2.1 Materials and regents

Fructus mume (FM; College of Pharmacy, Anhui University of Chinese Medicine, AnHui, China), reference standards of benzoic acid, quercitrin, caffeic acid, fumaric acid, chlorogenic acid, protocatechuic acid, succinic acid, quinic acid, rutin, citrate, malic acid, kaempferol, ursolic acid, amygdalin, apigenin and 5-hydroxymethyl furfural were purchased from Shanghai Yuanye Biotechnology Co., Ltd(Shanghai, China). The purity of all standard ingredients was ≥ 98 %. Mesalazine (Losan Pharma GmbH Germany, Shanghai Sanwei Pharmaceutical Company, Shanghai, China), RAGE inhibitor (FPS-ZM1; Shanghai Yuanye Co., Ltd), Dextrose Sodium Sulfate (DSS; MP Company, Canada), TNF-α, IL-1β, ROS, and NADPH ELISA kits (Beijing Sizhengbai Biotechnology, Beijing, China), BCA protein quantitative kit (Thermo Fisher, USA), p38, AGEs, NF-kB, p-p38, RAGE, p-NF-kB antibody (Beijing Zhongshan Golden Bridge Company, Beijing, China), and 4 % paraformaldehyde fixative (Tianjin Solomon Biotechnology Company, Tianjin, Chian) were used for experiments in this study.

2.2 Preparation of the control sample

The appropriate amount of Benzoic acid, quercetin, caffeic acid, fumaric acid, chlorogenic acid, protocatechuic acid, succinic acid, quinic acid, rutin, citric acid, malic acid, kaempferol, ursolic acid, amygdalin, Apigenin, 5-hydroxymethyl furfural was accurately weighed using a 1/100,000 precision analytical balance (Sartorius Group, Germany) in turn. Place the samples in 25 mL volumetric flasks, add ultrapure water to prepare the controls at 0.12, 0.11, 1.21, 2.64, 1.30, 1.31, 2.40, 2.69, 1.41, 12.31, 6.12, 0.41, 2.01, 0.50, 2.58, and 0.98 mg/mL.

2.3 Preparation of FM test samples

In total, 12 batches of FM samples were collected from the regions of Sichuan and Fujian. The sample powder (1 g) was precisely weighed, placed in a 50 mL centrifuge tube, 25 mL of 80 % methanol added, and weighed. After ultrasonication for 30 min, the sample was allowed to cool, centrifuged at 12000r for 15 min, and passed through a 0.22 μm filter membrane to obtain the filtrate.

2.4 LC-MS analysis

Samples were analyzed using liquid chromatography-mass spectrometry (LC-MS) with a QUAD-4500 triple quadrupole linear ion trap mass spectrometry system (AB SCIEX, USA). The separation was performed using an Agilent ZORBAX SB-C18 column (3.0 mm × 100 mm, 3.5 μm, Agilent, USA). The column temperature was maintained at 30 °C and flow rate at 0.2 mL/min, with 0.2 % formic acid in water as mobile phase A and 0.2 % formic acid in acetonitrile as mobile phase B.

The mobile phase comprised 0.2 % formic acid–water (A) and 0.2 % acid-acetonitrile (B). A gradient elution procedure was used: 0–3 min, 10–30 % A; 3–7 min, 30–50 % A; 7–10 min, 50–80 % A; 10–14.5 min, 80–10 % A; 14.5–17 min, 10 % A. The injection volume was 1 µL and mass spectrometry detection mode was multi-reaction monitoring (MRM). The conditions included a spray voltage of 5.5 kV and an ionized temperature of 550℃. Nitrogen was used for Curtain Gas, Atomized Gas (Gas1), and Auxiliary Gas (Gas2), with pressures set at 35, 55, and 55 psi, respectively. A total of 16 components were collected using Analyst software (version 1.6). The chemical structures of the main compounds of FM were generated with ChemDraw from CambridgeSoft, which allowed for accurate mass analyses for LC/MS (electrospray), including adducts and protonated molecules.

2.5 Screening of active constituents of FM

Drug-likeness (Xu et al., 2021) refers to the possibility of chemical components becoming a drug while Lipinski's rules summarize the drug-like properties produced upon oral administration and provide a classic method for analyzing the drug-like nature of compounds. The specific screening rules for active compounds are as follows: relative molecular weight (MW) ≤ 500, hydrogen bond donors (the number of hydrogen atoms connected to O and N, nOHNH) ≤ 5, hydrogen bond receptor (the number of O and N, nON) ≤ 10, and lipid water partition coefficient (miLog P) ≤ 4.15. In cases where the above conditions are met, when these ingredients are administered orally, they have better availability in the process of biological transformation. Since FM is usually administered orally, Linpinski rules were applied to analyze the drug-like properties of the chemical components of FM and identify active ingredients.

2.6 Establishment of a SDF format file for active ingredients of FM

The molecular 2D structure of the active compound was mapped with PubChem (https: //pubchem.ncbi.nlm. nih.gov/) database and saved in the SDF format.

2.7 Prediction of potential targets for active ingredients of FM

Swiss Target Prediction (https://www.swisstargetprediction.ch/) is a web server used to accurately predict the targets of bioactive molecules based on two- and three-dimensional similarities of known ligands, which provides valuable insights into their mechanisms of action (Gfeller et al., 2014). The SDF format files of active ingredients identified via LC-MS were uploaded to the Swiss Target Prediction server and “Homo sapiens” selected as the species. The target prediction results were sorted from high to low according to “Probability ＞0.2” and the official names of drug targets retrieved through the UniProtKB search function in the UniProt database (https://www.uniprot.org/).

2.8 Mapping of UC-related targets for active ingredients of FM

Genes potentially related to UC were searched using the keyword “Ulcerative colitis” in the GeneCards database. Comparison of the data with the targets obtained from Swiss Target Prediction server led to the identification of targets of the active ingredients in FM potentially involved in pathways affecting UC.

2.9 Construction of protein-protein and component-target networks

Targets of the active ingredients and UC were imported into the String database.

(https://string-db.org/) and the corresponding protein–protein interaction network diagrams generated. We selected 'Homo sapiens', set the interaction score to 0.700, hid the unconnected nodes, and obtained protein interaction diagrams. We used Cytoscape 3.7.1 software to combine the active ingredient targets in FM with the disease targets of UC, generated a network analysis topology map, exported the data, and calculated the median and maximum values of its degree. The median and maximum values were subsequently inputted into Cytoscape 3.7.1 software for screening and the core targets for treatment of UC obtained. The significance of each node in the network was determined by the degree of topological parameters. The degree of a node refers to the number of edges connected to that node (the higher the degree, the more nodes directly connected, and the greater importance of the node in the network). The edges represent the interactions between compounds and targets in the network.

2.10 Functional enrichment analysis of potential action targets

The Bioscape Annotation Database DAVID (https://david. ncifcrf.gov/summary. jsp) is a reliable, effective, and intuitive online bioinformatics annotation tool for understanding the biological functions of genes and protein lists on a large scale for biomedical researchers. Gene ontology (GO) enrichment and KEGG pathway annotation analyses of basic ontology terms were performed using DAVID for potential targets of FM, and P < 0.05 considered statistically significant.

2.11 UC animal experiments

2.12 Statistical analysis

Each experiment was repeated at least three times. All data were statistically analyzed using SPSS 22.0 software and expressed as mean ± standard error ( $\overline{X}$  ± SEM). Data were considered statistically significant at P < 0.05.

3.1 Optimization of sample preparation

Ultrasonic extraction of FM is reported to be more effective than refluxing (Li et al., 2020). To determine the best extraction conditions, based on previous findings (Ou et al., 2020), three factors were selected and the properties of each component optimized. The effects of extraction solvent (water, 50 % methanol, 80 % methanol), ratio of solvent to sample (15:1, 25:1 and 35:1) and ultrasonic time (20, 30 and 40 min) on extraction efficiency were examined. Our results showed optimal extraction efficiency at a concentration of 80 % methanol, solvent to sample ratio of 25:1 and ultrasonic time of 30 min.

3.2 Optimization of MS and LC conditions

Various chromatographic conditions, such as column type, mobile phase composition and pH, were examined in preliminary experiments to obtain the ideal chromatographic profile and optimize retention time and peak shape. Two column types were compared, specifically, BEH C18 (100 mm × 2.1 mm, 1.7 μm) and ZORBAX SB-C18 (3.0 mm × 100 mm, 3.5 μm), to optimize retention time of the target analyte. The results showed that under the same conditions, the ZORBAX SB-C18 column had strong retention capacity, good resolution, and short analysis time for all analytes. Acetonitrile, a polar aprotic solvent with better elution capacity, separation selectivity and peak shape than methanol, was selected as the organic solvent for the mobile phase. According to earlier studies, mixed solution containing formic acid as a mobile phase additive could increase sensitivity and improve the peak shape of the measured components. Therefore, acetonitrile-0.1 % formic acid water, 0.1 % acetonitrile-0.1 % formic acid water and 0.2 % acetonitrile-0.2 % formic acid water mixtures were compared in this study. Notably, the 0.2 % acetonitrile-0.2 % formic acid water combination produced the optimal peak in the shortest time.

Full-scan MS was conducted to assess the target compounds in positive and negative modes for determining optimal MS conditions. The sensitivity and intensity of ursolic acid, amygdalin, apigenin and 5-hydroxymethylfurfural standards in the positive mode were higher than those in the negative mode but better for the other standards in the negative mode. The MRM chromatograms of 16 standards are depicted in Fig. S1(A).

3.3 Identification of active ingredients in FM

Mass spectrometry information on materials obtained from the experiment was compared with related literature reports. Consequently, 16 compounds were identified in FM, of which were validated based on reference standards. The results are summarized in Table S1. Mass spectra and structures of each compound are shown in Fig. S1(B).

3.4 Content quantification of the 16 main active ingredients of FM

We obtained 1 μL standard and mixed standard stock solutions of different concentrations, which were subjected to stepwise dilution. Experiments were performed in parallel three times. A standard curve with mass concentration X (μg/mL) as the abscissa and peak area (y) as the ordinate is presented in Table S1. Each sample solution was assessed, the 16 compounds quantified based on the corresponding standard curves (Table S1), and changes in quantity evaluated (Table S2). The compounds assessed included 10 organic acids, 4 flavonoids, amygdalin and 5-hydroxymethylfurfural. We observed a number of differences in composition and organic acid contents among the samples. The citric acid content was between 13.28 % and 16.85 %, which was higher than the pharmacopoeia standard (content of no less than 12 %). Malic acid, fumaric acid, quinic acid and succinic acid levels were the second highest while those of protocatechuic acid and benzoic acid were much lower. Among the flavonoids, the apigenin content was the highest while that of quercitrin was the lowest. The contents of amygdalin and 5-hydroxymethylfurfural were 1.59 % and 1.60 %, respectively.

3.5 Determination of the potential main active ingredients of FM

The 16 components determined via LC-MS/MS were screened for classification as active ingredients based on the Lipinski rules. The results are listed in Table 1.

3.6 Screening the targets of Anti-UC active components of FM

The 2D.SDF structures of the 11 compounds mentioned above were queried using the PubChem database and imported into the SwissTarget Prediction platform, from which 368 corresponding targets were obtained. A total of 1977 UC-related targets were obtained by querying GeneCards. The selected drug and disease targets were inputted into the Venn diagram production software Venny 2.1. Overall, 151 common targets were mapped, as shown in Fig. 2A.

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Fig. 2

Construction of FM active ingredient—target gene network in UC and screening of key molecules. (A) Screening of targets for the main active components of FM on UC. 217 Predicted targets (in the pink circle) were mapped to 1826 UC-related targets (in the blue circle).The intersection of these targets resulted in a total of 151 potential targets for treatment of UC. (B) PPI network of related targets of FM in the treatment of UC. (C)PPI network of core targets of FM in the treatment of UC. (D) “Active ingredient-target” network diagram of FM.

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3.7 PPI network construction and analysis

The common targets of drugs and diseases were imported into the String database (https://string-db.org/cgi/input.pl) to construct the PPI network (Fig. 2B). Customized interaction network confidence was 0.990 and data were exported using Cytoscape3.7.1 software. The NetworkAnalyzer tool was employed to perform topology analysis and a core PPI network diagram generated with the two parameters of degree and betweenness centrality as reference standards (Fig. 2C). The results support the involvement of key proteins, such as signal transducer and activator of transcription 3 (STAT3), matrix metalloproteinase 9 (MMP9) and serine/threonine protein kinase 1 (protein kinase Bα, AKT1), in FM activity against UC. STAT3 plays an important role in UC as it is involved in regulating inflammatory responses. Studies have shown that the expression levels of STAT3 in the intestinal tissues of patients with UC are significantly increased. Therefore, inhibiting the activity of STAT3 may help alleviate the symptoms of UC. Additionally, MMP9 is also involved in the development of UC. Research has indicated that the expression levels of MMP9 in the intestinal tissues of UC patients are significantly increased. MMP9 can degrade the matrix molecules in intestinal tissues, leading to tissue damage and ulcer formation.

3.8 Construction of a component–target network

An active component–target network of FM for UC was constructed using Cytoscape 3.7.1 software. Network topology analysis showed that the degree and betweenness centrality of caffeic acid were the highest (degree = 57, betweenness centrality = 0.36787426). The degree and betweenness centrality of CA1, a target of FM components, also ranked among the top (degree = 10, betweenness centrality = 0.07807696）; Fig. 2D. Our results showed that multiple targets were associated with multiple components, suggesting that different components of FM exert synergistic effects leading to greater therapeutic efficacy.

3.9 GO enrichment and KEGG pathway enrichment analysis

GO enrichment analysis is subdivided into three ontologies: 1. Biological Process (BP), 2. Molecular Function (MF), and 3. Cellular Component (CC). Using DAVID database, GO enrichment analysis was conducted on 151 potential targets of FM against UC, with statistical significance set at P < 0.05. A total of 206 biological processes or pathways were obtained, among which 80 were related to BP, 76 to MF, and 50 to CC. According to P values sorted from small to large, the top 10 were selected and R language used to generate a high-level bubble chart, as shown in Fig. 3A. The bubble represents the number of genes enriched in GO entry, i.e., the larger the bubble, the greater number of genes enriched. The targets mainly involved response to drug, plasma membrane, and enzyme binding.The common targets were subjected to KEGG pathway enrichment analysis and screened according to P value ≤ 0.05. Overall, 97 signaling pathways were enriched. R 3.6.3 was used and the clusterProfiler package installed and quoted. The first 20 channels were selected to generate a histogram (Fig. 3B), including Pathways in cancer, Small cell lung cancer, Non-small cell lung cancer, etc. Among these, AGEs and their receptors (RAGE) exist on the surface of many cell types. The Interactions of AGE and RAGE trigger activation of intracellular oxidative stress, PIK3-AKT1 and Jak-STAT signals, thereby producing a large number of growth factors and pro-inflammatory cytokines, such as IL-1βa, IL-6 and NF-κB, which could promote the development of UC. The specific signaling pathways are shown in Fig. 3C (This image is from DAVID's database). The activation of the AGE-RAGE signaling pathway can trigger inflammatory responses, including the promotion of inflammatory cell infiltration and the release of inflammatory factors, leading to tissue damage and the development of inflammatory diseases. Furthermore, activation of the AGE-RAGE signaling pathway can also lead to increased intracellular oxidative stress, thereby affecting normal cellular function, accelerating cellular aging, and the progression of diseases. The AGE-RAGE signaling pathway plays a significant role in physiological and pathological processes such as inflammation and oxidative stress, and may have potential implications for the development of UC.

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Fig. 3

(A)Enrichment analysis results; (B)KEGG pathway enrichment analysis; (C)AGEs-RAGE signaling pathway map.

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3.10 General indicators of FM activity in DSS-induced UC rats

On day 3 after modeling, animals from the model group and each treatment group experienced weight loss, dull coat color, reduced food intake, and varying degrees of diarrhea. Weight loss of the model group was the fastest, with evidence of hematochezia. Body mass, hair and activity levels were all normal.Compared with the control group, disease activity score (DAI) of the model group was significantly higher (P < 0.01). Relative to the model group, DAI scores of rats in FM administration groups were significantly lower (P < 0.05). The scores of the mesalazine and inhibitor groups were markedly decreased (P < 0.01 and P < 0.05, respectively) compared to the model group, as shown in Table 2. Our results showed that all treatments effectively reduced the DAI scores representative of symptoms of UC rats.

3.11 Effect of FM on colon length

The mousecolon lengths from each experimental group are presented in Fig. 4A. Colon length of the model group was significantly smaller than that of the control group (P < 0.01), indicating successful generation of the UC model. Compared with the model group, colon lengths of the FM, mesalazine and inhibitor groups were significantly increased (P < 0.01), clearly demonstrating FM-induced improvement of UC.

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Fig. 4

(A) Effect of FM on the length of colons were observed after FM administration (1, 2, and 4 g/kg), Mesalazine(50 mg/kg) and Inhibitor(5 mg/kg). Data were expressed as the mean ± SEM. **P < 0.01 vs control group; ##P < 0.01 vs model group. (B) Effect of FM on TNF-α. (C) Effect of FM on IL-1β. (D) Effect of FM on NADPH. (E) Effect of FM on ROS.

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3.12 Effect of FM on microscopic injury of colon

The results of HE staining are illustrated in Fig. 5. In the control group, colon tissue structure was complete, glands were arranged neatly, and no ulcers were detected. In the model group, obvious diseased tissue was present in colon and the upper mucosal cells were shed and necrotic. Part of the colon tissue in the low-dose FM group was severely damaged, with infiltration of inflammatory cells. The medium-dose group showed less colonic tissue damage and ordered arrangement of some glands, with a limited number of inflammatory cells. The colon tissue in the high-dose group displayed only a small amount of damage. The majority of glands were arranged neatly and part of the tissue structure was complete. In the mesalazine group, villi of the colon tissue were clear and mucosa was not damaged or ulcerated. In the inhibitor group, colon tissue was relatively complete with limited inflammatory cell infiltration. All animals in the treatment groups showed improvement of UC.

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Fig. 5

Effect of FM on colonic histopathological changes in UC mice (HE staining).

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3.13 Effects of FM on expression of TNF-α, IL-1β, NADPH and ROS

ELISA was applied to determine the levels of TNF-α, IL-1β, NADPH and ROS in sera of mice. The levels of inflammatory factors (TNF-α, IL-1β), reduced coenzymes (NADPH) and reactive oxygen species (ROS) were significantly higher relative to the control group, clearly supporting successful generation of the UC model (Fig. 4B. Fig. 4C. Fig. 4D). Compared with the model group, levels of TNF-α, IL-1β, and ROS in sera of mice from each FM treatment group were significantly reduced (P < 0.05). In view of these findings, we propose that FM suppresses the generation of TNF-α, IL-1β, NADPH and ROS levels in mice with DSS-induced UC, thereby inhibiting the occurrence and development of UC.

3.14 Effects of FM on proteins related to AGE-RAGE signaling

We observed changes in the levels of AGE, RAGE, p38, p-p38,NF-kB and p-NF-kB proteins involved in the AGE-RAGE signaling pathway in each group. The levels of AGE, RAGE, p38, p-p38, NF-kB and p-NF-kB proteins were significantly increased in the model group compared with the control group (P < 0.01; P < 0.05), as shown in Fig. 6A. Notably, relative to the model group, expression levels of each protein in the FM, mesalazine and inhibitor treatment groups were markedly reduced (P < 0.05). The gray value of protein expression in each group showed similar trends. The results are presented in Fig. 6B. Compared with the control group, expression of AGE, RAGE, p38, p-p38, NF-kB and p-NF-kB proteins in the model group was significantly higher. The levels of the corresponding proteins in the other treatment groups were significantly lower than those of the model group (P < 0.05). Based on these results, we propose that FM participates in regulation of AGE/RAGE pathway. Moreover, with increasing treatment concentrations, mRNA and protein expression levels of downstream related genes (p38, p-p38, NF-kB and p-NF-kB) were inhibited, contributing to protective effects against DSS-induced UC.

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Fig. 6

A:Electrophoresis of protein expression in mouse colon. (1: Control; 2: Model; 3: 1 g/kg FM; 4: 2 g/kg FM; 5 4 g/kg FM; 6: The mesalazine group; 7: The inhibitor group); B Effects of FM on the expressions of AGEs、RAGE、p38、p-p38、NF-kB and p-NF-kB proteins in colon. The expressions of AGEs, RAGE, p38, p-p38, NF-kB and p-NF-kB proteins in colon were determined by western blotting analysis after FM administration (1, 2, and 4 g/kg), Mesalazine(50 mg/kg) and Inhibitor(5 mg/ml). Data were expressed as the mean ± SEM.**P < 0.01 vs normal group; ^##P < 0.01 vs model group.

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