Abstract
Inflammation is a well-known risk factor for tumor development. Intervening in chronic inflammation may be an effective approach to inhibit the progression of ulcerative colitis (UC) to colitis-associated colon cancer (CAC). Sophora japonica is a medicinal and food plant commonly used to treat hemorrhoids, bleeding, and inflammation, symptoms that coincide with those of patients with CAC. However, studies on using Sophora japonica to treat CAC are scarce. Therefore, this study aimed to investigate the anti-inflammatory and antitumor effects of Sophora japonica extract (SJE) using dextran sodium sulfate (DSS)-induced UC and azoxymethane (AOM)/DSS-induced CAC models. Furthermore, we employed ultraperformance liquid chromatography–quadrupole time-of-flight–tandem mass spectrometry (UPLC–QTOF–MS/MS) technology to explore the metabolomics mechanism through which SJE inhibited the progression of UC to CAC. The results demonstrated that SJE significantly inhibited UC and CAC and alleviated symptoms such as bloody stools, colon shortening, inflammation, and intestinal tissue damage in model mice. Further, 57 differential metabolites were identified by UPLC–QTOF–MS/MS, which were composed mainly of lipids (fatty acids, glycerophospholipids, sterol lipids, pregnenolone lipids, etc.) and peptides (amino acids). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested that SJE might inhibit the progression of UC to CAC by regulating lipid metabolic pathways such as arachidonic acid metabolism, primary bile acid biosynthesis, and linoleic acid metabolism. This study, for the first time, substantiated the inhibitory effect of SJE on CAC. Employing metabolomics techniques, the research delved into the potential pathways through which SJE may exert its inhibitory effects on CAC by suppressing the progression of UC. These findings contribute scientific evidence to the application of Sophora japonica in functional foods and the development of natural drugs for the prevention and treatment of UC and CAC.
1. Introduction
Ulcerative colitis (UC) is a chronic nonspecific inflammatory bowel disease affecting the colonic mucosa. It has been classified as one of the difficult-to-treat illnesses by the World Health Organization due to its long course, persistent symptoms, and high risk of recurrence [1]. Unfortunately, the symptoms of UC during its development are often overlooked, and prolonged UC aggravates the likelihood of numerous complications. One of the most serious and life-threatening complications is colorectal cancer (CRC), and colitis-associated colon cancer (CAC) is the primary cause of colon resection and mortality in this population [2, 3]. Epidemiological studies have reported that patients with UC are two to eight times more likely to develop CAC [4, 5]. Increasing evidence suggests that controlling chronic inflammation and mucosal damage is crucial for developing the disease, and adopting a maintenance therapy approach for chronic UC may serve as a vital strategy for reducing the risk of CAC in patients with UC [6].
According to the clinical manifestations of abdominal pain, diarrhea, and purulent bloody stool, UC belongs to “diarrhea,” “dysentery,” “intestinal wind,” and “hematochezia” in traditional medicine, presenting the pathological changes in terms of dampness and heat in the intestines and the imbalance of qi and blood, with dampness and heat being the main pathological factors, and dampness and heat in the large intestine are the core symptoms [7–9]. Sophora japonica belongs to the large intestine meridian; has efficacy in terms of cooling blood, stopping bleeding, and clearing the liver and cathartic fire and is used to treat blood in stool, hemorrhoidal blood, bloody dysentery, and so forth. Therefore, it is commonly used for treating UC to improve intestinal bleeding and inflammation [10, 11].
Sophora japonica, as a plant with the homology of medicine and food, is often made into dishes and tea in Asia (especially in China) [10, 12]. Modern pharmacological research has shown that Sophora japonica has anti-inflammatory [12, 13], hypoglycemic action [14], antimicrobial [15], antioxidant [16], and treatment effects on hyperuricemia [17], among others. Sophora japonica is rich in chemical components, mainly including flavonoids, saponins, alkaloids, and polysaccharides [10, 17]. By reviewing the literature [10, 18–21] and analyzing the components of Sophora japonica extract (SJE) in the early stage of the study, we found multiple antitumor-active ingredients in Sophora japonica, such as rutin, genistein, sophoridine, matrine, and quercetin. Combined with the symptoms of colorectal cancer-related rectal bleeding, we speculated that Sophora japonica might have a certain preventive and protective effect on inflammation-related cancer through its anti-inflammatory and hemostatic effects, but relevant studies are scarce. Therefore, we established UC and CAC animal models and first demonstrated the inhibitory effect of SJE on CAC. We used metabolomics to investigate the metabolic disturbances induced by the UC and CAC models and to explore the potential mechanisms underlying the inhibition of chronic colitis and related cancers by SJE. We aimed to offer a scientific foundation for developing and using SJE as a medicinal agent for preventing and treating CAC.
2. Materials and Methods
2.1. Animals
Male BALB/c mice weighing 20 ± 2 g and in good health were procured from Liaoning Changsheng Biotechnology Co., Ltd., and maintained under standard laboratory conditions at a temperature of 25°C ± 2°C, relative humidity of 55% ± 5%, and natural light/dark cycle. The mice were acclimatized for 1 week and had free access to food and water prior to experimentation. The certificate of conformity number for the experimental animals was SCXK (Liao) 2020-0001.
2.2. Preparation of SJE
Appropriate amounts of Sophora japonica were pulverized and mixed with 10 times the amount of 50% ethanol. Ultrasonic extraction (at a power of 200 W and a frequency of 40 kHz) was performed for 1 h. The resulting extract was filtered, and the ethanol was recovered until the filtrate was devoid of any alcohol odor. The extract was then diluted with water to the desired concentration for administration, mixed thoroughly, and stored at 4°C until use.
2.3. Experimental Design for UC and CAC
For the UC experiment, the mice were randomly classified into six groups: a control (CON) group, a model (DSS) group, a low-dose SJE (DSS + LSJE, 3.9 g/kg) group, a medium-dose SJE (DSS + MSJE, 11.7 g/kg) group, a high-dose SJE (DSS + HSJE, 35.1 g/kg) group, and mesalazine enteric-coated tablets positive control (DSS + MES, 0.078 g/kg) group (batch number 210112, produced by Khuh Pharm Co., Ltd., Jiamusi, China), with eight mice in each group. The dosages were based on human equivalent doses. Except for the CON group, the mice in all other groups were given 2.5% DSS (batch number: M0510B; Meilunbio Co., Ltd., China) in their drinking water for seven consecutive days to induce colitis, followed by 14 days of drinking regular water. This 21-day cycle of DSS treatment and free access to water was repeated three times. Meanwhile, the oral gavage of the test substances began after the first cycle and continued until the end of the third cycle. Body weight and fecal occult blood were monitored weekly during the experiment.
For the CAC experiment, male BALB/c mice weighing 20 ± 2 g were randomly divided into the control (CON) group, the model (AOM + DSS) group, the low-dose SJE (AOM + DSS + LSJE, 3.9 g/kg) group, the medium-dose SJE (AOM + DSS + MSJE, 11.7 g/kg) group, the high-dose SJE (AOM + DSS + HSJE, 35.1 g/kg) group, and the capecitabine tablet–treated positive control (AOM + DSS + CAPE, 0.13 g/kg) group (batch number: 1B0213DE3; Qilu Pharmaceutical Co., Ltd.), with eight mice per group. Except for the CON group, the mice in the model group were intraperitoneally injected with azoxymethane (AOM) at a dose of 10 mg/kg (purity 95%, batch number: A1908142; Aladdin) and given 2.5% DSS in drinking water for 7 days after 1 week, followed by free access to distilled water for 14 days. The aforementioned 21-day cycle of DSS and distilled water was repeated three times. Meanwhile, the mice were orally administered the test substances starting from the end of the first cycle until the end of the third cycle. Body weight was monitored weekly throughout the experiment.
2.4. Collection of Tissues and Blood Samples
At the end of the experiment, the mice were fasted for 24 h prior to collecting blood and tissue samples. Blood was allowed to clot for 15 min, followed by centrifugation at 2500 rpm for 10 min to obtain serum, which was then stored at −80°C until further use. The colon length was measured, and the tissue was washed and weighed. A 2-cm segment of the distal colon near the anus was cut and fixed in 4% paraformaldehyde for pathological sectioning. The spleen and thymus were collected and weighed for immunological analysis.
2.5. Histological Analysis
The colon tissues were processed for pathological analysis. Specifically, they were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned into 5-μm-thick slices. The evaluation of the pathological status of UC (see Table S1 for scoring criteria) and CRC (see Table S2 for scoring criteria) was conducted through hematoxylin and eosin (H&E) staining, which was performed in a blinded manner by trained personnel. The images were captured using a bright-field microscope (Eclipse Ci-L; Nikon), scanning software (3DHISTECH, CaseViewer2.4), and a panoramic slide scanner (3DHISTECH, PANNORAMIC DESK/MIDI/250/1000), and the analysis was performed by Wuhan Cever Biotechnology Co., Ltd.
2.6. Inflammatory Cytokine Detection
The enzyme-linked immunosorbent assay (ELISA) kits from Shanghai Kexing Biotech Co., Ltd., were used to measure the levels of tumor necrosis factor (TNF)-α (lot number: 21071230N), interleukin (IL)-6 (lot number: 21071233N), and IL-1β (lot number: 20171235N) in serum following the manufacturer’s protocols.
2.7. Serum Metabolomics Analysis
The serum samples (100 μL) were prepared by adding 300 μL of 0.1% formic acid–methanol and vortexing for 3 min. The mixture was then sonicated for 5 min and left overnight at 4°C. The next day, the mixture was centrifuged at 12,000 rpm for 10 min, and the supernatant (100 μL) was transferred to a new tube and dried using vacuum centrifugation. The residue was dissolved in 50 μL of 70% methanol and vortexed for 3 min before sonication for 5 min. After centrifugation at 12,000 rpm for 10 min, the supernatant was collected for UPLC-QTOF-MS analysis. A quality control (QC) sample was prepared by pooling 10 μL of the supernatant from each sample.
The UPLC-QTOF-MS system (Agilent 1290 UPLC coupled with an Agilent G6550 Q-TOF-MS) was used for separation using a poroshell SB-C18 120 chromatographic column (100 × 2.1 mm2, 2.7 μm). The positive ion-mode mobile phase consisted of 0.1% formic acid water (A) and methanol (B), whereas the negative ion mode mobile phase consisted of 0.1% formic acid water (A) and acetonitrile (B). The optimized gradient for positive ion mode was 0–6 min, 95%–35% A; 6–13 min, 35%–20% A; 13–27 min, 13%–8%A; and 27–30 min, 8%–0% A. The optimized gradient for negative ion mode was 0–30 min, 95%–0% A. The flow rate was set at 0.4 mL/min, and the column temperature was maintained at 30°C. Electro spray ionization (ESI) was used to collect data in both positive and negative ion modes. The mass range was 100–1000 m/z, and the collision voltage was set at 125 V.
The mass spectrometry data were processed using Agilent MassHunter Profinder 08.00 software, which involved peak detection, peak alignment, and normalization. Compound data files were extracted, and data with an RSD >30% in the QC sample were removed. Differential metabolites were screened using MetaboAnalyst 5.0 software with partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) based on Variable Importance in Projection (VIP) > 1, , FC > 1.5, or FC < 0.67. Short time-series expression miner (STEM) was used to identify metabolites that reverted to the CON group. Furthermore, the fragmentation patterns of the metabolites were compared with databases such as Metlin and The Human Metabolome Database (HMDB), as well as standard compounds, for identification. Pathway enrichment analysis and visualization were conducted using MetaboAnalyst 5.0.
2.8. Statistical Analysis
The data were expressed as mean ± standard deviation (SD). Statistical analysis and data visualization were performed using GraphPad Prism 8.0. The unpaired-samples Student’s t-test was employed to compare the differences between the two groups, while one-way analysis of variance was used for multiple group comparisons. The threshold for statistical significance was set at .
3. Results
3.1. SJE Suppressed DSS-Induced UC Development
The body weight and occult blood of mice were monitored and recorded weekly during the experiment to evaluate the protective effects of SJE on UC. No abnormal symptoms were observed in the CON group throughout the experiment. The mice in the DSS group showed fluctuations in body weight from the third week and exhibited symptoms such as diarrhea and positive occult blood during the DSS intervention period (Figures 1(a) and 1(b)). The aforementioned symptoms in mice improved in the SJE groups compared with the DSS group. The colon of mice in the DSS group was significantly shortened, the tissue was thickened, the elasticity reduced, and brittleness increased on hand touch (Figures 1(c) and 1(d)). Additionally, the colon weight-to-length ratio increased (Figure 1(e)), and the spleen and thymus indexes also increased (Figures 1(f) and 1(g)). However, these symptoms were significantly alleviated in the SJE groups. ELISA results showed that SJE could regulate the significant increase in the serum levels of inflammatory factors TNF-α, IL-6, and IL-1β induced by DSS intervention (Figures 1(h)–1(j)). The histological examination showed that DSS intervention caused pathological changes in the colon tissue, including mucosal layer necrosis, reduced number of intestinal glands, increased connective tissue hyperplasia, and moderate infiltration of lymphocytes and neutrophils. In some samples, ulcers, epithelial cell shedding, and infiltration of inflammatory cells into the submucosal layer were also observed. Treatment with different concentrations of SJE significantly reduced colon tissue damage (Figures 1(k) and 1(l)).
3.2. SJE Inhibited AOM/DSS-Induced CAC Development
We monitored and recorded the body weight and fecal blood of mice every week during the experiment to evaluate the protective effect of SJE on CAC. The AOM + DSS model mice exhibited significant diarrhea, bloody stools, and even prolapse of the anus in the ninth week (Figures 2(a) and 2(b)). Colon tissue examination of the AOM + DSS group mice showed that the colon length was significantly shortened and edema was apparent compared with the CON group, and multiple adenomas and bleeding sites in the colon tissue were observed. However, the colon length of mice in each concentration of the SJE group was significantly larger than that of the AOM + DSS group, and no obvious edema or bleeding phenomenon was noted (Figures 2(c) and 2(d)). At the same time, the colon weight-to-length ratio in the AOM + DSS group mice increased (Figure 2(e)), and the spleen and thymus indexes also increased (Figures 2(f) and 2(g)). The various concentrations of SJE alleviated the aforementioned symptoms. The CON group did not show obvious adenomas in the colon, while the adenoma formation rate in the AOM + DSS group was 100%, with an average number of colon adenomas per mouse of 19 ± 5.13. The high-dose SJE group had significantly fewer adenomas with smaller diameters, while the low- and medium-dose groups had significantly better adenoma numbers than the AOM + DSS group, although the diameters were not significantly reduced (Figures 2(h) and 2(i)). Additionally, the histological examination showed that SJE treatment significantly reduced the characteristic pathological symptoms caused by AOM + DSS, including large areas of tissue carcinogenesis, glandular hyperplasia, increased nuclear–cytoplasmic ratio, nuclear division, tumor cell necrosis, lymphocyte infiltration, connective tissue proliferation, and epithelial cell shedding (Figures 2(j) and 2(k)). These results suggested that SJE had a good preventive and inhibitory effect on tumor occurrence in the mouse CAC model.
3.3. QC of Metabolomics Analysis
We analyzed the serum metabolic profiles of UC and CAC experimental groups based on untargeted metabolomics analysis, and the representative total ion chromatograms (TICs) obtained from the serum samples are shown in Figure 3. We used QC samples in both positive and negative ion modes to evaluate the stability and reliability of the experimental method. The PLS-DA model was used to evaluate the samples. The QC samples, CON group, model group, and treatment group were significantly clustered, and every experimental group could be clearly distinguished. This indicated that the experimental method was stable and the data quality was high (Figure S1). After RSD screening of the QC samples, 1259 (negative ion mode) and 4539 (positive ion mode) characteristic metabolites were left in the UC experimental serum samples, and 1776 (negative ion mode) and 4089 (positive ion mode) characteristic metabolites were left in the CAC experimental serum samples. The metabolites identified in both positive and negative ion modes were combined for further analysis.
(a)
(b)
(c)
(d)
3.4. Metabolic Profiling of UC and CAC Mouse Serum
We compared the serum metabolomes of the CON and DSS groups, and CON and AOM + DSS groups in UC and CAC experiments, respectively, to observe the metabolic changes induced by modeling. OPLS-DA models showed a clear separation of the model groups from the CON groups, indicating significant metabolic disturbances caused by DSS-induced UC and AOM/DSS-induced CAC (Figures 4(a) and 4(d)). = 0.925, = 0.729 for the UC experiment and = 0.981, = 0.834 for the CAC experiment (Figures 4(b) and 4(e)), indicating that the models had good fitting and predictive abilities and could be used for differential marker screening. Based on VIP >1, , FC > 1.5, or FC < 0.67 as screening criterion, we identified 281 and 240 significantly different metabolites for UC and CAC experiments, respectively, by matching the METLIN and HMDB databases (Figures 4(c) and 4(f)). We submitted 281 and 240 significantly different metabolites in serum to MetaboAnalyst 5.0 for the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to explore the abnormal changes in the metabolic pathways of UC and CAC. The top 20 enriched abnormal metabolic pathways in UC and CAC models mainly included lipid metabolism, amino acid metabolism, cofactors and vitamin metabolism, and nucleotide metabolism, among others (Figures 4(g) and 4(h)). Specifically, the biosynthesis of phenylalanine, tyrosine, and tryptophan; purine metabolism, arachidonic acid (AA) metabolism, phenylalanine metabolism, taurine and hypotaurine metabolism, and linoleic acid (LA) metabolism; and primary bile acid (BA) biosynthesis were significantly enriched in UC and might play important roles in UC development. These pathways were also significantly enriched in CAC, suggesting that they might play important roles in the progression from UC to CAC. Further, glycerophospholipid metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and alpha-linolenic acid metabolism might also play roles in CAC development.
3.5. Effects of SJE on Metabolic Profiles in Mice with UC and CAC
The serum metabolic profiles of mice in the DSS and DSS + SJE groups and the AOM + DSS and AOM + DSS + SJE groups were compared to investigate the regulatory effects of SJE on metabolic disorders in the models. OPLS-DA models showed that the model groups of UC and CAC were significantly separated from the SJE group, indicating that SJE could regulate the metabolic disorder induced by UC and CAC (Figures 5(a) and 5(b)). STEM analysis was conducted on CON, DSS, and DSS + SJE groups, as well as CON, AOM + DSS, and AOM + DSS + SJE groups to identify the differential metabolites regulated by SJE. Metabolites with a trend of returning to the CON group were identified in profiles 1, 5, 6, 9, 10, and 14 (Figures 5(c) and 5(d)), and the fraction of them that were identical to the significantly different metabolites of UC and CAC might be essential for SJE to inhibit the development of UC and the progression of UC to CAC. UPLC–QTOF–MS/MS was then used to identify the fragments of the aforementioned metabolites and compared them with standard substances and databases, identifying 57 differential metabolites (Table 1). These metabolites were mainly concentrated in lipids (fatty acids, glycerophospholipids, sterol lipids, pregnenolone, ketosterol lipids, etc.) and peptides (amino acids) (Figure 5(e)). Metabolic pathways were mainly enriched in lipid metabolism pathways such as AA metabolism, primary BA biosynthesis, and LA metabolism, as well as amino acid metabolism pathways (Figure 5(f)). Therefore, we established a metabolic network based on lipid metabolism pathways to reveal the regulatory mechanisms of these metabolites on UC and CAC and their interactions (Figure 5(g)). Red metabolites represented upregulated metabolites after SJE intervention, whereas blue metabolites represented downregulated metabolites. These metabolites might be critical pathways for the inhibitory effects of SJE on the UC-to-CAC transition.
4. Discussion
It has been confirmed that chronic inflammation contributes to the development and progression of CAC, and cytokines produced by inflammatory and intestinal epithelial cells are among the mechanisms driving carcinogenesis. Cytokine stimulation leads to complex interactions between intestinal epithelial cells and immune system cells in the gut, disrupting tolerance to the gut microbiota and disturbing the balance between pro-inflammatory and anti-inflammatory signals, resulting in changes in cellular behavior and the onset of cancer [3, 22]. Therefore, a treatment strategy that can effectively control chronic inflammation may be an effective form of prevention and treatment for CAC.
DSS-induced mice with colitis and AOM/DSS-induced mice with colon cancer exhibit symptoms similar to those of patients with UC and CAC, respectively, including weight loss, diarrhea, rectal bleeding, colonic shortening, inflammatory responses, and intestinal tissue damage [23]. Thus, DSS and AOM/DSS are widely used for establishing mouse colitis and colon cancer models. Traditional Chinese medicine holds that Sophora japonica can cool the blood and stop bleeding. In pharmacological experiments, it can be seen that SJE has good controlling effects on occult blood in UC and bleeding symptoms in CAC, and it alleviates the pathological symptoms such as weight loss, diarrhea, colon shortening, and inflammation caused by UC and CAC models to varying degrees. Moreover, SJE significantly controls the number of colon adenomas and tissue carcinogenesis caused by CAC, indicating that SJE has protective effects on CAC.
In the metabolomic analysis of UC and CAC, lipid metabolism, amino acid metabolism, nucleotide metabolism, and translation showed significant metabolic disorders. SJE effectively regulated the aforementioned metabolic disorders. We further characterized the metabolites by UPLC-QTOF-MS/MS to improve the accuracy. We found that 60% of the 57 identified differential metabolites were lipids, which were significantly enriched in lipid metabolism pathways such as AA metabolism, LA metabolism, BA metabolism, and so forth. Therefore, we speculated that regulating lipid metabolism by SJE might be the key pathway to inhibit UC and CAC. Significant perturbations in metabolic pathways related to fatty acid biosynthesis, detectable in serum several years prior to diagnosis, have been detected in patients with UC, and perturbations in the metabolism of serum lipids and sphingolipids have been found in newly diagnosed patients with UC [24]. Andrew Gold et al. analyzed 37 studies characterizing the metabolomics of CRC reported between January 2012 and July 2021 and found that a large number of metabolites were differentially regulated in patients with CRC, with dysregulation of metabolic biomarkers such as certain amino acids, fatty acids, and lysophosphatidylcholines (LPCs) being particularly pronounced [25]. This was in line with our findings. The abnormal metabolism of lipids is an essential phenotype of cancer cells, including CAC [26, 27]. Cancer cells activate aberrant lipid metabolism by increasing the uptake of exogenous lipids or endogenous lipid generation to meet the needs of continuous proliferation, invasion, and metastasis, as well as to obtain energy and respond to the impact of the tumor microenvironment [28]. Additionally, the cross-regulation between lipid metabolism and pro-cancer signaling pathways also promotes the growth and metastasis of cancer cells. The metabolomic analysis of UC and CAC showed that lipid metabolism, represented by AA metabolism, LA metabolism, and primary BA biosynthesis, showed significant disorders. Previous studies also found that these metabolic pathways played substantial roles in developing inflammation and cancer.
AA is metabolized by the cyclooxygenase (COX) pathway to produce prostaglandins (PGs), which act as critical inflammatory mediators in the inflammatory process, such as vasodilation, increased permeability of capillary walls, and enhanced pain and tissue swelling effects of histamine and kinins [29, 30]. Inhibiting the generation and function of PGs can effectively inhibit inflammation. Additionally, AA is metabolized by the lipoxygenase pathway to produce many hydroperoxy fatty acids, including 5-hydroxyeicosatetraenoic acid (5-HPETE) and 12-hydroxyeicosatetraenoic acid (12-HPETE), which indirectly activate COX pathway metabolism. 5-HPETE further converts into leukotrienes, which can enhance vascular permeability, leading to edema at the site of inflammation [30]. 12-HPETE is catabolized to 12-hydroxyeicosatetraenoic acid (12-HETE), which is involved in increased inflammation and platelet activation [31]. Several preclinical in vitro studies, including on nonsteroidal anti-inflammatory drugs such as aspirin, have shown that the inhibition of COX-2 can suppress the progression of adenomas to adenocarcinomas [32]. Epidemiological and clinical trial studies also support the effectiveness of this class of anti-inflammatory drugs in reducing the risk of CRC. Therefore, it is feasible to control inflammation and cancer development by regulating the AA metabolic pathway. Our study found that SJE alleviated inflammation symptoms and intestinal tissue edema in the UC and CAC models. SJE had a regulatory effect on AA, prostaglandin E2, leukotriene B4, and 12-HETE, suggesting that SJE might inhibit the development of UC to CAC by regulating AA metabolism.
BAs are the final products of cholesterol breakdown and are primarily regulated by farnesoid X receptor (FXR) and G protein-coupled BA receptor 1 (GPBAR1, TGR5) to modulate their various functions [33]. The activation of intestinal FXR can reduce inflammation and immune cell infiltration and decrease intestinal epithelial permeability. Inflammation disrupts the metabolic balance between gut microbiota and BA, resulting in insufficient secondary BA production such as deoxycholic acid (DCA) and lithocholic acid, leading to reduced FXR activity and ineffective control of intestinal inflammation, which accelerates inflammation progression and carcinogenesis. However, excessive secondary BAs, especially DCA, can cause DNA damage by producing reactive oxygen species, promoting cell proliferation, reducing apoptosis and differentiation, and promoting the development of CRC [34]. Additionally, conjugated BA such as taurocholic acid (TCA) and glycocholic acid can have pro-inflammatory and anti-inflammatory effects by activating M1 and M2 macrophage receptor TGR5, respectively. Dysbiosis can disrupt conjugated BA and alter the pro-inflammatory and anti-inflammatory activities mediated by TGR5, causing intestinal damage. Metabolomics analysis results showed that SJE affected BA metabolism by modulating cholic acid, TCA, and DCA and was also one of the pathways that inhibited UC-to-CAC development.
Omega-3 polyunsaturated fatty acids such as alpha-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid (DHA) can usually reduce the inflammatory process and lower the risk of cancer [35]. The human body is unable to synthesize alpha-linolenic acid endogenously, and the conversion rate of DHA is extremely low; supplementation with stearic acid can improve the conversion rate of DHA [36]. Our previous analysis of the composition of SJE and literature research [37] found that SJE contained alpha-linolenic acid and stearic acid. It was hypothesized that SJE might be used as an omega-3 supplement to regulate lipid metabolism and play a role in preventing inflammation and cancer. SJE also contains LA, which is a synthetic precursor of AA and can inhibit inflammation by reducing the production of inflammatory factors such as TNF-α and IL-1 [38]. Recent studies have shown that LA is a major positive regulator of CD8+ T-cell function, improves CD8+ T-cell function and memory differentiation through mitochondrial reprogramming, prevents CD8+ T-cell exhaustion, and enhances antitumor responses [39]. Moreover, the UC and CAC models caused abnormal levels of LPCs closely related to inflammation metabolism, mainly involving glycerophospholipid metabolism. Glycerophospholipids are the major lipid components of cell membranes, and alterations in glycerophospholipid metabolic pathways affect various cellular functions, such as membrane fusion and vesicular transport [40]. High concentrations of LPCs can impair intestinal barrier function and increase gastrointestinal permeability [41]. The levels of LPCs in mice tended to return to normal after the administration of SJE. Maoqing Wang et al. [42] identified five biomarkers of CRC, including LysoPC (14 : 0), LysoPC (16 : 0), LysoPC (18 : 0), LA, and tryptophan, through metabolomics studies. In this study, we found that LysoPC (16 : 0), LysoPC (18 : 0), LA, and tryptophan were all significantly regulated by SJE, providing supporting evidence for the effectiveness of SJE.
5. Conclusions
This study was novel in integrating validation experiments and metabolomics data to explore the protective effect of SJE on CAC. Combining UC experiments, we discussed the potential pathways for SJE to inhibit UC-to-CAC development at the organismal metabolomics level. SJE exhibited good activity in reversing DSS-induced UC and AOM/DSS-induced CAC, significantly alleviated symptoms such as blood in the stool, colon shortening, inflammatory response, and intestinal tissue damage in the model mice and regulated 57 differential metabolites common to both UC and CAC, which might inhibit the progression of UC to CAC by regulating lipid metabolism. Although dietary intake in daily life is not a substitute for medication, nutritional intervention is also a good way to alleviate symptoms and maintain intestinal health. This study may contribute to the development and rational application of functional foods from Sophora japonica, as well as to the prevention and treatment of UC and CAC.
Data Availability
All data used to support the findings of this study are presented in this paper. However, additional information can be obtained from corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Authors’ Contributions
Funding acquisition and study design were done by Xiansheng Meng; study design, data processing, and writing the original draft were done by Ying Zheng; pharmacodynamic experiments were carried out by Tianjiao Li, Hao Yu, and Li-ying Han; index assays were done by Xi Luo and Mengnan Jia; reviewing and editing were done by Shuai Wang and Yongrui Bao. All authors have read and agreed to the published version of the manuscript.
Acknowledgments
This research was funded by grants from the Key R&D Projects in Liaoning Province (2020JH2/10300088) and the Basic Research Project of Liaoning Provincial Department of Education (JYTQN2023474).
Supplementary Materials
Figure S1: PLS-DA analysis of all samples. PLS-DA plots of all samples from UC experiments in negative ion mode (A) and positive ion mode (B). PLS-DA plots of all samples from CAC experiments in negative ion mode (C) and positive ion mode (D). Table S1: Scoring criteria for pathological states of UC; Table S2: Scoring criteria for pathological states of CAC. (Supplementary Materials)