Chrysin ameliorates dextran sulfate-induced ulcerative colitis in mice by modulating inflammation and gut microbiota
Inflammatory bowel disease (IBD) represents a complex group of inflammatory conditions primarily affecting the human intestinal mucosa. Among these, Crohn’s disease (CD) and ulcerative colitis (UC) are the most prevalent forms, characterized by both gastrointestinal and systemic symptoms, including significant weight loss, bloody diarrhea, fever, gastrointestinal dysmotility, and colonic shortening . The pathogenesis of IBD is closely linked to an overproduction of inflammatory mediators such as tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), and interleukin-6 (IL-6), which are key drivers of the inflammatory response . Current therapeutic approaches for IBD, including glucocorticoids, 5-aminosalicylic acid, and various immunosuppressants, have been integrated into clinical practice . However, these treatments often fail to induce remission in a substantial subset of patients and are frequently associated with adverse effects. This has prompted interest in alternative therapies, particularly those derived from dietary sources, which are generally considered safer but lack robust scientific validation regarding their efficacy and mechanisms of action . Flavonoids, naturally occurring polyphenolic compounds in plants, are abundant in the human diet and have been identified in numerous plant extracts, honey, and propolis . Chrysin (5, 7-dihydroxyflavone), a specific type of flavonoid, has shown promise due to its anti-inflammatory, anticancer, and antioxidant properties . Despite its potential, the specific role of Chrysin in modulating inflammatory diseases such as IBD remains underexplored. This study aims to elucidate the pharmacological mechanisms underlying Chrysin’s beneficial effects on IBD using a combination of bioinformatics and network pharmacology approaches. We developed a murine model of IBD induced by dextran sulfate sodium (DSS) to closely mimic the disruption of intestinal microflora and to assess the protective effects of Chrysin. Through these investigative methods, we aim to provide a deeper understanding of Chrysin’s therapeutic potential, paving the way for novel, evidence-based clinical applications in the management of IBD. 

Screening of Chrysin targets To identify the molecular targets of Chrysin, begin by searching for Chrysin on PubChem to access comprehensive chemical and biological data. Then, download the target data from four established pharmacological databases: Comparative Toxicogenomics Database (CTD) ( http://ctdbase.org/ ), SwissTargetPrediction (STP) ( http://www.swisstargetprediction.ch/ ), Binding Database (BindingDB) ( http://bindingdb.org/bind/index.jsp ), and TargetNet ( http://targetnet.scbdd.com/home/index/ ). After collecting the target data, use Venn diagram analysis to visually represent and compare the overlapping and unique targets of Chrysin identified across these platforms. Microarray data and identification of differentially expressed genes The mRNA expression profiling dataset GSE9452 was retrieved from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). This dataset comprises tissue samples from 8 individuals with inflammatory bowel disease (IBD) and 18 non-IBD controls, and it utilizes the GPL570 [HG-U133_Plus_2] platform of the Affymetrix Human Genome U133 Plus 2.0 Array. Differentially expressed genes (DEGs) between IBD and healthy control samples were identified using the Limma package in the R programming environment. The criteria set for significant DEGs included an absolute log2 fold change (|Log2FC|) greater than 0.25, a p -value less than 0.05, and a false discovery rate (FDR) below 0.05. The FDR is a statistical measure used to estimate the proportion of false positives among the genes declared significantly different, thereby controlling the expected proportion of incorrect rejections of the null hypothesis. Functional enrichment analyses The intersection of genes identified from microarray-based differentially expressed genes and Chrysin target genes was subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. These analyses were performed to elucidate the biological functions and pathways associated with the intersected gene set. The statistical significance for enriched GO terms and KEGG pathways was established with a p -value threshold of less than 0.05. The “clusterProfiler” package in R was utilized to conduct these enrichment analyses, offering a comprehensive approach for interpreting gene functions and pathway involvement. IBD and Chrysin target PPI network analysis The protein–protein interaction (PPI) networks for the genes differentially expressed in IBD and associated with Chrysin targets were constructed using the STRING database software. This analysis provides insights into the complex molecular interactions and functional associations between these genes. Subsequent degree analysis of the network was conducted using the CytoNCA module within the Cytoscape software suite. This analysis enabled the identification of key nodes within the network, highlighting proteins with high connectivity that may play crucial roles in the biological processes influenced by Chrysin in IBD. Additionally, significant clusters within the network were isolated using the MCODE module in Cytoscape, allowing for the identification of densely interconnected regions or key clusters that are potentially significant in the pathophysiology of IBD and the mechanism of action of Chrysin. Animal experimental design Male C57BL/6 mice, aged 6–8 weeks, were selected for the study to ensure they were healthy and free from disease. Prior to experimentation, mice were acclimatized to the laboratory environment for at least 1 week under standard conditions, including a 12-h light/dark cycle, with ad libitum access to drinking water and standard chow. For the induction of colitis, formulations of 2–5% (w/v) DSS (dextran sulfate sodium, catalog no. 9011–18-1, Merck) were utilized, with concentrations typically ranging between 2.5 and 3%. DSS powder was dissolved in sterile drinking water to ensure complete dissolution and was provided as the sole source of drinking water, continuously administered for 4–8 weeks. The DSS solution was refreshed daily to maintain potency. Throughout the experiment, parameters such as body weight, food and water intake, and overall health status of the mice were monitored daily. Observations were also made for clinical signs such as diarrhea and presence of blood in the stool, documenting the progression of colitis symptoms. The colitis model of mice used was from the Experiment Animal Center of the Army Medical University. The anesthetic, surgical, and post-operative care protocols were examined and approved by the Animal Ethics Committee of Army Medical University (AMUWEC20235020). 16S RNA sequencing and metabolome sequencing of cecal contents We commissioned the sequencing and metabolomics analyses to Maiwei Metabolism (Chongqing) Company. Initially, fresh colon contents were collected and stored at − 80 °C. Genomic DNA was extracted using the TIANamp Stool DNA Kit (DP328, TIAGEN, China), and DNA concentrations were quantified with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). The V3–V4 region of the 16S rRNA gene was amplified and subjected to paired-end sequencing on an Illumina MiSeq platform (Illumina, USA). Quality filtering and merging of raw reads were conducted using FLASH software . Amplicon sequence variant (ASV) analysis was executed using UPARSE software (version 7.1) . Bacterial diversity was evaluated using alpha diversity indices such as Chao1, ACE, Shannon, and Simpson, along with principal coordinate analysis (PCoA) based on Bray–Curtis distances. Enriched bacteria were identified using linear discriminant analysis (LDA) through LEfSe, and indicator taxa were determined by indicator value analysis ( p ≤ 0.05) . Correlations between key microbial taxa and biochemical parameters associated with ulcerative colitis (UC) were also calculated. For metabolomics analysis, cecal content samples were thawed on ice, and 10 µL of each sample was pooled to prepare a quality control (QC) sample that represented the median profile encountered during the analyses. Metabolite separation and extraction were performed by adding 200 µL of methanol (1:2 v/v) to 100 µL of the cecal mixture, followed by centrifugation (14,000 × g, 4 °C, 10 min). The supernatant was then analyzed using liquid chromatography-mass spectrometry (LC–MS). Mass spectral data preprocessing included peak picking, grouping, retention time correction, secondary peak grouping, and annotation of isotopes and adducts. LC–MS raw data files were converted to mzXML format and analyzed using the XCMS and CAMERA packages in the R statistical language (v3.4.1). The KEGG ( http://www.genome.jp/kegg/ ) and HMDB ( http://www.hmdb.ca ) databases were utilized to identify differential metabolites. Hematoxylin and eosin (H&E) staining Colon tissue samples were fixed in 4% paraformaldehyde and subsequently embedded in paraffin blocks to prepare for sectioning. The sections were then deparaffinized and rehydrated through a graded series of xylene to ethanol and finally water. Following rehydration, the sections underwent hematoxylin and eosin (H&E) staining to highlight the cellular and structural components. Dehydration was performed post-staining to prepare the slides for viewing. Histopathological analysis was conducted using a light microscope to assess the morphological and pathological changes within the tissue. Enzyme-linked immunosorbent assay (ELISA) For the ELISA procedure, the antibody was diluted to a concentration of 1–10 µg/ml using an appropriate blocking buffer. A volume of 0.1 ml of the diluted antibody solution was dispensed into each well, containing specific antibodies for IL-6 (P05231, Merck), IL-1 (P01584, Merck), IL-17 (RAB0262, Merck), and MPO (RAB0374, Merck), and incubated overnight at 4 °C. The following day, the antibody solution was discarded, and the wells were washed three times for 3 min each to remove unbound antibodies. Next, 0.1 ml of blocking solution was added to each well to prevent non-specific binding, and the plates were incubated at 37 °C for 1 h, followed by another washing step. Subsequently, 0.1 ml of each test sample along with gradient-diluted antigen standards was added to the respective wells and incubated at 37 °C for 1 h, followed by a washing step to remove unbound antigens. Afterward, 0.1 ml of freshly diluted enzyme-conjugated secondary antibodies was added to each well. The dilution ratio was optimized for the best performance. The plates were then incubated at 37 °C for 30 to 60 min, followed by a final washing step. For the development of the colorimetric reaction, 0.1 ml of freshly prepared TMB (tetramethylbenzidine) substrate solution was added to each well and allowed to react at 37 °C for 10 to 30 min. The reaction was stopped by adding 50 µl of 2 M sulfuric acid to each well. The absorbance was measured at 450 nm using a microplate reader. The optical density (OD) value of the blank wells was subtracted from that of the test wells. A standard curve was plotted to calculate the antigen concentration in the samples, allowing for quantitative analysis of the cytokine levels. Western blot analysis For the Western blot analysis, protein samples were separated by SDS-PAGE and subsequently transferred onto a PVDF membrane. The membrane was blocked with 5% skim milk at room temperature for 1 h to prevent non-specific binding. After blocking, the membrane was incubated overnight at 4 °C with primary antibodies targeting specific proteins: NF-κB p65 (10745–1-AP, Proteintech), phosphorylated p65 (ab31624, Abcam), IκB-α (ZRB1665, Merck), and phosphorylated IκB-α (2859 T, Cell Signaling Technology), all diluted 1:500. This was followed by a 1-h incubation with horseradish peroxidase-conjugated secondary antibodies, also diluted 1:500. Beta-actin (Cat. no. MA1-140, Invitrogen) was used as a loading control to ensure equal protein loading across the gels. The chemiluminescent signal was detected using an appropriate imaging system, allowing for the quantification of protein expression levels relative to beta-actin, providing insights into the regulatory mechanisms of the NF-κB signaling pathway. Transcriptomic analysis The transcriptomic analysis began with the extraction of total RNA, which was then quality assessed using the Bioanalyzer 2100 system (Agilent Technologies). Only RNA samples that met quality standards were advanced to the next stage. These qualified samples were used to construct sequencing libraries employing the Illumina TruSeq RNA Sample Preparation Kit (Illumina, USA), following the manufacturer’s guidelines. The prepared libraries were sequenced on the McWei sequencing platform (McWei, Wuhan), and the resultant raw data were stored in FASTQ format. Initial quality control of the raw sequence data was performed using FastQC software, which facilitated the identification and removal of low-quality reads and adapter sequences. Following this preprocessing step, the high-quality, cleaned data were used for downstream analyses, including alignment to the reference genome, quantification of gene expression, and differential expression analysis. 

To identify the molecular targets of Chrysin, begin by searching for Chrysin on PubChem to access comprehensive chemical and biological data. Then, download the target data from four established pharmacological databases: Comparative Toxicogenomics Database (CTD) ( http://ctdbase.org/ ), SwissTargetPrediction (STP) ( http://www.swisstargetprediction.ch/ ), Binding Database (BindingDB) ( http://bindingdb.org/bind/index.jsp ), and TargetNet ( http://targetnet.scbdd.com/home/index/ ). After collecting the target data, use Venn diagram analysis to visually represent and compare the overlapping and unique targets of Chrysin identified across these platforms. 

The mRNA expression profiling dataset GSE9452 was retrieved from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). This dataset comprises tissue samples from 8 individuals with inflammatory bowel disease (IBD) and 18 non-IBD controls, and it utilizes the GPL570 [HG-U133_Plus_2] platform of the Affymetrix Human Genome U133 Plus 2.0 Array. Differentially expressed genes (DEGs) between IBD and healthy control samples were identified using the Limma package in the R programming environment. The criteria set for significant DEGs included an absolute log2 fold change (|Log2FC|) greater than 0.25, a p -value less than 0.05, and a false discovery rate (FDR) below 0.05. The FDR is a statistical measure used to estimate the proportion of false positives among the genes declared significantly different, thereby controlling the expected proportion of incorrect rejections of the null hypothesis. 

The intersection of genes identified from microarray-based differentially expressed genes and Chrysin target genes was subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. These analyses were performed to elucidate the biological functions and pathways associated with the intersected gene set. The statistical significance for enriched GO terms and KEGG pathways was established with a p -value threshold of less than 0.05. The “clusterProfiler” package in R was utilized to conduct these enrichment analyses, offering a comprehensive approach for interpreting gene functions and pathway involvement. 

The protein–protein interaction (PPI) networks for the genes differentially expressed in IBD and associated with Chrysin targets were constructed using the STRING database software. This analysis provides insights into the complex molecular interactions and functional associations between these genes. Subsequent degree analysis of the network was conducted using the CytoNCA module within the Cytoscape software suite. This analysis enabled the identification of key nodes within the network, highlighting proteins with high connectivity that may play crucial roles in the biological processes influenced by Chrysin in IBD. Additionally, significant clusters within the network were isolated using the MCODE module in Cytoscape, allowing for the identification of densely interconnected regions or key clusters that are potentially significant in the pathophysiology of IBD and the mechanism of action of Chrysin. 

Male C57BL/6 mice, aged 6–8 weeks, were selected for the study to ensure they were healthy and free from disease. Prior to experimentation, mice were acclimatized to the laboratory environment for at least 1 week under standard conditions, including a 12-h light/dark cycle, with ad libitum access to drinking water and standard chow. For the induction of colitis, formulations of 2–5% (w/v) DSS (dextran sulfate sodium, catalog no. 9011–18-1, Merck) were utilized, with concentrations typically ranging between 2.5 and 3%. DSS powder was dissolved in sterile drinking water to ensure complete dissolution and was provided as the sole source of drinking water, continuously administered for 4–8 weeks. The DSS solution was refreshed daily to maintain potency. Throughout the experiment, parameters such as body weight, food and water intake, and overall health status of the mice were monitored daily. Observations were also made for clinical signs such as diarrhea and presence of blood in the stool, documenting the progression of colitis symptoms. The colitis model of mice used was from the Experiment Animal Center of the Army Medical University. The anesthetic, surgical, and post-operative care protocols were examined and approved by the Animal Ethics Committee of Army Medical University (AMUWEC20235020). 

We commissioned the sequencing and metabolomics analyses to Maiwei Metabolism (Chongqing) Company. Initially, fresh colon contents were collected and stored at − 80 °C. Genomic DNA was extracted using the TIANamp Stool DNA Kit (DP328, TIAGEN, China), and DNA concentrations were quantified with a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). The V3–V4 region of the 16S rRNA gene was amplified and subjected to paired-end sequencing on an Illumina MiSeq platform (Illumina, USA). Quality filtering and merging of raw reads were conducted using FLASH software . Amplicon sequence variant (ASV) analysis was executed using UPARSE software (version 7.1) . Bacterial diversity was evaluated using alpha diversity indices such as Chao1, ACE, Shannon, and Simpson, along with principal coordinate analysis (PCoA) based on Bray–Curtis distances. Enriched bacteria were identified using linear discriminant analysis (LDA) through LEfSe, and indicator taxa were determined by indicator value analysis ( p ≤ 0.05) . Correlations between key microbial taxa and biochemical parameters associated with ulcerative colitis (UC) were also calculated. For metabolomics analysis, cecal content samples were thawed on ice, and 10 µL of each sample was pooled to prepare a quality control (QC) sample that represented the median profile encountered during the analyses. Metabolite separation and extraction were performed by adding 200 µL of methanol (1:2 v/v) to 100 µL of the cecal mixture, followed by centrifugation (14,000 × g, 4 °C, 10 min). The supernatant was then analyzed using liquid chromatography-mass spectrometry (LC–MS). Mass spectral data preprocessing included peak picking, grouping, retention time correction, secondary peak grouping, and annotation of isotopes and adducts. LC–MS raw data files were converted to mzXML format and analyzed using the XCMS and CAMERA packages in the R statistical language (v3.4.1). The KEGG ( http://www.genome.jp/kegg/ ) and HMDB ( http://www.hmdb.ca ) databases were utilized to identify differential metabolites. 

Colon tissue samples were fixed in 4% paraformaldehyde and subsequently embedded in paraffin blocks to prepare for sectioning. The sections were then deparaffinized and rehydrated through a graded series of xylene to ethanol and finally water. Following rehydration, the sections underwent hematoxylin and eosin (H&E) staining to highlight the cellular and structural components. Dehydration was performed post-staining to prepare the slides for viewing. Histopathological analysis was conducted using a light microscope to assess the morphological and pathological changes within the tissue. 

For the ELISA procedure, the antibody was diluted to a concentration of 1–10 µg/ml using an appropriate blocking buffer. A volume of 0.1 ml of the diluted antibody solution was dispensed into each well, containing specific antibodies for IL-6 (P05231, Merck), IL-1 (P01584, Merck), IL-17 (RAB0262, Merck), and MPO (RAB0374, Merck), and incubated overnight at 4 °C. The following day, the antibody solution was discarded, and the wells were washed three times for 3 min each to remove unbound antibodies. Next, 0.1 ml of blocking solution was added to each well to prevent non-specific binding, and the plates were incubated at 37 °C for 1 h, followed by another washing step. Subsequently, 0.1 ml of each test sample along with gradient-diluted antigen standards was added to the respective wells and incubated at 37 °C for 1 h, followed by a washing step to remove unbound antigens. Afterward, 0.1 ml of freshly diluted enzyme-conjugated secondary antibodies was added to each well. The dilution ratio was optimized for the best performance. The plates were then incubated at 37 °C for 30 to 60 min, followed by a final washing step. For the development of the colorimetric reaction, 0.1 ml of freshly prepared TMB (tetramethylbenzidine) substrate solution was added to each well and allowed to react at 37 °C for 10 to 30 min. The reaction was stopped by adding 50 µl of 2 M sulfuric acid to each well. The absorbance was measured at 450 nm using a microplate reader. The optical density (OD) value of the blank wells was subtracted from that of the test wells. A standard curve was plotted to calculate the antigen concentration in the samples, allowing for quantitative analysis of the cytokine levels. 

For the Western blot analysis, protein samples were separated by SDS-PAGE and subsequently transferred onto a PVDF membrane. The membrane was blocked with 5% skim milk at room temperature for 1 h to prevent non-specific binding. After blocking, the membrane was incubated overnight at 4 °C with primary antibodies targeting specific proteins: NF-κB p65 (10745–1-AP, Proteintech), phosphorylated p65 (ab31624, Abcam), IκB-α (ZRB1665, Merck), and phosphorylated IκB-α (2859 T, Cell Signaling Technology), all diluted 1:500. This was followed by a 1-h incubation with horseradish peroxidase-conjugated secondary antibodies, also diluted 1:500. Beta-actin (Cat. no. MA1-140, Invitrogen) was used as a loading control to ensure equal protein loading across the gels. The chemiluminescent signal was detected using an appropriate imaging system, allowing for the quantification of protein expression levels relative to beta-actin, providing insights into the regulatory mechanisms of the NF-κB signaling pathway. 

The transcriptomic analysis began with the extraction of total RNA, which was then quality assessed using the Bioanalyzer 2100 system (Agilent Technologies). Only RNA samples that met quality standards were advanced to the next stage. These qualified samples were used to construct sequencing libraries employing the Illumina TruSeq RNA Sample Preparation Kit (Illumina, USA), following the manufacturer’s guidelines. The prepared libraries were sequenced on the McWei sequencing platform (McWei, Wuhan), and the resultant raw data were stored in FASTQ format. Initial quality control of the raw sequence data was performed using FastQC software, which facilitated the identification and removal of low-quality reads and adapter sequences. Following this preprocessing step, the high-quality, cleaned data were used for downstream analyses, including alignment to the reference genome, quantification of gene expression, and differential expression analysis. 

Cyberpharmacology prediction of Chrysin’s role in intestinal diseases To elucidate the role of Chrysin in intestinal diseases, target predictions were made using four pharmacological databases: CTD, STP, BindingDB, and TargetNet. After eliminating redundant targets, 202 key targets associated with Chrysin were identified. The GSE9452 dataset, obtained from the GEO database, yielded 588 differentially expressed genes (DEGs) (Fig. A). Venn analysis of the targets shared between the drug and disease highlighted 193 crossover targets (Fig. B). Subsequent functional enrichment analyses of these 193 genes were performed, revealing significant associations in biological processes (BP), cellular components (CC), and molecular functions (MF). The identified genes are primarily involved in pathways such as adenylate cyclase-modulating G protein-coupled receptor signaling, coupled to cyclic nucleotide second messengers, and various synaptic processes including the synaptic membrane, postsynaptic membrane, and integral components of the postsynaptic membrane. These genes also exhibited significant activity in neurotransmitter receptor activity, G protein-coupled amine receptor activity, and postsynaptic neurotransmitter receptor activity (Fig. C). Additionally, KEGG pathway enrichment analysis showed that these genes are predominantly involved in pathways such as neuroactive ligand-receptor interaction, hepatitis B, serotonergic synapse, measles, prostate cancer, toxoplasmosis, pancreatic cancer, nitrogen metabolism, Th17 cell differentiation, and Epstein-Barr virus infection (Fig. C). Protein–protein interaction (PPI) network analysis was conducted using the STRING database to predict interactions among these key genes, focusing on the core genes within significant clusters (Fig. D). Chrysin mitigates DSS-induced intestinal inflammation To assess the therapeutic potential of Chrysin in intestinal diseases, a dextran sulfate sodium (DSS)-induced model of intestinal inflammation was employed (Fig. A). Chrysin administration notably mitigated the DSS-mediated elevation in the Disease Activity Index (DAI), significantly reducing the scores observed during disease progression (Fig. B). In this model, DSS-treated mice exhibited weight loss, whereas control mice displayed weight gain. Remarkably, Chrysin treatment effectively curbed the loss of body weight (Fig. C). Furthermore, while colonic shortening was pronounced in the DSS-treated mice, significant amelioration was observed following Chrysin administration, suggesting a protective effect against structural damage induced by DSS (Fig. D, E). Histopathological evaluation by hematoxylin and eosin (H&E) staining of the colon tissue revealed severe mucosal injury in DSS-treated mice. In contrast, Chrysin treatment resulted in substantial reduction of mucosal damage (Fig. F, G). Biochemical analysis demonstrated that TNF-α levels, significantly elevated in the DSS group, were markedly reduced in the Chrysin and control groups (Fig. H). Further, ELISA assays quantified the inflammatory markers interleukin-6 (IL-6), interleukin-1β (IL-1β), and myeloperoxidase (MPO) in colon tissues. DSS administration significantly increased IL-1β, IL-6, and MPO levels compared to control, while Chrysin treatment significantly suppressed these elevations (Fig. I–K). Transcriptomic insights into Chrysin’s role in DSS-induced intestinal diseases To delve deeper into the molecular mechanisms underlying Chrysin’s therapeutic effects on intestinal diseases, we utilized RNA sequencing to analyze genome-wide transcriptional changes in DSS-treated mice following Chrysin administration. Our differentially expressed genes (DEGs) are visualized in volcano plots and heatmaps (Fig. A, B), identifying a total of 1002 distinct DEGs, with 552 upregulated and 450 downregulated post-Chrysin treatment. Gene Ontology (GO) enrichment analysis of these DEGs highlighted significant enrichments in cellular components such as the immunoglobulin complex, postsynaptic specialized membrane, and synaptic membrane (Fig. C). In biological processes, DEGs were predominantly involved in the circulating immunoglobulin-mediated humoral immune response, complement activation, B-cell-mediated immunity, and phagocytosis. Molecular function analysis revealed that the DEGs were chiefly associated with immunoglobulin receptor binding, carbohydrate binding, and antigen binding. Further, Reactome pathway analysis indicated that these genes were involved in critical pathways such as MAPK activation, CD22-mediated BCR regulation, and NF-κB activation (Fig. D). KEGG pathway enrichment analysis demonstrated that the DEGs in both Chrysin-treated and DSS model groups participated in 184 signaling pathways, with 23 pathways showing significant enrichment ( p < 0.05) (Fig. E). Noteworthy pathways include cytokine-cytokine receptor interaction, IL-17 signaling pathway, and sphingolipid biosynthesis. Importantly, DEGs were significantly enriched in pathways central to inflammatory responses, such as the NF-kappaB signaling pathway and the inflammatory bowel disease pathway. Chrysin modulates NF-κB pathway activation and IL-17 expression in DSS-induced intestinal dysfunction in mice The NF-κB signaling pathway is pivotal in the pathogenesis of DSS-induced intestinal diseases . Leveraging our transcriptomic data, we explored the impact of Chrysin on the NF-κB pathway in the colonic tissues of DSS-treated mice. As illustrated in Fig. A, there was a significant upregulation in the expression levels of phosphorylated p65 (p-p65), phosphorylated IκBα (p-IκBα), and IKK in the colons of mice from the DSS group compared to controls. Notably, Chrysin treatment markedly reduced the expression of p-p65, p-IκBα, and IKK relative to the DSS group, suggesting that Chrysin attenuates inflammation by inhibiting the NF-κB pathway. Furthermore, our transcriptome sequencing results indicated that Chrysin significantly impacts the IL-17 signaling pathway in the colonic tissues of mice subjected to DSS. IL-17, a critical promoter of T-cell-induced inflammatory responses, enhances inflammation by facilitating the release of pro-inflammatory cytokines. ELISA results depicted in Fig. B demonstrate that IL-17 levels were significantly elevated in the DSS group compared to controls. However, Chrysin treatment resulted in a substantial decrease in IL-17 expression compared to the DSS group, indicating that Chrysin mitigates inflammation by inhibiting IL-17-mediated T-cell activation. Chrysin reshapes the intestinal microbial community structure in DSS-treated mice We conducted 16S rRNA gene sequencing on colon content samples from three groups, yielding an average of 40,093 sequences for control samples, 45,227 for DSS model samples, and 73,969 for Chrysin-treated samples (sTable ). Through a noise-reduction method, amplicon sequence variants (ASVs) were identified, analyzing common and unique ASVs among the groups. The number of ASVs identified were 712, 620, and 685, respectively (Fig. A). Alpha diversity indices, including Chao1 and Shannon, were employed to assess the species richness and complexity across the groups (Fig. B, C). Both indices were significantly lower in the DSS group compared to the control, indicating reduced microbial diversity. Notably, post-Chrysin treatment, the microbial diversity showed signs of recovery, with an increase in the Chao1 index, although Shannon indices did not show significant changes. Bray–Curtis-based principal coordinate analysis (PCoA) distinctly illustrated the divergence between the microbial communities of the DSS group and the control group and demonstrated partial restoration following Chrysin treatment (Fig. D). Phylogenetic diversity analysis further highlighted significant shifts in microbial abundance and diversity between the Chrysin-treated mice and the DSS group, along with notable changes in bacterial community composition (Fig. E, F). At the phylum level (Fig. E), Bacteroidetes and Firmicutes were the predominant taxa in all groups. The DSS group exhibited a reduction in Firmicutes and an increase in Cyanobacteria compared to controls. Chrysin treatment significantly reduced Cyanobacteria levels. At the genus level (Fig. F), Chrysin treatment significantly increased Lactobacillus levels, while Alloprevotella levels returned to those of the control group. Using linear discriminant analysis effect size (LEfSe), we summarized the enrichment and variation within each bacterial community to pinpoint specific taxa with altered abundances that could serve as potential biomarkers. A total of 50 genera with LDA scores greater than 3.5 were identified (Fig. G). The branching plots of family and generic abundance are depicted in Fig. H. Indicator markers for Chrysin treatment were predominantly observed at various taxonomic levels (sFigs. , ). At the family level (sFig. A), Lactobacillaceae and Bacteroidaceae were key indicators. At the class level (sFig. B), Lactobacillales emerged as an important indicator. At the species level (sFig. A), taxa such as Lachnospiraceae were significant indicators. At the order level, Campylobacterales, Clostridiales, and Lactobacillales were highlighted as critical indicators (sFig. B). These ASVs offer valuable biomarkers for assessing the efficacy of Chrysin in treating intestinal diseases. Impact of Chrysin on intestinal metabolites in DSS-treated mice To elucidate the impact of Chrysin on the metabolomic profile of mice, comprehensive non-targeted metabolomic sequencing was conducted using liquid chromatography-mass spectrometry (LC–MS). Initial principal component analysis (PCA) was performed to assess variations between and within groups, setting the stage for comparative analyses (Fig. A). Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to identify differentially expressed metabolites among the groups, with selection criteria set at VIP values > 1 in the OPLS-DA model and p -values < 0.05 in T -tests (Fig. B, C). The relationship between different metabolites in each group was depicted in a Venn diagram, revealing a total of 298 distinct metabolites, with fatty acyl changes being the most pronounced (sTable ). KEGG pathway enrichment analysis of these 298 metabolites highlighted significant alterations in pathways such as thermogenesis, choline metabolism in cancer, glycerophospholipid metabolism, nucleotide metabolism, pyrimidine metabolism, autoimmune thyroid disease, one carbon pool by folate, antifolate resistance, folate biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis ( p < 0.05). These pathways are crucial for regulating animal growth and development (Fig. E). The identified metabolites are associated with key metabolic processes including pentose and glucuronate interconversions, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, and folate biosynthesis (Fig. F). Correlations between the colon metabolome and gut microbiome Further analysis explored the interplay between the intestinal microbiota and metabolites through Spearman correlation analysis involving 298 differential metabolites and six phylum-level bacterial groups (Fig. A, B). The analysis highlighted 20 metabolites and six bacterial groups exhibiting the most significant interactions, suggesting a robust correlation between changes in the gut microbiome and metabolic alterations. These findings indicate that the modulation of intestinal metabolites observed during Chrysin treatment for ulcerative colitis may be driven by alterations in the gut microbiota, underscoring the interconnected nature of microbial and metabolic dynamics in therapeutic responses. 

To elucidate the role of Chrysin in intestinal diseases, target predictions were made using four pharmacological databases: CTD, STP, BindingDB, and TargetNet. After eliminating redundant targets, 202 key targets associated with Chrysin were identified. The GSE9452 dataset, obtained from the GEO database, yielded 588 differentially expressed genes (DEGs) (Fig. A). Venn analysis of the targets shared between the drug and disease highlighted 193 crossover targets (Fig. B). Subsequent functional enrichment analyses of these 193 genes were performed, revealing significant associations in biological processes (BP), cellular components (CC), and molecular functions (MF). The identified genes are primarily involved in pathways such as adenylate cyclase-modulating G protein-coupled receptor signaling, coupled to cyclic nucleotide second messengers, and various synaptic processes including the synaptic membrane, postsynaptic membrane, and integral components of the postsynaptic membrane. These genes also exhibited significant activity in neurotransmitter receptor activity, G protein-coupled amine receptor activity, and postsynaptic neurotransmitter receptor activity (Fig. C). Additionally, KEGG pathway enrichment analysis showed that these genes are predominantly involved in pathways such as neuroactive ligand-receptor interaction, hepatitis B, serotonergic synapse, measles, prostate cancer, toxoplasmosis, pancreatic cancer, nitrogen metabolism, Th17 cell differentiation, and Epstein-Barr virus infection (Fig. C). Protein–protein interaction (PPI) network analysis was conducted using the STRING database to predict interactions among these key genes, focusing on the core genes within significant clusters (Fig. D). 

To assess the therapeutic potential of Chrysin in intestinal diseases, a dextran sulfate sodium (DSS)-induced model of intestinal inflammation was employed (Fig. A). Chrysin administration notably mitigated the DSS-mediated elevation in the Disease Activity Index (DAI), significantly reducing the scores observed during disease progression (Fig. B). In this model, DSS-treated mice exhibited weight loss, whereas control mice displayed weight gain. Remarkably, Chrysin treatment effectively curbed the loss of body weight (Fig. C). Furthermore, while colonic shortening was pronounced in the DSS-treated mice, significant amelioration was observed following Chrysin administration, suggesting a protective effect against structural damage induced by DSS (Fig. D, E). Histopathological evaluation by hematoxylin and eosin (H&E) staining of the colon tissue revealed severe mucosal injury in DSS-treated mice. In contrast, Chrysin treatment resulted in substantial reduction of mucosal damage (Fig. F, G). Biochemical analysis demonstrated that TNF-α levels, significantly elevated in the DSS group, were markedly reduced in the Chrysin and control groups (Fig. H). Further, ELISA assays quantified the inflammatory markers interleukin-6 (IL-6), interleukin-1β (IL-1β), and myeloperoxidase (MPO) in colon tissues. DSS administration significantly increased IL-1β, IL-6, and MPO levels compared to control, while Chrysin treatment significantly suppressed these elevations (Fig. I–K). 

To delve deeper into the molecular mechanisms underlying Chrysin’s therapeutic effects on intestinal diseases, we utilized RNA sequencing to analyze genome-wide transcriptional changes in DSS-treated mice following Chrysin administration. Our differentially expressed genes (DEGs) are visualized in volcano plots and heatmaps (Fig. A, B), identifying a total of 1002 distinct DEGs, with 552 upregulated and 450 downregulated post-Chrysin treatment. Gene Ontology (GO) enrichment analysis of these DEGs highlighted significant enrichments in cellular components such as the immunoglobulin complex, postsynaptic specialized membrane, and synaptic membrane (Fig. C). In biological processes, DEGs were predominantly involved in the circulating immunoglobulin-mediated humoral immune response, complement activation, B-cell-mediated immunity, and phagocytosis. Molecular function analysis revealed that the DEGs were chiefly associated with immunoglobulin receptor binding, carbohydrate binding, and antigen binding. Further, Reactome pathway analysis indicated that these genes were involved in critical pathways such as MAPK activation, CD22-mediated BCR regulation, and NF-κB activation (Fig. D). KEGG pathway enrichment analysis demonstrated that the DEGs in both Chrysin-treated and DSS model groups participated in 184 signaling pathways, with 23 pathways showing significant enrichment ( p < 0.05) (Fig. E). Noteworthy pathways include cytokine-cytokine receptor interaction, IL-17 signaling pathway, and sphingolipid biosynthesis. Importantly, DEGs were significantly enriched in pathways central to inflammatory responses, such as the NF-kappaB signaling pathway and the inflammatory bowel disease pathway. 

The NF-κB signaling pathway is pivotal in the pathogenesis of DSS-induced intestinal diseases . Leveraging our transcriptomic data, we explored the impact of Chrysin on the NF-κB pathway in the colonic tissues of DSS-treated mice. As illustrated in Fig. A, there was a significant upregulation in the expression levels of phosphorylated p65 (p-p65), phosphorylated IκBα (p-IκBα), and IKK in the colons of mice from the DSS group compared to controls. Notably, Chrysin treatment markedly reduced the expression of p-p65, p-IκBα, and IKK relative to the DSS group, suggesting that Chrysin attenuates inflammation by inhibiting the NF-κB pathway. Furthermore, our transcriptome sequencing results indicated that Chrysin significantly impacts the IL-17 signaling pathway in the colonic tissues of mice subjected to DSS. IL-17, a critical promoter of T-cell-induced inflammatory responses, enhances inflammation by facilitating the release of pro-inflammatory cytokines. ELISA results depicted in Fig. B demonstrate that IL-17 levels were significantly elevated in the DSS group compared to controls. However, Chrysin treatment resulted in a substantial decrease in IL-17 expression compared to the DSS group, indicating that Chrysin mitigates inflammation by inhibiting IL-17-mediated T-cell activation. 

We conducted 16S rRNA gene sequencing on colon content samples from three groups, yielding an average of 40,093 sequences for control samples, 45,227 for DSS model samples, and 73,969 for Chrysin-treated samples (sTable ). Through a noise-reduction method, amplicon sequence variants (ASVs) were identified, analyzing common and unique ASVs among the groups. The number of ASVs identified were 712, 620, and 685, respectively (Fig. A). Alpha diversity indices, including Chao1 and Shannon, were employed to assess the species richness and complexity across the groups (Fig. B, C). Both indices were significantly lower in the DSS group compared to the control, indicating reduced microbial diversity. Notably, post-Chrysin treatment, the microbial diversity showed signs of recovery, with an increase in the Chao1 index, although Shannon indices did not show significant changes. Bray–Curtis-based principal coordinate analysis (PCoA) distinctly illustrated the divergence between the microbial communities of the DSS group and the control group and demonstrated partial restoration following Chrysin treatment (Fig. D). Phylogenetic diversity analysis further highlighted significant shifts in microbial abundance and diversity between the Chrysin-treated mice and the DSS group, along with notable changes in bacterial community composition (Fig. E, F). At the phylum level (Fig. E), Bacteroidetes and Firmicutes were the predominant taxa in all groups. The DSS group exhibited a reduction in Firmicutes and an increase in Cyanobacteria compared to controls. Chrysin treatment significantly reduced Cyanobacteria levels. At the genus level (Fig. F), Chrysin treatment significantly increased Lactobacillus levels, while Alloprevotella levels returned to those of the control group. Using linear discriminant analysis effect size (LEfSe), we summarized the enrichment and variation within each bacterial community to pinpoint specific taxa with altered abundances that could serve as potential biomarkers. A total of 50 genera with LDA scores greater than 3.5 were identified (Fig. G). The branching plots of family and generic abundance are depicted in Fig. H. Indicator markers for Chrysin treatment were predominantly observed at various taxonomic levels (sFigs. , ). At the family level (sFig. A), Lactobacillaceae and Bacteroidaceae were key indicators. At the class level (sFig. B), Lactobacillales emerged as an important indicator. At the species level (sFig. A), taxa such as Lachnospiraceae were significant indicators. At the order level, Campylobacterales, Clostridiales, and Lactobacillales were highlighted as critical indicators (sFig. B). These ASVs offer valuable biomarkers for assessing the efficacy of Chrysin in treating intestinal diseases. 

To elucidate the impact of Chrysin on the metabolomic profile of mice, comprehensive non-targeted metabolomic sequencing was conducted using liquid chromatography-mass spectrometry (LC–MS). Initial principal component analysis (PCA) was performed to assess variations between and within groups, setting the stage for comparative analyses (Fig. A). Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to identify differentially expressed metabolites among the groups, with selection criteria set at VIP values > 1 in the OPLS-DA model and p -values < 0.05 in T -tests (Fig. B, C). The relationship between different metabolites in each group was depicted in a Venn diagram, revealing a total of 298 distinct metabolites, with fatty acyl changes being the most pronounced (sTable ). KEGG pathway enrichment analysis of these 298 metabolites highlighted significant alterations in pathways such as thermogenesis, choline metabolism in cancer, glycerophospholipid metabolism, nucleotide metabolism, pyrimidine metabolism, autoimmune thyroid disease, one carbon pool by folate, antifolate resistance, folate biosynthesis, and phenylalanine, tyrosine, and tryptophan biosynthesis ( p < 0.05). These pathways are crucial for regulating animal growth and development (Fig. E). The identified metabolites are associated with key metabolic processes including pentose and glucuronate interconversions, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, and folate biosynthesis (Fig. F). 

Further analysis explored the interplay between the intestinal microbiota and metabolites through Spearman correlation analysis involving 298 differential metabolites and six phylum-level bacterial groups (Fig. A, B). The analysis highlighted 20 metabolites and six bacterial groups exhibiting the most significant interactions, suggesting a robust correlation between changes in the gut microbiome and metabolic alterations. These findings indicate that the modulation of intestinal metabolites observed during Chrysin treatment for ulcerative colitis may be driven by alterations in the gut microbiota, underscoring the interconnected nature of microbial and metabolic dynamics in therapeutic responses. 

Due to the multifaceted pathogenesis of inflammatory bowel disease (IBD), the establishment of animal models using chemical inducers is a common approach to explore its pathogenesis and identify effective treatments. Among these, dextran sulfate sodium (DSS)-induced colitis is a well-accepted method to study ulcerative colitis (UC) and has been extensively utilized in research . In this study, clinical manifestations in the model group, such as shortened colon, diarrhea, hematochezia, and histopathological changes, were analogous to those observed in human UC. These symptoms were ameliorated following Chrysin treatment. Current clinical studies have verified that levels of inflammatory cytokines such as IL-6, IL-1, and TNF-α are elevated in UC patients compared to healthy individuals . In our DSS-induced model, these cytokines were also found to be elevated, mirroring the human disease state. Chrysin treatment effectively reduced the levels of IL-6, IL-1, and TNF-α to near-normal levels. Histopathological analyses using hematoxylin and eosin (HE) staining confirmed a significant reduction in inflammation in the Chrysin-treated group, illustrating Chrysin’s potent anti-inflammatory effects in UC. This evidence supports the potential of Chrysin as a therapeutic agent in managing UC by modulating key inflammatory pathways. Chrysin (5,7-dihydroxyflavone) is a naturally occurring flavonoid prevalent in various plants and known for its antitumor properties . In this study, we have expanded its potential by demonstrating that Chrysin alleviates the symptoms of dextran sodium sulfate (DSS)-induced colitis in mice, influencing intestinal metabolites through interactions with the gut microbiota, thus highlighting its multifaceted role. NF-κB, a pivotal regulator of inflammation and innate immunity, plays a crucial role in mediating interactions between the mucosal immune system and gut microbiota . Initial verification of transcriptomic predictions by Western blot confirmed the inhibition of the NF-κB signaling pathway in the colons of DSS-treated mice by Chrysin, showcasing its anti-inflammatory effects. Further transcriptomic analysis suggests that Chrysin reshapes intestinal immunity by modulating T-cell responses, notably affecting the IL-17 signaling pathway. Given that T cells are critical during the acute phase of DSS-induced colitis, where IL-17 initiates and amplifies inflammatory responses , Chrysin’s ability to significantly downregulate IL-17 expression (as evidenced by ELISA results) indicates its potential to restore immune balance and control inflammatory damage, offering a therapeutic approach to IBD. Reviewing literature, DSS treatment in mice typically results in colonic inflammation, microbiome dysregulation, and intestinal mucosal damage, with specific bacterial shifts reflective of disease progression . Post-Chrysin treatment, significant changes were observed in 50 ASVs. Predominantly, Bacteroidetes and Firmicutes dominated the fecal microbiota, with notable associations at various taxonomic levels, including genera such as Ligilactobacillus and Muribaculum , which are linked to intestinal health and inflammation regulation . Particularly, the abundance of Lactobacillales, known for their beneficial effects on intestinal barriers and inflammation, was markedly altered. Metabolomic analysis revealed significant alterations in 298 colon metabolites post-Chrysin treatment, highlighting the role of fatty acyl metabolites in regulating intestinal barriers . These metabolites are intricately connected to critical biochemical pathways such as amino sugar and nucleotide sugar metabolism and folate biosynthesis. Spearman correlation analysis further underscored significant correlations between these metabolites and bacterial groups, suggesting that Chrysin’s therapeutic effects in the IBD model may be mediated through its regulatory impact on the intestinal microbiota and barrier function. Although this study provides important insights into the therapeutic potential of Chrysin in IBD, several limitations remain. First, the DSS-induced colitis model primarily reflects the acute phase of IBD and does not fully recapitulate the chronic nature of the disease. Thus, the long-term efficacy and safety of Chrysin remain unexplored. Future studies utilizing chronic IBD models will be critical to assess the sustainability of Chrysin’s therapeutic effects and its potential side effects over extended periods. Second, though murine models are commonly used in preclinical studies, there are inherent biological differences between them and humans that may limit the clinical relevance of such findings. Future clinical studies will therefore be required to confirm the translational potential of Chrysin in human IBD patients. Overall, Chrysin emerges as a promising multi-target, multi-component microecological regulator, offering new insights and feasible strategies for preventing and treating enteric-related diseases. This comprehensive analysis establishes a strong foundation for Chrysin’s role in modulating disease pathways and supports its potential as a therapeutic agent in managing gut health and disease. 

Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 835 KB) Supplementary file2 (XLSX 10.2 KB) Supplementary file3 (XLSX 453 KB) 

