Wild-type (WT) male C57BL/6 mice were purchased from Guangdong Medical Laboratory Animal Center (Guangdong, China). They were bred in a specific pathogen-free (SPF) environment at 22 ± 2°C and a humidity of 55 ± 10% on a 12-hour light/dark cycle. The mice were aged 8–10 weeks and weighed 25–30 grams at the beginning of the experiments. Animal care and management protocols were performed according to the Institutional Animal Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (ethics no. TCMF1-2019063).

Mice were administered 2.5% DSS (w/v; 36,000–50,000 M.W.; MP Biomedicals, Santa Ana, California, USA) in water for 7 days to induce experimental colitis and then administered regular water for 3 days. Stigmasterol (400 mg/kg/d) was provided to stigma + DSS group mice orally by gavage for 10 days. The stool consistency, faecal blood, and weight loss of each mouse were assessed daily. The DAI score is the mean of the three parameters ( Supplementary Table S1 ) (21). On day 10, fresh faeces were collected from the mice for FMT experiments and 16S rRNA sequencing.

The mice were administered an ABX cocktail orally by gavage for 5 days to deplete the gut microbiota. The cocktail comprised amphotericin (1 mg/mL), neomycin (10 mg/mL), vancomycin (50 mg/mL) and metronidazole (100 mg/mL) (22). Fresh faeces were collected from donor mice (from the DSS and stigma + DSS groups) and resuspended in PBS (100 mg/mL). The suspension was vortexed thoroughly for 30 s before centrifugation. Next, the supernatant was intragastrically administered to antibiotic-treated mice once a day for 5 days.

Colon tissues were cleaned with PBS and fixed in 4% paraformaldehyde. Next, they were embedded in paraffin, sectioned and stained with haematoxylin and eosin (H&E, magnification: 40× and 200×). Histological sections were blindly observed by an experienced pathologist. Histological scores, including the crypt structure, inflammation, mucosal thickening, crypt abscesses and goblet cell exhaustion, were evaluated ( Supplementary Table S2 ). Immunofluorescence staining for PPARγ (red) and DAPI (blue) in colon tissue sections was also performed according to the manufacturer’s instructions.

Distal colon tissues were homogenized, and proteins were extracted. A BCA kit (Beyotime, Beijing, China) was used to determine the protein concentrations. According to the manufacturer’s instructions (Dogesce, Beijing, China; Andygene Biotechnology Co., Ltd, Beijing, China), the concentrations of the inflammatory cytokines IL-6, IL-1β, TNF-α and serum IL-10, TGF-β and IL-17A levels were then measured using ELISA kits. The absorbance at 450 nm was measured using a microplate reader (Thermo Scientific, Waltham, MA, USA).

Genomic DNA was extracted from faecal samples and quantified. Quality genomic DNA samples were then used for PCR amplification. The V4 region of the 16S rRNA gene was amplified using the 515F-806R primer. PCR was performed in a PCR thermocycler (GeneAmp 9700; Applied Biosystems, France). A HiSeq 2500 system (Illumina, San Diego, CA, USA) was used for sequencing. For 16S analysis, QIIME (version 1.9.1) was used to demultiplex and quality-filter the raw FASTQ files. Next, operational taxonomic units (OTUs) were generated and clustered using a 97% threshold and Usearch (version 7.1). After aligning with the gold database (v20110519), UCHIME (4.2.40) was used to filter out chimaeric sequences, and usearch_global was used to quantify the OTU abundances for each sample. For taxonomic analysis of each representative OTU, the Greengenes database (v201305) was used based on the RDP classifier (Version 2.2) with a 0.8 confidence value. OTUs were then assigned to different hierarchical levels and taxonomic relative abundance profiles were summarized. Alpha diversity was studied using diversity (Shannon) and richness (Chao, ace, sobs) indexes.

The contents of faecal SCFAs were assessed using gas chromatography coupled with mass spectrometry (Agilent 7890B/MSD 5977A; Wilmington, DE, USA). Mixtures of acetate, propionate, butyrate, isobutyrate, valerate, and isovalerate were used as standards, which were acquired from Sigma-Aldrich (St. Louis, MO, USA).

The crystal structure of the protein was obtained from the Protein Data Bank (PDB) database (https://www.rcsb.org/). For the target preparation, the original ligands and water molecules of the protein were deleted, and hydrogen was added to polar groups via PyMOL 2.3.2. For ligand preparation, the chemical structures were retrieved from the DrugBank database (https://www.drugbank.ca). Using AudoDock MGL Tools 1.5.6, atomic partial charges were added, and the torsional root of the ligands was chosen. The molecular docking calculations were performed using Autodock Vina 1.1.2. The docking results were analysed and visualized using PyMOL 2.3.2.

Single-cell suspensions from mouse spleens and MLNs were prepared using a 100-μm cell strainer. After centrifugation, red blood cells (RBCs) were lysed with ACK buffer. Colonic lamina propria mononuclear cells (LPMCs) were extracted as previously described (23). Next, the cells were washed with staining buffer and used for flow cytometry analysis.

Treg and Th17 cells were identified using flow cytometry. Lymphocytes were stained with anti-CD4 FITC and anti-Foxp3 PE antibodies to identify Treg cells. To identify Th17 cells, lymphocytes were first stimulated in 5% carbon dioxide at 37°C for 6–8 hours using a Cell Stimulation Cocktail (plus protein transport inhibitors), which contains phorbol ester, phorbol 12-myristate 13-acetate (PMA), ionomycin, brefeldin A and monensin. The lymphocytes were then stained with anti-CD4 FITC and anti-IL-17 PE antibodies. The percentages of CD4+Foxp3+ (Treg) cells and CD4+IL-17+ (Th17) cells were then detected by flow cytometry. The data were analysed using FlowJo V.10 software. All the reagents were purchased from Tonbo Biosciences (San Diego, CA, USA).

Blood from IBD patients was collected after informed written consent was obtained. Peripheral blood mononuclear cells (PBMCs) were isolated from diluted blood by density-gradient centrifugation using lymphocyte separation solution (TBD, Tianjin, China). Naïve CD4⁺ T cells were then separated using the EasySep™ Human Naïve CD4⁺ T Cell Isolation Kit (Stem Cell Technologies, London, UK). All human blood samples were obtained from the First Affiliated Hospital of Guangzhou University of Chinese Medicine, China. This study was approved by the Medical Ethics Committee of the Academic Medical Center of Guangzhou University of Chinese Medicine (ethics no. JY[2021]025) and was registered in the Chinese Clinical Trial Registry (ChiCTR) (registration no. ChiCTR2100043238; registration date: 2021-02-09).

Naïve CD4+ T cells were cultivated in a 96-well plate (1 × 10⁵ cells/well) containing plate-bound anti-CD3 antibody (2.5 µg/ml) and anti-CD28 antibody (5 µg/ml) (Tonbo Biosciences). To induce Treg cell differentiation, TGF-β1 (3 ng/ml) and IL-2 (2 ng/ml) were added. To induce Th17 cell differentiation, recombinant human TGF-β1 (3 ng/ml), IL-23 (25 ng/ml), anti-IL-4 antibody (5 µg/ml), and IFN-γ antibody (5 µg/ml) (PeproTech) were added. Naïve CD4+ T cells were treated with either butyrate alone (0.2 mM) or both butyrate (0.2 mM) and GW9662 (10 µM) for 5 days. Next, the cells were used for flow cytometry analysis, western blotting, glycolytic rate analysis and a mitochondrial stress assay.

RIPA buffer (Beyotime) was used to prepare cell lysates. After quantification of the protein concentration using a BCA kit, the proteins were dissolved via SDS-PAGE and transferred to polyvinylidene fluoride (PVDF) membranes. After blocking and incubation with the primary antibody, the membrane was then probed with secondary antibodies. The protein bands were detected and visualized using a ChemiDoc Imaging System (Bio-Rad). Antibodies against PPAR gamma (Abcam, ab178860), HIF-1α (Abcam, ab216842), and β-Actin (Abcam, ab8226) were used.

OCR and glycoPER were measured using a Seahorse XF24 Extracellular Flux Analyser (Seahorse Bioscience, Agilent Technologies, Santa Clara, CA). A Seahorse XF Cell Mito Stress Test Kit (103015-100; Seahorse Bioscience) and Seahorse XF Glycolytic Rate Assay Kit (103344-100; Seahorse Bioscience) were also used. T cells (3×10⁵/well) were seeded in 24-well Cell-Tak-coated (22.4 μg/ml, Corning) Seahorse culture plates for analysis. For OCR measurement, 1.5 μM oligomycin, 0.5 μM FCCP and 0.5 μM antimycin-A/rotenone were added sequentially. To measure the glycoPER and mitoOCR/glycoPER ratio, 0.5 μM Rot/AA and 50 mM 2-DG were added sequentially.

GraphPad Prism 8.0 (GraphPad Software, San Diego, USA) was used to analyse the data, which are presented as means ± SD. Unpaired two-tailed Student’s t-test or one-way analysis of variance (ANOVA) was performed for statistical analyses. Survival differences were assessed using the log-rank test. P values were defined as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; n.s., not significant (P > 0.05).

First, we explored the protect effect of stigmasterol on colitis. The workflow used to study the pharmacodynamics of stigmasterol is illustrated in Figure 1A . Compared with the DSS group, the mouse body weight was increased ( Figure 1B ), the DAI score was decreased ( Figure 1C ), and shortening of the colon was alleviated ( Figure 1D ) in the stigma + DSS group. Furthermore, stigmasterol appreciably reduced the colon pathohistological score ( Figure 1E ) and downregulated the expression of inflammatory factors, including IL-6, IL-1β and TNF-α, in DSS-treated mice ( Figure 1F ). However, no noticeable difference in mortality was observed ( Figure 1G ). The above results suggested that stigmasterol prevents DSS colitis and may protective against IBD.

We next explored whether stigmasterol protects against colitis by regulating the Treg/Th17 balance. Flow cytometric analyses demonstrated that the proportion of Tregs among gated CD4⁺ T cells in the spleen, MLNs and colon was reduced in DSS colitis. However, stigmasterol increased the Treg proportion ( Figures 2A–D ). Similarly, DSS-induced colitis mice exhibited a higher percentage of Th17 cells than controls, and stigmasterol reduced the percentage of Th17 cells ( Figures 2E–H ). Additionally, stigmasterol increased the expression of IL-10 and TGF-β but downregulated the expression of IL-17A ( Figure 2I ). These findings strongly suggested that stigmasterol promotes Treg cell development and inhibits the Th17 cell response in DSS-induced colitis mice.

Since the gut microbiota plays important roles in IBD pathogenesis, we next determined whether stigmasterol influences the gut microbiota. At the genus level, stigmasterol increased the composition of Ruminococcus, Prevotella, Paraprevotella, Helicobacter, Odoribacter, Clostridium_IV, and Clostridium_XlVa. Additionally, stigmasterol decreased the abundances of Streptococcus, Escherichia, Enterococcus and Allobaculum ( Figure 3A and Supplementary Table S3 ). Different diversity indexes (Sobs, Shannon, ACE, and Chao) showed similar trends: stigmasterol improved the diversity of intestinal microflora ( Figure 3B ). Clustering analysis of OTUs revealed the differences in the microbial community among the three groups ( Supplementary Figure S1 ). The dominant bacteria in the stigma+DSS group included Helicobacter, Odoribacter, Prevotella, Oscillospira, Paraprevotella, Turicibacter, Ruminococcus, Butyricicoccus, Ruminococcaceae, and Paraprevotellaceae ( Supplementary Figure S2 ). The differences in the faecal microbiota composition are presented in a heat map ( Figure 3C ). Additionally, stigmasterol FMT treatment alleviated colitis ( Supplementary Figure S3 ) and restored the Treg/Th17 balance ( Supplementary Figure S4 ). Because the trend of Treg/Th17 cells in the colon was consistent with that in the spleen and MLNs, we detected the Treg/Th17 cells in only the spleen and MLNs in the FMT experiment.

Then we analysed the faecal microbiota composition of each group at the class level ( Figure 4A and Supplementary Table S4 ). We found that stigmasterol treatment increased the composition of Clostridia ( Figure 4B ), which is closely related to the production of SCFAs (24). Quantitative analysis of the contents of specific SCFAs demonstrated that the concentrations of SCFAs in faeces decreased in DSS-induced colitis mice. Additionally, stigmasterol treatment markedly increased the acetate, propionate, butyrate, isobutyrate and valerate levels in the faeces of DSS-induced colitis mice ( Figure 4C ). Pearson correlation-based network analysis revealed that the butyrate level was most strongly correlated with the abundance of certain species in the gut microbiota ( Figure 4D and Supplementary Table S5 ). The abundance of Clostridia in the gut was positively correlated with the butyrate concentrations in faeces ( Supplementary Figure S5 ). These results further suggested that stigmasterol supplementation results in a change in the intestinal microflora and affects the levels of SCFAs, particularly butyrate.

Next, we explored the mechanism underlying butyrate-mediated Treg and Th17 cell differentiation in vitro. Molecular docking analysis suggested that butyrate directly interacted with PPARγ, a key target for IBD therapy which was upregulated by stigmasterol treatment ( Supplementary Figure S6 ). Butyrate might bind to the active sites of PPARγ at Arg-288 ( Figure 5A ). Additionally, the expression of PPARγ in Treg and Th17 cells was upregulated after butyrate intervention ( Figure 5B ). To confirm that PPARγ activation is important for Treg and Th17 cell differentiation by butyrate, we used GW9662 to specifically inhibit PPARγ activity. Further analysis indicated that butyrate increased the expression of Treg cells; however, the promotion of Treg cell differentiation was inhibited after GW9662 treatment ( Figures 5C, D ). Consistently, butyrate reduced Th17 cells, and the inhibition of Th17 cell differentiation was attenuated after GW9662 treatment ( Figures 5E, F ).

Seahorse analyses demonstrated that butyrate supplementation increased the basal and maximal oxygen consumption rates (OCRs) in naïve CD4+ T cells under Treg-polarizing conditions; however, the OCR was decreased after GW9662 treatment ( Figures 6A–C ). This finding indicated that butyrate increased OXPHOS in a PPARγ-dependent manner, thereby promoting Treg cell differentiation. Simultaneously, under Th17 polarizing conditions, the level of glycolysis was decreased, and the mitochondrial OCR (mitoOCR)/proton efflux rate from the glycolysis (glycoPER) ratio was increased under butyrate treatment, whereas GW9662 treatment increased the basal level of glycolysis and decreased the mitoOCR/glycoPER ratio ( Figures 6D–F ). Hypoxia inducible factor-1α (HIF-1α) is critical for glycolytic metabolism (25). Butyrate supplementation reduced the expression of HIF-1α under Treg- and Th17-polarizing conditions, whereas GW9662 treatment increased the expression of HIF-1α ( Figure 6G ). The results suggested that butyrate promotes the metabolic transition of glycolysis to OXPHOS by activating PPARγ, thereby inducing T-cell differentiation into Treg cells but not Th17 cells.

The aforementioned results revealed that stigmasterol restores the Treg/Th17 balance to ameliorate intestinal inflammation by modulating the intestinal microflora and its metabolite SCFAs. To further observe the role of the intestinal microflora, the mice were pretreated with an ABX cocktail to deplete the intestinal flora and then treated with stigmasterol ( Figure 7A ). Interestingly, when the gut microbiota was suppressed, the therapeutic effects of stigmasterol were abolished. Weight loss ( Figure 7B ), the DAI score ( Figure 7C ), the colon length ( Figure 7D ), the colonic histopathological score ( Figure 7E ), and the mortality rate ( Figure 7G ) were not substantially different between the ABX (DSS) and the ABX (stigma+DSS) groups. Similarly, no significant differences were found in the IL-6, IL-1β, or TNF-α ( Figure 7F ) levels or number of Treg cells ( Figures 8A–C ) or Th17 cells ( Figures 8D–F ). The serum IL-10, TGF-β, or IL-17A levels showed no apparent difference ( Figure 8G ). Overall, these results indicated that the gut flora is important in the therapeutic efficacy of stigmasterol.