to STC-associated motility dysfunction, RH-mediated activation of arginine biosynthesis directly restores NO bioavailability. Additionally, enrichment of tryptophan metabolism confirmed RH’s regulation of the 5-HT pathway, providing metabolomic evidence for its pharmacological feature of “multi-target modulation of intestinal motility.”

Hierarchical visualization via the KEGG enriched pathway circular plot (Figure 9B) revealed distinct differences in pathway enrichment profiles between the STC model group and RH-treated groups. The model group exhibited enrichment in neurodegenerative disease pathways [e.g., Huntington’s disease (map05016), spinocerebellar ataxia (map05014)], indicating enteric neurodegenerative-like metabolic dysfunction in STC rats—which is consistent with well-established STC pathogenesis, where ENS degeneration drives motility impairment. In contrast, RH-treated groups were enriched in amino acid metabolism (map00220, map00250) and synaptic signaling pathways (map04724, map04727), demonstrating that RH reverses neurodegenerative metabolic dysfunction by restoring amino acid homeostasis and synaptic transmission, thereby re-establishing ENS regulatory control. These inter-group pathway differences further validate RH’s targeting efficacy, providing direct evidence for its “ENS function restoration” mechanism.

Collectively, RH intervenes in STC primarily via an axis-based mechanism: “amino acid metabolic reprogramming—neurotransmitter signal activation—intestinal motility restoration.” Specifically, RH reprograms the amino acid metabolic network by activating alanine-aspartate–glutamate metabolism and arginine biosynthesis, enhancing intestinal epithelial energy supply and NO production capacity; modulates neurotransmitter release and restores intestinal nerve regulation by targeting the tryptophan-5-HT pathway and glutamatergic/GABAergic synaptic signals; and alleviates intestinal motility retardation by repairing the intestinal epithelial barrier and regulating smooth muscle tone.

The Spearman correlation heatmap (Figure 10A) constructed a fine-scale interaction network To characterize the associations between metabolites and gut microorganisms, Spearman’s rank correlation analysis (Figure 9) was performed, with results visualized as a clustered heatmap.

In the heatmap, L-glutamate exhibited strong positive correlations with Bacillota-phylum genera (e.g., Ligilactobacillus reuteri, Bacteroides dorei). This correlation indicates that these genera enhance L-glutamate biosynthesis by expressing glutamate dehydrogenase: as the primary energy substrate for intestinal epithelial cells and a key excitatory neurotransmitter in the ENS, elevated L-glutamate levels directly strengthen ENS synaptic transmission—reversing the hypoperistalsis caused by glutamate metabolic dysregulation in STC rats.

Similarly, 4-guanidinobutyric acid showed a significant positive correlation with Ligilactobacillus reuteri. This association implies that L. reuteri facilitates the conversion of arginine to 4-guanidinobutyric acid: as a precursor of γ-aminobutyric acid (GABA), 4-guanidinobutyric acid acts via GABAergic synapses to suppress excessive intestinal smooth muscle contraction, thereby alleviating motility retardation in STC models. These positively correlated microbial-metabolite modules directly validate the mechanism by which gut microbiota modulate metabolite production to improve intestinal motility.

In contrast, N-acetylserotonin (a tryptophan metabolite) displayed a strong negative correlation with Anaerotruncus sp. This negative association suggests that Anaerotruncus sp. inhibits tryptophan hydroxylase activity, reducing the conversion of tryptophan to N-acetylserotonin (a precursor of 5-HT). RH intervention alleviates this inhibition by decreasing Anaerotruncus sp. abundance, indirectly elevating intestinal N-acetylserotonin levels and subsequent 5-HT secretion—strengthening peristaltic signaling in STC rats.

Additionally, loperamide (the STC model inducer) showed a significant positive correlation with Dorea sp., indicating that Dorea sp. abundance increases in response to loperamide exposure and contributes to model-induced metabolic dysregulation. RH reverses this dysbiosis by downregulating Dorea sp. abundance, restoring metabolic homeostasis.

From the perspective of metabolite subclass clustering, the “Amino acids, peptides, and analogues” subclass showed the closest associations with Bacillota-phylum genera, confirming that amino acid metabolism is the core module of microbiota-metabolite interactions. In contrast, diphenylmethanes were specifically correlated with Actinobacteriota-phylum genera, suggesting this metabolite class regulates the secondary metabolism of gut microbiota. This subclass-phylum specificity refines the microbiota-metabolite interaction network, providing a precise target framework for RH-mediated regulation.

A Venn diagram of differential pathways between the metabolome and microbiome (Figure 10B) revealed 35 metabolome-unique pathways, 31 microbiome-unique pathways, and 18 co-enriched pathways (total 84 pathways). Most single-omics studies focus on independent regulation of either omics layer, but this study quantified the contribution of microbiota-metabolite interactions to RH intervention (18/84, 21.4%)—the first report of such a synergistic regulation ratio for RH.

The KEGG pathway enrichment bar plot (Figure 10C) further distinguished regulatory patterns between the metabolome (Metab, blue bars) and microbiome (KO, red bars) across functional categories. Unlike previous studies that focused on either neurotransmitter signaling (metabolome) or microbial metabolism (microbiome), this analysis identified “Metabolism” as the most prominently co-enriched category (62.5% of co-enriched pathways). For example, in arginine and proline metabolism, the microbiome contained ~70 differential genes (far more than the metabolome’s ~ 10 differential metabolites), confirming that gut microbiota act as upstream regulators of this pathway—consistent with RH’s ability to enhance arginine biosynthesis (via microbiota) to promote NO production and smooth muscle relaxation.

Although this study has preliminarily revealed the mechanism by which RH exerts anti-STC effects by regulating intestinal microecology, alleviating metabolic pathway disorders, and modulating gastrointestinal hormone levels—using animal experiments, metabolomics, and metagenomics techniques—it has several notable limitations that need to be addressed in subsequent research.

First, the small sample size is a major limitation. The sample size of rats in each group was only 3. Although data reliability was ensured through quality control sample validation and repeated measurements, the small sample size may reduce statistical power. Particularly in gut microbiota differential analysis and metabolite correlation analysis, it may lead to detection bias for some low-abundance differential bacteria or metabolites, affecting the stability and reproducibility of the results. Future studies should expand the animal sample size and conduct multiple independent batches of experiments to verify the generalizability of the conclusions.

Second, the validation of underlying mechanisms is insufficiently in-depth. Based on multi-omics correlation analysis, this study speculates that RH may exert its effects by regulating the abundances of Eubacteriales and Lachnospirales, activating arginine/tryptophan metabolic pathways, and modulating molecules such as 5-HT and NO. However, direct mechanism validation experiments are lacking. For example, fecal microbiota transplantation (FMT), specific genus knockout, or pathway inhibitor intervention were not used to clarify the causal role of core microbiota (e.g., Ruminococcus sp., Eubacterium sp.) or key metabolic pathways (e.g., arginine-proline metabolism) in the anti-STC effect of RH. Meanwhile, the specific targets and molecular regulatory networks of active components in RH (e.g., terpenoids, lipids) have not been clarified, and the complete “component-microbiota-pathway-pharmacodynamic” regulatory chain remains unelucidated. In the future, in vitro cell experiments, genetically edited animal models, and molecular interaction technologies should be combined to further validate the causal relationships of the core mechanisms.

Finally, there is a lack of support from clinical translation research. This study was conducted using an STC rat model, and significant differences exist in pathophysiological status between animal models (e.g., single-factor induced constipation, standardized feeding environment) and clinical STC patients. Clinical patients often exhibit characteristics such as dietary heterogeneity, comorbidities (e.g., diabetes mellitus, anxiety, depression), and more complex gut microbiota diversity. Additionally, this study did not explore the safety, tolerability, or optimal dosage of RH in humans. Therefore, the current research conclusions cannot be directly extended to clinical applications. Future studies should conduct well-designed clinical cohort studies, enroll STC patients with different disease courses and subtypes, and systematically evaluate the clinical efficacy, adverse reactions of RH, as well as its effects on patients’ intestinal microecology and metabolic profiles—thus providing evidence-based medical evidence for its clinical translation.

Although the above limitations do not affect the reliability of the core conclusions of this study, they point out directions for future research. In the future, the theoretical system of RH against STC can be further improved by expanding the sample size, deepening mechanism validation, and promoting clinical translation, providing a more solid scientific basis for its development as a new therapeutic drug or functional food.