
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72797
10.1038/s41598-024-72797-y
Article
Water-soluble garlic polysaccharide (WSGP) improves ulcerative volitis by modulating the intestinal barrier and intestinal flora metabolites
Shao Xin 12
Li JiaLong 2
Shao Qi 3
Qu Rong 4
Ouyang Xin 4
Wang Yong twyong@jnu.edu.cn

2
Chen ChunBo gghccm@163.com

15
1 https://ror.org/0124z6a88 grid.508269.0 Department of Critical Care Medicine, Maoming People’s Hospital, Maoming, 525000 Guangdong China
2 https://ror.org/02xe5ns62 grid.258164.c 0000 0004 1790 3548 Department of Food Science and Engineering, Jinan University, Guangzhou, 510632 Guangdong China
3 https://ror.org/02xe5ns62 grid.258164.c 0000 0004 1790 3548 Department of Cell Biology, Jinan University, Guangzhou, 510632 Guangdong China
4 grid.410643.4 Department of Intensive Care Unit of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 Guangdong China
5 https://ror.org/01hcefx46 grid.440218.b 0000 0004 1759 7210 Department of Critical Care Medicine, Shenzhen People’s Hospital, Shenzhen, 518001 Guangdong China
14 9 2024
14 9 2024
2024
14 2150420 6 2024
10 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
WSGP has demonstrated significant potential for various bioactive effects. However, limited research has explored their anti-ulcerative colitis (UC) effects and mechanism on the colonic system and gut microbial metabolites. We evaluated the ameliorative effects of WSGP on the UC mice model. Using H&E to assess histological injury of colon morphology, AB-PAS staining to detect mucin secretion from goblet cells and the mucous layer, IF to evaluate the expression of intercellular tight junction proteins, ELISA to measure inflammatory factors, WB analysis to measure protein expression of inflammatory signaling pathways, RT-qPCR to quantify gene transcription of inflammatory factors, and LC-MS to analyze metabolites in mouse cecum contents. WSGP supplementation increased food intake, body weight, and colon length while reducing disease activity and histological scores in colitis-afflicted mice. WSGP mitigated colonic tissue damage and restored intestinal barrier integrity by suppressing NF-κB/STAT3 signaling, thereby decreasing gene transcription, protein expression of proinflammatory factors, and nitric oxide production. Additionally, WSGP improved UC by altering the variety of intestinal microbial metabolites. This study demonstrates that WSGP supplementation attenuates UC mice by suppressing the NF-κB/STAT3 signaling pathway, enhancing mucosal barrier function, reducing pro-inflammatory cytokines, and modulating gut microbial metabolites.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72797-y.

Keywords

Garlic polysaccharide
Ulcerative colitis
Flora metabolites
NF-κB/STAT3
Inflammation
Subject terms

Carbohydrates
Metabolomics
Diseases
Gastroenterology
Guangdong Medical Research Fund ProjectB2023048 Shao Xin Office of Talent Work Leading Team in Maoming (MaoRenCaiBan (2020) 24, Project)200221115835503 Chen ChunBo issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

An inflammatory bowel disease (IBD) is a chronic and incurable condition that leads to loss of appetite, abnormal pain, diarrhoea, rectal bleeding, and weight loss1. Ulcerative Colitis (UC) and Crohn’s disease (CD) are the two primary manifestations of inflammatory bowel disease (IBD). IBD has become one of the most common gastrointestinal diseases over the last few decades2,3. There is no known mechanism for how UC develops, but it usually occurs due to the location and symptoms of the disease4–6. Furthermore, studies have shown that the onset of UC increases the risk of colon cancer7,8. Despite the unknown pathogenesis of UC, more and more research in recent years has implicated the interplay between inflammatory signaling pathways and UC pathogenic mechanisms, including nuclear factor kappa-B (NF-κB) and signal transducer and activator of transcription (STAT3). Substantial research shows that NF-κB and STAT3 pathways may mediate inflammatory cytokine production and amplify inflammatory cytokine release, which may result in UC progression9–11. The intestinal flora has been found to inhibit the occurrence of inflammation12. UC research has also been focused on the relationship between metabolites from specific intestinal flora and inflammation13,14. Therefore, exploring the mechanisms of immunomodulatory and anti-inflammatory effects of natural functional polysaccharides in inflammatory bowel disease is crucial to elucidate the relationship between key metabolites of the intestinal flora and these targets.

UC is commonly treated with steroids, aminosalicylates, immunosuppressive and biological agents, antibiotics, and antibodies. Although these drugs can alleviate their inflammatory manifestations, they also have serious side effects, including kidney impairment, hormone dependence, stimulation of infections, and damage to the digestive and hematopoietic systems15,16. The discovery of alternative anti-inflammatory drugs or functional foods, which are highly effective and non-toxic, is therefore of great clinical significance.

Polysaccharide components in natural products have demonstrated significant anti-oxidant and anti-inflammatory capacities, which have led to an efficient and non-toxic method of treating and preventing UC. The efficacy of natural polysaccharide components in treating DSS-induced UC has been demonstrated in numerous animal studies17–19. For thousands of years, garlic has been used as a flavoring agent and herbal medicine and designated as an edible medicine by the Chinese Ministry of Health. Golden pesto primarily comprises water, carbohydrates, proteins, amino acids, and organosulfur compounds, accounting for 62-68%, 26-30%, 1.5-2.1%, and 1-1.5%, respectively20. Polysaccharides comprise most dried garlic components21,22. Various physiological functions of garlic polysaccharides have been discovered, including antitumor, immune boosting, antioxidative, anticoagulant qualities, liver protection, and intestinal flora balancing23–26. Our previous studies have also demonstrated that garlic polysaccharides inhibit inflammation in LPS-induced murine RAW264.7 macrophages27. Nevertheless, no comprehensive research has examined golden aromatic garlic polysaccharides’ in vivo anti-inflammatory effects.

In this study, the anti-inflammatory ability of WSGP was assessed by observing the histological morphology, cytokine secretion and gene expression, disease activity index (DAI), and changes in metabolites of its intestinal microorganisms in the UC mice model after WSGP treatment. Moreover, garlic polysaccharide improved the pathogenic mechanism of UC by targeting downstream signaling pathways, such as NF-κB and STAT3. This study will provide theoretical and experimental evidence to support the development of novel health food products based on garlic polysaccharide to alleviate ulcerative colitis.

Materials and methods

Reagents and chemicals

The WSGP was obtained by the method previously used by our research team to isolate and purify WSGP28. Anhydrous ethanol, phenol, concentrated sulphuric acid, ether, chloroform, isoamyl alcohol, methylenebisacrylamide, ammonium persulphate, acrylamide and tetramethylethylenediamine were purchased from Guangzhou Chemical Reagent Factory, China; Isoflurane was purchased from Shenzhen RWD Life Science Co., Ltd.; 8 M lithium chloride and 3 M sodium acetate were purchased from Guangzhou Meiji Co. Ltd., China; IL-1β (E-EL-M0037), Il-6 (E-EL-M0044), and TNF-α (E-EL-H0109) Sandwich ELISA kit were purchased from Elabscience Biotechnology Co., Ltd., China; Penicillin-streptomycin double antibody, TRIzol™ reagent, 6× Loading buffer, Agarose agarose, nucleic acid dye and ELC ultrasensitive luminescent solution were purchased from Thermo Ltd., USA; fecal occult blood kit from Shanghai Yuanye Co. Ltd, China; PrimeScript™ RT reagent kit with gDNA Eraser reverse transcription kit and TB Green® Premix DimerEraser™ fluorescence quantification kit from Takara Bio, Japan; Antibodies for GAPDH (#2118), p-IκB-α (#2859), IκB-α (#4812), p-p65 (#3033), p65 (#8242), p-STAT3 (#9145), STAT3 (#9139) antibodies were purchased from Cell Signaling Technology, Inc., USA; HRP-labelled goat anti-mouse IgG HL were purchased from abcam, UK; PVDF membranes were purchased from Millipore, USA; Whatman™ filter paper was purchased from Beijing Solebro Co., China.

Animals

We purchased forty SPF-grade 5-week-old C57BL/6 male mice from Beijing Huafukang Biotechnology Co. and fed them in Jinan University’s standard animal house.

Modeling and grouping

Acute ulcerative colitis was modeled in mice by following the modeling method of Peng et al. with minor modifications29. The mice were first acclimatized for 10 days. All mice were housed in a constant temperature (25 ± 3°C) with humidity (50 ± 5%) automatically regulated environment, and all mice were allowed unrestricted free access to water and regular chow. The mice were evenly and randomly divided into 5 groups of 8 mice each according to their body weight: group N (Normal), group D (3% DSS), group GP (200 mg/kg/d WSGP), group DGPL (DSS + 200 mg/kg/d WSGP) and group DGPH (DSS + 400 mg/kg/d WSGP). Each mouse in all groups was numbered separately with a marker pen. Specifically (Fig. 1), from days 1 to 14, mice in the GP and DGPL groups were given WSGP (200 mg/kg/day, 25 mg/mL) by continuous gavage for 14 days. Meanwhile, the DGPH group was given WSGP (400 mg/kg/d, 25 mg/mL) for 14 days, the dosage of WGSP was chosen based on our previous studies28. From days 1 to 7, all five groups drank water ad libitum. From days 8 to 15, groups D, DGPL, and DGPH were fed with 3% (w/v) DSS, while groups N and GP were fed with normal water ad libitum. Body weight and food intake were measured daily during the experiment. The mice were sacrificed on day 15 following anesthesia with isoflurane.

Fig. 1 Model establishment and experimental group design of acute UC.

Disease activity index (DAI)

The experimental period (1 day-14 day) involved measuring body weight daily, calculating food intake, and observing fecal characteristics. The average daily food intake of each group of mice was calculated according to Eq. (1.1). The average daily weight loss of each group of mice was calculated according to Eq. (1.2), defined as the percentage difference between the initial body weight (day 0 of the experiment). The daily disease activity index (DAI) was scored by combining the weight loss and diarrhoea results. The DAI was scored according to Ghia et al.30, shown in Table S1, and was calculated for each mouse in each group according to Eq. (1.3).1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{Average amount of food eaten}= \frac{{\varvec{A}}_{\varvec{f}}}{\varvec{n}}$$\end{document}

1.1 Where: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{A}}_{\varvec{f}}$$\end{document} is the sum of all surviving mice in the same group eating on the same days, and n is the number of surviving mice in the group.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{Weight loss}= \frac{{\varvec{M}}_{1}-{\varvec{M}}_{0}}{{\varvec{M}}_{0}}\times 100{\%}$$\end{document}

1.2 Where: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{M}}_{1}$$\end{document} is the weight of each surviving mouse on that day, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{M}}_{0}$$\end{document} is the initial weight of that surviving mouse before the start of the experiment.3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{DAI}= \frac{{\varvec{S}}_{\varvec{w}}+{\varvec{S}}_{\varvec{f}}+{\varvec{S}}_{\varvec{b}}}{3}$$\end{document}

1.3 Where: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{\varvec{S}}_{\varvec{w}}, {\varvec{S}}_{\varvec{f}}, {\varvec{S}}_{\varvec{b}}$$\end{document} are scored for body weight and feces in mice, respectively.

Colon histopathology

Histopathological sections and hematoxylin-eosin (H&E) staining of the mouse colon were performed according to the method described in the staining kit and images were collected. The method for pathological observation and scoring concerning the scoring criteria is shown in Table S231. The tissues were stained with AB-PAS in the order of operation of dewaxing blue staining-periodic acid staining-chevron staining-hematoxylin staining-dehydration sealing, and the number of cupped cells was determined using Image-pro plus 6.0 software. Meanwhile, immunofluorescence (IF) staining was performed for the observation of intestinal tight junction proteins.

Enzyme-linked immunosorbent assays (ELISA)

200 mg of colon tissue from mice were weighed, homogenized into 1800 µL of saline, and centrifuged at 4000 g for 20 min to obtain a 10% colon tissue homogenate. In addition, whole blood samples from mice were centrifuged at 12,000 r/min for 10 min at 4°C and the supernatant was then collected at −20°C. Cytokines in colon tissue homogenates, including IL-6, IL-1β, TNF-α, and NO in serum were assayed according to the ELISA reagent vendor’s instructions, and concentrations were observed according to the standard curve.

Western blotting (WB)

Expression of the inflammatory pathway proteins p-IκB-α, IκB-α, p-p65, p65, p-STAT3, and STAT3 was detected by Western blot. This was done as follows, homogenizing colon tissue in sodium dodecyl sulfate sample buffer containing protease and phosphatase inhibitors. Total protein was then extracted from the colon samples using RIPA lysis buffer containing protease inhibitors at a ratio of 1:100. Protein quantification was performed using a BCA assay kit. Equal amounts of protein (40 µg) were separated on 10% SDS-PAGE gels at 140 V for 1 h and then transferred onto polyvinylidene fluoride (PVDF) membranes using a semi-dry device. The membranes were closed with 5% skimmed milk. Primary antibodies (1:1000) were incubated two hours later with PVDF membranes at 4°C. The protein bands were visualized using an ECL kit and a developer after 2 h incubation with a secondary antibody (1:5000).

Quantitative real-time polymerase chain reaction (RT-qPCR)

Total RNA from colon tissue was extracted using TRIzol reagent. Extracted RNA was treated with lithium chloride and sodium acetate to remove any remaining DSS. Normalized RNA was calculated using a NanoDrop 2000 C spectrophotometer and a PrimeScrip RT kit with a gDNA eraser was used. The primer sequences used for the polymerase chain reaction are shown in Table S3. Relative expression levels of inflammation-related genes were detected using the 2−ΔΔCT method.

Detection of metabolites in the contents of the colon cecum

Detection of metabolites in the contents of mouse colon cecum using LC-MS/MS. Chromatographic conditions: The column was an ACQUITY UPLC HSS T3 (100 mm × 2.1 mm i.d., 1.8 μm); mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid) and mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid); the flow rate was 0.40 mL/min and the injection volume was 2 µL. The column temperature was 40 ℃. The mobile phase elution procedure is shown in Table S4. Mass spectrometry conditions: Electrospray ionized samples and mass spectrometry signals were acquired in positive and negative ion scanning modes (Table S5). Differential metabolite analysis was performed using R language ropls package (Version1.6.2) for principal component analysis (PCA) and orthogonal least squares discriminant analysis (OPLS-DA), and 7 rounds of cross-validation were used to assess the stability of the model. In addition, the student’s t-test and multiplicative analysis of variance were performed. The selection of significantly different metabolites was determined based on the variable weight values (VIP) obtained from the OPLS-DA model and Student’s t-test p-values, with metabolites with VIP > 2 and p < 0.05 being significantly different metabolites.

Statistical analysis

The data were subjected to one-way ANOVA using SPSS 26.0 software and the results were expressed as mean ± standard deviation (SD), and the experimental means were analyzed for the significance of difference (p < 0.05) using Duncan’s multiple tests. Data from analyses were plotted using Prism GraphPad 9.0, and Origin 10.0.

Results and discussion

WSGP reduces clinical signs in mice with colitis

The severity of colitis symptoms can be determined by specific signs and symptoms such as weight change, food intake, fecal characteristics, and DAI in a DSS-induced acute UC mouse model32,33. We established a mouse UC model using 3% DSS induction and evaluated the efficacy of WSGP at different concentrations by gavage to improve the apparent physical indices.

Due to the absence of DSS intervention on days 1 to 7, food intake and weight loss did not change significantly in all groups, body weight increased slowly, and DAI scores were 0. During the DSS intervention, DAI continued to increase in group D (Fig. 2A), while food intake and weight loss decreased and increased, respectively (Fig. 2B,C). Compared to the D group, the trend of change in the DGPH group was more significant than in the DGPL group, suggesting that the WSGP intervention alleviated the increase in weight loss and decreased food intake. The mice in group D had lost 19.97% of their body weight by day 14. DAI scores (7.00/5.67) and weight loss (14.89%/9.92%) in the DGPL and DGPH groups were significantly lower than those in the D group on day 14, with DGPH losing more weight than DGPL. Furthermore, there were no significant differences between the GP and N groups in terms of food intake, weight loss, DAI, and colonic length (p > 0.05), suggesting that the WSGP intervention alone did not cause a pathological inflammatory response in the gut.

Fig. 2 Observation and score of clinical symptoms in mice. (A) DAI score. (B) Food intake. (C) Baby weight changes. (D) A representative sample of colon length. (E) Statistical results of colon length. Different lowercase letters indicate that the data are statistically different (p < 0.05).

DSS causes shortening of the colon. As a result, colon length was often used as an important morphological indicator of inflammation34,35. According to Fig. 2D,E, the length of the colon in the DGPH and DGPL groups was shorter than that in the N and GP groups, but significantly longer than that in the D group (p < 0.05). Moreover, food intake, weight change, DAI, and colonic length did not differ significantly between groups (p > 0.05). Both the prevention and treatment groups improved the symptoms of colitis, indicating that WSGP does not cause inflammation. Collectively, WSGP interventions significantly improved colitis-related symptoms in mice with a dose-dependent trend.

WSGP improves histological morphology of the colon in mice with colitis

H&E staining is a common method for assessing colonic damage36. Therefore, we observed the mouse colon histological damage using H&E staining. As shown in Fig. 3, the colon in the N and GP groups exhibited typical features of normal morphological structure with intact mucosa, high columnar surface colonic cells (Co) and abundant cup cells (Gc), long crypt (Cr) of uniform depth, submucosa (SM) and peripheral muscle layer (M). However, in group D, the colonic tissue displayed severe GC failure, severe loss of Co and Cr, and significant leukocyte infiltration. SM also displayed severe epithelial damage and mucosal erosion. As compared to the D group, the DGPL and DGPH groups showed significantly less pathological damage and a lower histological score. In addition, the DGPH group had a thinner SM, a more intact Cr, and Co, as well as a less edematous or infiltrated inflammatory response. Based on these data analyses, it appears that WSGP reduces DSS-induced damage to colonic tissue and that DGPH is more effective than DGPL. A similar result was found by Tang et al. Polysaccharides from Astragalus membranaceus and Codonopsis pilosula were found to repair the disruption of the epithelial layer, the loss of goblet cells, and the infiltration of inflammatory cells caused by DSS by showing obvious effects37. Interestingly, Zhao et al. also found that tea polysaccharides containing selenium reduced colonic mucosal damage38.

Fig. 3 H&E staining section and a pathological score of colon tissue. Different lowercase letters indicate that the data are statistically different (p < 0.05).

WSGP promotes the secretion of intestinal mucin and the maturation of cupped cells in mice with enteritis

The mucosal barrier is the first line of defense for the intestinal tract, consisting of an internal mucus layer and an external mucus layer covering the surface of the colon39. Mucin secreted by cupula cells and mucin on the mucus layer was visualized using AB-PAS staining. N and GP cupped cells accumulated mucin, which was enriched on the surface of the colonic epithelium (Fig. 4). In contrast to the N and GP groups, the D group had significantly fewer cup cells and mucin areas than the no-DSS-induced group (p < 0.05). Intestinal mucus and sparse mucus are the most important defenses against pathogens. The former prevents bacteria and toxins from damaging intestinal epithelial cells, while the latter provides habitat and a source of nutrition for commensal microorganisms40. In colitis mice, WSGP intervention dramatically reduced mucin and cupped cell damage. The GC numbers and mucin areas in the DGPH and DGPL groups were considerably greater than those in the D group (p < 0.05), and the DGPH group was significantly higher than the DGPL group (p < 0.05). According to previous studies, mucin and cupped cells decrease during IBD29. In this study, higher doses of WSGP were more effective than lower doses in preventing the progression of colitis by preventing mucus destruction and cupped cell depletion.

Fig. 4 Pathological section of AB-PAS staining and the number of goblets. Different lowercase letters indicate that the data are statistically different (p < 0.05).

WSGP repairs the intestinal barrier in mice with colitis

IBD treatment focuses on maintaining the tightness and integrity of the intestinal barrier. The ZO-1 protein is a major component of tight junctions that may act as a marker of the mechanical intestinal barrier29,40. Occludin plays a crucial role in both tight junction stability and barrier function41,42. Claudin-1 is an integral membrane protein and a key component of tight junctions43. Consequently, we used the immunofluorescence approach to conduct colorimetric labeling immunofluorescence analysis on the three proteins ZO-1 (Fig. 5A), Ocludin (Fig. 5B), and Caludin-1 (Fig. 5C), as well as software-based statistical analysis of their mean densities (Fig. 5D–F). As compared to the N group, the D group showed significant decreases in the three proteins ZO-1, Ocludin, and Caludin-1, whereas the GP group exhibited higher levels of these proteins. The WSGP-treated mice (DGPL and DGPH groups) had significantly higher levels of ZO-1, Ocludin, and Caludin-1 than the D group, and the DGPH group had significantly higher levels than the DGPL group. The findings indicate that garlic polysaccharide enhanced intestinal tight junction-related protein expression in immunofluorescence at the protein level, demonstrating that garlic polysaccharide had a considerable protective impact on intestinal integrity. Therefore, garlic polysaccharide ameliorated colitis and protected intestinal barrier integrity and tightness, and its effects were better at higher doses than at lower doses.

Fig. 5 Representative images of immunofluorescence of ZO-1 (A), Claudin-1 (B), and Occludin (C). The mean density of ZO-1 (D), Claudin-1 (E), and Occludin (F). Different lowercase letters indicate that the data are statistically different (p < 0.05).

WSGP inhibits NO in serum and inflammatory factor secretion in colonic tissues of mice with colitis

Several studies have demonstrated that NO is a sensitive indicator of intestinal inflammation and oxidative stress. High levels of NO production and secretion, as well as inflammatory cytokines TNF-α, IL-6, and IL-1β, enhance the start and progression of inflammation44. To determine the amount of NO in the mice’s serum, we started by measuring the level of NO in their serum. Figure 6A shows that serum NO levels are low in mice in the normal N and WSGP-only GP groups, but significantly higher in the D group (p < 0.05). Inhibition of NO in the serum of mice in the DGPL and DGPH groups was substantially lower than that of the D group (p < 0.05), and the inhibition effect of the high-dosage group was significantly better than that of the low-dose group (p < 0.05) after the induction of DSS with various doses of WSGP. Based on the above results, WSGP of garlic polysaccharide had dose-dependent effects on intestinal inflammation in mice with colitis.

Fig. 6 Effects of WSGP on the production levels of NO in the serum (A), pro-inflammatory cytokines IL-6, TNF-α, and IL-1β (B–D), and gene expression of IL-6, TNF-α, and IL-1β (E–G) in the colonic tissues of DSS-treated mice. Different lowercase letters indicate that the data are statistically different (p < 0.05).

In colitis, cytokines are essential for controlling inflammatory mechanisms. TNF-α is a crucial cytokine implicated in the escalation of mucosal inflammation, including the activation of macrophages, neutrophils, and T lymphocytes10,45. IL-1β is generated by activated macrophages, in part through the formation of reactive oxygen species (ROS), and plays a crucial role at the beginning of IBD29. There is evidence that IL-6 plays a crucial role in both acute inflammation recovery as well as the transition from acute to long-term and chronic inflammation46. The protein expression levels of IL-6, IL-1β, and TNF-α are shown in Fig. 6B–D. Group D had considerably greater protein expression levels of all cellular inflammatory markers than group N (p < 0.05). All cellular expression levels of inflammatory factor proteins in the DGPL and DGPH groups were considerably lower than in the D group (p < 0.05), and protein expression levels of TNF-α in the DGPH group were almost identical to those in the N group. Moreover, the protein expression levels of IL-6 and IL-1β in the DGPH group were significantly lower than those in the DGPL group (p < 0.05). The gene expression levels of IL-6, IL-1β, and TNF-α are shown in Fig. 6E–G. The mRNA expression levels of all pro-inflammatory cytokines were substantially greater in the D group than in the N group (p < 0.05). Notably, the IL-6 mRNA expression level rose the greatest in the D group, rising to a level that was 123 times higher than that of the N group. In the DGPL and DGPH groups, three pro-inflammatory cytokines were significantly lower than in the D group (p < 0.05). From the above data analysis, it is clear that WSGP has consistent results in suppressing inflammatory factors at the protein synthesis and gene expression level.

WSGP blocks NF-κb/STAT3 signaling pathway in colonic tissues of mice with colitis

Activation of the NF-κB signaling pathway leads to phosphorylation of IκB-α, which releases p65 into the nucleus for gene transcription and promotes the expression of inflammatory cytokines47. STAT3 phosphorylation is another important signaling molecule involved in mediating inflammatory and immune responses48. STAT3 phosphorylation is induced by IL-6, which promotes the expression of cytokines such as TNF-α49. Accordingly, this study examined the phosphorylation levels of p65, IκB-α, and STAT3 proteins in the NF-κB and STAT3 signaling pathways. Phosphorylation levels of target proteins were determined as the ratio of phosphorylated to total proteins (p-IκB-α/IκB-α, p-p65/p65, p-STAT3/STAT3). As shown in Fig. 7 (original results shown in Fig. S1), group D significantly enhanced the phosphorylation levels of the target proteins compared to group N. In addition, amounts of p-IκB-α, p-p65, and p-STAT3 proteins increased significantly (p < 0.01). However, the above trends were reversed after WSGP intervention at both 200 mg/kg/d and 400 mg/kg/d. The DGPH group repressed the expression of p-IκB-α, p-p65, and p-STAT3 proteins significantly better than the DGPL group (p < 0.05). The aforementioned findings further confirm those showing a considerable increase in IL-6, TNF-α, and IL-1β protein and gene levels in colonic tissues in the DSS-induced ulcerative colitis mouse model, with a significant reversal of this trend after WSGP intervention (p < 0.05).

Fig. 7 Effects of WSGP on the signaling pathways of NF-κB and STAT3 in DSS-induced colitis mice. Different lowercase letters indicate that the data are statistically different (p < 0.05).

In conclusion, in DSS-induced ulcerative colitis mice, WSGP may exercise its anti-inflammatory effects by suppressing the activation of NF-κB and STAT3 signaling pathways, consequently reducing the secretion and gene expression of cellular inflammatory factors.

Effect of WSGP on microbial metabolites in the colonic tract of mice with colitis

Significant research has demonstrated the importance of the intestinal microbiota to growth, development, metabolism, immune function, and health promotion in humans and animals50,51. Dysbiosis of the gut microbiota is associated with many systemic diseases because the gut microbiota and the host have a close symbiotic relationship52,53. For further prevention and treatment of intestinal diseases, it is necessary to investigate factors that affect intestinal flora’s metabolic balance. LC-MS untargeted metabolomics was used to study the changes in intestinal flora of metabolites after WSGP intervention on DSS-induced UC mice54.

We obtained a total of 19,328 qualitative substances by LC-MS untargeted metabolomics techniques. The comparison database already had 1364 names, of which 632 and 732 substances corresponding to cations and anions, respectively, were detected for the next analysis. Considering the overlap of the detected substances for anions and cations, we used the detected substances for cations as the standard for the next analysis. As shown in Fig. 8A,B and 590 of the 632 substances were prevalent for all groups. 2 and 11 characteristic metabolites were found in the DGPL and D groups, respectively. In contrast, N, GP, and DGPH groups had no characteristic metabolites and a high degree of similarity. Combined with the results of PLS-DA analysis, it can be seen that the metabolite structures of the intestinal flora of the mice in the N and GP groups were completely different from those in the D group, and the structural circles did not overlap at all, while the GP and N groups completely overlapped, proving that their metabolite compositions were the same. What’s more, the structure of the intestinal flora metabolites of the mice in the DGPL and DGPH groups was between the N and D groups, and the DGPH group, although not completely separated from the D group, also showed a clear tendency to be similar to the group without the DSS intervention. In summary, WSGP intervention partially reversed the DSS-induced alterations in intestinal flora of metabolites.

Fig. 8 OPLS-DA (A) and Venn (B) diagram of five groups of metabolites. (C) Comparison of metabolites in the first 20 abundances of the five groups. Y-axis indicates metabolite name, X-axis indicates the mean relative abundance of metabolites in different subgroups, and other colored bars indicate different subgroups; p-values are on the far right, *0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001. Differentiated metabolite OPLS-DA score and load plot of N vs. D (D,E), DGPL vs. D (F,G), and DGPH vs. D (H,I).

Comparing the top 20 differential metabolites in abundance among the five groups was further investigated (Fig. 8C). Of these, the differential metabolites PE (16:0/0:0) and cholic acid showed high significance, whereas 15,16-dihydroxyoctadecanoic acid, 3-Methyl-2-cyclopenten-1-one, 3-Methyl-2-cyclohexen-1-one, and 3-Methyl-2-cyclohexen-1-one showed high significance. -3-Methyl-2-cyclohexen-1-one, 2,6-octadienal, 8,10-dodecadienal, 5-bicycloisopropyl [3.1.0], hexan-2-one 5-Isopropylbicyclo [3.1.0] hexan-2-one, 2,6,6-trimethyl-1-cyclohexen-1-acetaldehyde, 2,6,6-Trimethyl-1-cyclohexen-1-acetaldehyde, (1R,4R)-dihydrocarvone and palmitic amide showed significant differences. This suggests that the above metabolites may be involved in the development and progression of UC. In addition, the expression of bile acids was significantly lower in the D group compared to the N group. This trend was reversed after the WSGP intervention, and the metabolism of bile acids was more significantly increased in the DGPH group. Bile acids were also significantly enriched in the GP group compared to the N group, suggesting that the WSGP intervention alone would promote the metabolism of intestinal bile acids. Previous studies have shown that bile acids can improve the development and occurrence of UC by promoting or inhibiting the growth of certain characteristic flora55. The mechanism is that bile acids can promote the expression of anti-inflammatory anti-microbial agents such as iNOS and IL-18 through the farnesoid X receptor (FXR), as well as the expression of cyclic adenosine monophosphate (CAMP) through the activation of TGR5, and suppress the expression of cellular inflammatory factors such as IL-1β and IL-6. It also inhibits the expression of cellular inflammatory factors such as IL-1β and IL-6, thus exerting an anti-inflammatory effect56,57. It has also been shown that the progesterone X receptor (PXR) is activated by bile acids, which inhibit the activation of NF-κB and reduce the expression of inflammatory cytokines, thus protecting against inflammatory bowel disease58,59.

OPLS-DA was a derivative of PLS-DA that could improve model validity and resolution by removing irrelevant differences so that relevant information was concentrated in the first predictor component, thus better distinguishing between groups and differences60.

As shown in Fig. 8D, groups N and D are located in completely different quadrants, with group N in quadrants 2 and 3 and group D in quadrants 1 and 4, with complete separation and no crossover or overlap between the two groups. This suggests that the differential metabolites in Group N and Group D are different. The model parameters were R2= (0, 0.9255), and Q2= (0, 0.3457). Figure 8E shows a plot of N vs. D for the corresponding metabolite loadings, combined with VIP > 2 to filter out the metabolites that had a greater effect on the differences between the two groups, namely Cholic Acid (3.256, C24H40O5), Phosphatidylcholine (10.658, C46H80NO8P) Hydroxyhomodestruxin B (3.288, C31H53N5O8), Ginsenoside MC (2.710, C41H70O12), 2, 4, 6-Triacetylglycitin (5.998, C28H28O13), N, N-(2,2-dihydroxy -ethyl) arachidonoyl amine (8.397, C24H41NO3), 7-Ketodeoxycholic acid (2.776, C24H38O5), N-Acetyl-D-glucosamine (1.052, C8H15NO6).

Figure 8F illustrates the DGPL and D groups are located in completely different quadrant regions, with the DGPL group in quadrants 2 and 3 and the D group in quadrants 1 and 4, with complete separation and no crossover or overlap between the two groups. This suggests that the differential metabolites in the DGPL and D groups are different. The model parameters were R2 = (0,0.9732), Q2 = (0,0.0706). According to Fig. 8G, a plot of the corresponding metabolite loadings for DGPL vs. D. The metabolites that had a greater effect on the differences between the two groups, when combined with VIP > 2, were screened as Phosphatidylcholine (10.658, C46H80NO8P), Hydroxyhomodestruxin B (3.288, C31H53N5O8), and Ginsenoside MC (2.710, C41H70O12), Lucidenic acid D2 (8.353, C29H38O8), Biotripyrrin-b (5.138, C25H27N3O6), and L-Serine (2.577, C3H7NO3).

In Fig. 8H, the DGPH and D groups are located in completely different quadrant areas, with the DGPH group in quadrants 2 and 3 and the D group in quadrants 1 and 4, with complete separation and no crossover or overlap between the two groups. This suggests that the differential metabolites in the DGPH and D groups are different. The model parameters were R2= (0,0.9288), and Q2= (0,0.0978). Figure 8I shows a plot of the corresponding metabolite loadings for DGPH vs. D. The metabolites that had a greater effect on the differences between the two groups were Cholic Acid (3.256, C24H40O5), Phosphatidylcholine (10.658, C46H80NO8P), combined with VIP > 2 screening. Hydroxyhomodestruxin B (3.288, C31H53N5O8), Ginsenoside MC (2.710, C41H70O12), 2, 4, 6-Triacetylglycitin (5.998, C28H28O13), N, N- (2,2-dihydroxy N, N- (2,2-dihydroxy) arachidonoyl amine (8.397, C24H41NO3), N-Acetyl-D-glucosamine (1.052, C8H15NO6). 2.332, C22H28O6), Ceratodictyol B (8.265, C19H38O4), 3-Hydroxy-10-apo-b, y-carotenal (9.762, C27H36O2), L-Serine (2.577, C3H7NO3). The comparison showed that the differential metabolites of N vs. D and DGPH vs. D were essentially the same, suggesting that DSS-induced colitis can cause a metabolic imbalance in the intestinal flora of colitis mice and that intervention to treat WSGP would reverse this imbalance.

Curtis et al. discovered a decrease in beneficial metabolites such as short-chain fatty acids, indole-like metabolites, pantothenic acid and niacin, and arachidonic carnitine in the feces of patients with IBD, and an increase in free arachidonic acid, which is evidence of an inflammatory stimulatory process in the organism61. Further research has shown that indole-like metabolites were significantly reduced in UC mice and that interventional indoles had a beneficial effect on inflammation and barrier function in the gut62. This suggests that indole-like metabolites were immunoprotective against intestinal inflammation. Therefore, the dietary intervention of WSGP can reverse the imbalance of metabolites in the intestinal microbiota induced by DSS-induced colitis in mice through an anti-inflammatory effect. Cholic Acid, Phosphatidylcholine, Ginsenoside MC, 2, 4, 6-Triacetylglycitin, N, N- (2,2-dihydroxy-ethyl) arachidonoyl amine, N-Acetyl-D-glucosamine, and L-Serine were identified in this study as key metabolites closely associated with inflammatory bowel disease.

Conclusions

Our study showed that the dietary intervention WSGP significantly inhibited and ameliorated DSS-induced acute ulcerative colitis in mice. The phenotypic indices were: significant improvement in the reduction of food intake, weight loss, length of the colon, and a significant reduction in DAI score in mice with colitis. The histopathology of the mice with DSS-induced colitis was improved by WSGP, which significantly reduced the histopathology score, while the crypt (Cr), high columnar surface colonic cells (Co), and peripheral muscular layer (M) were more intact and the submucosal layer (Sm) was less edematous. In addition, the WSGP significantly alleviated the reduction in the expression of ZO-1, Occludin, and Caludin-1 tight junction proteins in the DSS-induced colitis mouse model. It was further found that WSGP inhibited the phosphorylation of p65, IκB-α and STAT3 proteins in the NF-κB and STAT3 signaling pathways in the colon of mice with colitis, suppressed the secretion of NO in serum and significantly suppressed the transcription and protein expression of inflammatory cytokine genes. Ultimately, it improved the inflammation of colonic tissue in mice with colitis. Furthermore, WSGP dietary intervention could reverse altered metabolites of the intestinal microbiota caused by DSS-induced colitis in mice, leading to an anti-inflammatory effect. This will provide a theoretical basis for developing and applying WSGP as a clinical drug and functional health food.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

This work was graciously supported by the Office of Talent Work Leading Team in Maoming (MaoRenCaiBan (2020) 24, Project No. 200221115835503), Special funds for the research startup of doctors/post-doctors in Maoming People’s Hospital (No. BS2021012), and the Guangdong Medical Research Fund Project (No. B2023048).

Author contributions

Conceptualization, C.C. and X.S.; methodology, X.S. and C.C.; software, X.S. and J.L.; validation, X.S. and J.L.; formal analysis, X.S. and J.W.; investigation, X.S. and J.L.; resources, C.C. and Q.R.; data curation, X.S and Q.S.; writing—original draft preparation, X.S.; writing—review and editing, X.S., J.L., and Q.S.; visualization, X.S. and X.O.; supervision, C.C. and Y.W.; project administration, C.C. and Y.W.; funding acquisition, C.C. and X.S. All authors have read and agreed to the published version of the manuscript.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Competing interests

The authors declare no competing interests.

Ethics statement

The animal experiments in this study were approved by the Institutional Animal Care and Utilization Professional Committee of Jinan University (ethical approval number: IACUC-20190509-03). All experiments were performed by relevant guidelines and regulations, specifically adhering to the guidelines set forth by Jinan University. Additionally, the authors confirm compliance with the ARRIVE guidelines to ensure rigorous and transparent reporting of the animal studies.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Xin Shao, JiaLong Li and Qi Shao.
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