Targeting Macrophage Ferroptosis: A Mechanism of Lanxangia tsaoko methanol extract Inhibits VEGF to Attenuates ulcerative colitis

Targeting Macrophage Ferroptosis: A Mechanism of Lanxangia tsaoko methanol extract Inhibits VEGF to Attenuates ulcerative colitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeting Macrophage Ferroptosis: A Mechanism of Lanxangia tsaoko methanol extract Inhibits VEGF to Attenuates ulcerative colitis Jun Ge, Xingke Zhu, Li Cheng, XingYi Zhang, Lei Guo, Zhengzheng Wu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7446251/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lanxangia tsaoko is a kind of traditional Chinese medicine that is both food and medicine and has a long history in the treatment of gastrointestinal diseases. Recent studies have found that the methanol extract of Lanxangia tsaoko (LOM) exhibits potent anti-inflammatory and antioxidant properties. These properties align with the pathophysiological mechanisms underlying ulcerative colitis(UC), suggesting that LOM may offer a promising therapeutic avenue for UC treatment.This study aimed to identify a low-toxicity, daily edible extract of traditional Chinese medicine as a candidate drug for the prevention and treatment of UC, and to clarify the mechanism of drug treatment for the disease. Methods This study utilized UHPLC-QTOF-MS/MS, RNA-seq, and scRNA-seq analyses to determine the potential targets and mechanisms of LOM in the treatment of UC. Animal experiments, cell experiments and various molecular biology techniques were employed to evaluate the effects of LOM on UC symptoms, inflammation and ferroptosis, and to verify the related targets and pathways. Results WGCNA and immune infiltration analysis RNA-seq data identified five hub genes targeted by LOM. scRNA-seq analysis confirmed that the hub gene is mainly expressed in macrophages and affects endothelial cells to participate in disease progression through TLR4/NF-KB/VEGF. Conclusions LOM affects endothelial cells in alleviating the symptoms of UC by regulating the key and signaling pathways of macrophage hub genes. Based on the characteristic that Lanxangia tsaoko is both edible and medicinal, it has a very high potential for the prevention and treatment of UC, providing a scientific basis for its clinical application and subsequent development. Lanxangia tsaoko Ulcerative colitis UHPLC-QTOF-MS/MS Multi-omics analysis VEGF Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction UC is a complex immune-mediated inflammatory bowel disease characterized by diffuse inflammation of the colon and rectal mucosa associated with barrier dysfunction [ 1 ]. The fundamental causes of UC are multifaceted and elusive. However, increasing evidence [ 2 , 3 ] suggests that the pathogenesis of UC is closely linked to oxidative stress and inflammation, in addition to genetics, gut microbiota, the host immune system and environmental factors. Furthermore, ferroptosis, a novel iron-dependent form of regulated cell death distinct from apoptosis and autophagy, plays an important role in UC pathogenesis through its involvement in iron metabolism, lipid peroxidation, and imbalance of antioxidant systems [ 4 ]. Currently, the clinical treatment of UC predominantly relies on pharmacotherapy, including aminosalicylates, glucocorticoids, immunosuppressants, and biologics[ 5 ], which are associated with significant side effects and prolonged treatment periods. Therefore, the exploration of herb with homology of medicine and food that are safe, effective, and readily extractable has emerged as a new direction for treating UC. In Traditional Chinese Medicine, UC is categorized under conditions such as “xia li” (diarrhea) and “xie xie” (loose stools), which are typically attributed to a combination of constitutional deficiencies, weakened spleen Qi, and the external invasion of dampness, often aggravated by irregular diet and lifestyle [ 6 ]. Lanxangia tsaoko (Crevost & Lemarié) M.F.Newman & Škorničk.( On May 1, 2025, the name was verified by consulting the “World Flora Online”) is a traditional Chinese medicine that is both medicinal and culinary. It has low toxicity and is used both as a therapeutic agent and as a food seasoning due to its unique pungent aroma. Its medicinal and culinary values are widely recognized, and its low toxicity and diverse functionalities make it an important component in both traditional Chinese medicine and everyday diets. Lanxangia tsaoko , with its warm nature and affinity for the spleen and stomach meridians, is renowned for its ability to dispel dampness and warm these organs, and is commonly used in combination with other herbs to treat gastrointestinal disorders [ 7 , 8 ]. From the perspective of Traditional Chinese Medicine, Lanxangia tsaoko shows considerable therapeutic potential in the treatment of UC. Previous studies[ 7 ] have reported that Lanxangia tsaoko contains abundant essential oils, terpenoids, flavonoids, and phenolic compounds. Using bibliometric methods, it was found that about 500 monomeric compounds have been characterised from the alcoholic extract of Lanxangia tsaoko , among which flavonoids and polyphenols are more prevalent. Moreover, the ethanol extract of Lanxangia tsaoko is rich in flavonoids and polyphenolic compounds, exhibiting potent anti-inflammatory and antioxidant activities, which align with the pathogenesis of UC. This study aimed to comprehensively investigate the therapeutic effects and mechanisms of LOM in UC using integrated multi-omics approaches, including transcriptomics and single-cell analyses. By exploring the regulatory effects of LOM on key signaling pathways and hub genes, we seek to provide a scientific basis for its clinical application. The findings of this study are expected to contribute to the development of novel therapeutic strategies for UC and offer insights into the mechanisms underlying the efficacy of natural products in inflammatory bowel diseases. 2. Materials and methods 2.1.Animals and animal model Sixty SPF-grade male mice, aged 5 weeks and weighing 25–27 g, were obtained from Hunan Silek Jingda Laboratory Animal Co., Ltd. (Production License No.: SCXK (Xiang) 2021-0002, Quality Certificate No.: 430727241102279125). The research protocol was approved by the Ethics Committee of the Animal Experimentation Center, issuance number 2025090P, ensuring that all procedures were carried out in strict compliance with the European Community guidelines, thereby meeting all ethical standards. Throughout the study, except for special conditions required for animal model establishment, all mice were kept in a temperature - and humidity - controlled room (21–22 ℃, 55 ± 5%) with a 12 h light-dark cycle, and had free access to food and deionized water.After one week of adaptive feeding, the mice were randomly divided into several groups: a control group, a model group (3% DSS), low -, medium -, and high - dose LOM treatment groups (50, 100, 200 µg/mL), and a mesalazine - treated group (MMX). The blank group was given deionized water for 9 days, while the model group, LOM groups, and MMX group were provided with 3% DSS in drinking water for 9 days. Notably, on the third day, the LOM and MMX groups were treated with the corresponding drugs via gavage.During the experiment, the weight of each mouse was recorded daily, and the severity of colitis was assessed based on stool consistency, presence of bloody stool, and weight loss. By combining individual scores, the disease activity index (DAI) for each mouse was calculated daily according to Supplementary Table 1, using the formula: \(\:DAI\:=\:(S1\:+\:S2\:+\:S3)/3\:\) [9].At the end of the experiment, the mice were euthanized under anesthesia, their weights were measured, blood samples were collected for serum preparation, colons were dissected out, quickly frozen in liquid nitrogen, and stored at -80 ℃ for subsequent analysis. 2.2.Materials Mouse TNF-α ELISA Kit (Catalog No. MU30030) and Mouse IL-10 ELISA Kit (MU30055) were purchased from Wuhan Beinle Biotech Co., Ltd.; CCK-8 assay kit, NO detection kit (Lot No.: 112223240219), Fe 2+ content detection kit (Catalog No; BC5415), DSS(C16458324), Fecal occult blood kit(Lot.NO.20240628),ROS detection kit (BL714A) were purchased from Wuhan Hongrui Biotech Co., Ltd.; LPS (GC205009-10 mg), BCA protein concentration assay kit (Lot No.: G2026-1000T), HRP-conjugated goat anti-rabbit IgG (Lot No: GB23303), hypersensitive ECL chemiluminescence detection (G2020-50 mL), 50×Cocktail protease inhibitor (CR2211028), ice-free rapid transfer buffer (powder, Lot No: G2028-1 L), electrophoresis buffer (powder, Lot No: G2018-2 L), 0.45 µM PVDF membrane (Lot No: F2203402), prestained protein marker IV (8-200 ku, G2083-250 uL), bovine serum albumin BSA (Lot No: CR2204075), DMEM/High Glucose GlutaMax-I supplement (Lot No: G4511) were purchased from Wuhan Sevier Biotech Co., Ltd.; Lanxangia tsaoko (Lot No:191101;Dried mature fruit identified by Professor Yang Hongbing of the School of Pharmacy, Hubei University of Chinese Medicine, and produced by Hubei Chenmei Pharmaceutical Co., Ltd.); The Milli-Q water system was used to produce deionized water in this study (Millipore, Bedford, MA, USA); Methanol, acetonitrile, and formic acid were all obtained from Merck KGaA (Darmstadt, Germany). 2.3.Preparation of the methanol extract Lanxangia tsaoko was crushed and reflux-extracted with petroleum ether (1:10) for 2 h to eliminate fat. Afterward, Lanxangia tsaoko was air-dried at room temperature. The dried material was subsequently reflux-extracted twice with methanol (1:10), each extraction lasting 2 h. The resulting methanol extracts were combined, filtered, and concentrated under reduced pressure. Finally, the concentrated solution was freeze-dried to obtain a dry powder, referred to as LOM. 2.4.Cell culture and grouping Caco2 and RAW264.7 cells, derived from Professor Yi Liu, Director of the Graduate Program in Medicinal Chemistry at Hubei University of Traditional Chinese Medicine.Caco2 cellswere cultured in high-glucose DMEM supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 mg/L streptomycin. RAW264.7 were cultured with specialized medium.The cultures of two cells were maintained at 37℃ in a 5% CO 2 humidified atmosphere. Caco2 cells were seeded in DMEM medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, and cultured at 37℃ in 5% CO 2 . The culture medium was changed every 2 days. Cells in logarithmic growth phase were selected and randomly divided into blank control group(Con), model group(Mod), and drug-treated groups with different concentration gradients (10, 20, 40,80,100 µg/mL). The drug concentrations were determined based on literature[ 10 , 11 ]and preliminary experiments. After stimulating the model and drug-treated groups with 20 µg/mL LPS for 24 h to induce modeling, cells were treated with various concentrations of LOM for 24 h. The RAW264.7 cells were seeded in their specific medium, and both cell types were cultured under conditions of 37°C and 5% CO2, with the medium changed every 2 days. Cells in the logarithmic growth phase were harvested and randomly divided into a control group and different concentration gradient groups treated with LOM (10, 20, 40, 80 µg/mL). The concentrations of the drug were derived from literature and preliminary experiments. 2.5. Cell co-culture The logarithmically growing RAW264.7 cells were added to the upper chamber of the Transwell, and logarithmically growing Caco2 cells were added to the lower chamber to simulate the co-culture environment of RAW264.7 and Caco2. The cells were randomly divided into control group, model group, and low, medium, and high LOM groups (10, 20, 40 mg·L^-1). The low, medium, and high LOM groups were cultured for 24 hours to establish the co-culture model, followed by treatment with different doses of LOM for an additional 24 hours; the control group was directly cultured with 100% of the RAW264.7 specific medium for 48 hours under normal conditions. 2.6.Cell proliferation assay Cell proliferation was assessed using the CCK-8 assay. Caco2 cells in logarithmic growth phase were seeded at a density of 5×10 6 cells/mL in 96-well plates. Cells were grouped according to the method described in section “ Cell Culture and Grouping”(with 3 replicate wells per group). Subsequently, 10 µL of CCK-8 reagent was added to each well, and PBS was added to the outermost wells of the 96-well plate to seal it. The plate was then incubated at 37℃ for 1.5 h. The optical density (OD) values of each well were measured at a wavelength of 450 nm using an enzyme-linked immunosorbent assay (ELISA) reader to reflect the proliferation capacity of the cells. 2.7.Enzyme-Linked Immunosorbent Assay (ELISA) After incubating the blank control group, model group, and drug-treated groups at a constant temperature for 48 h, supernatants were collected and centrifuged at 4℃, 5,000 rpm for 10 min to remove insoluble materials. The supernatants were filtered through a 0.22 µM microporous membrane. Following the manufacturer's instructions, the levels of TNF-α, IL-10, and NO in the supernatants of the blank control group, model group, and various drug-treated groups were detected using ELISA. 2.8.Detection of Fe 2+ Fe 2+ levels in Caco2 cells were detected using an Fe 2+ content detection kit. Cells were grouped according to the method described in section “ Cell Culture and Grouping” (with 3 replicate wells per group) and treated accordingly. Following removal of the culture medium, 1 mL of reagent 1 was added, and cells were subjected to ice-cold homogenization. The homogenates were then centrifuged at 4℃ and 10,000 rpm for 10 min, and the supernatants were collected and processed according to the manufacturer's instructions. Finally, optical density (OD) values at 593 nm were measured using an ELISA reader to quantify intracellular Fe 2+ levels in each well. 2.9.UHPLC-QTOF-MS/MS analysis conditions The ACQUITY UPLC M − Class system was used for UHPLC-MS/MS analysis. A Waters ACQUITY UPLC BEH C 18 column (100 × 2.1 mM, 1.7 µM) was used for separation. The injection volume was 2.0 µL and the flow rate was 0.3 mL/min. Mobile phase A was water-formic acid (1000:1,v/v) and mobile phase B was acetonitrile. The following binary gradient with linear interpolation was used: 0.01 min, 10% B; 5 min, 20% B; 22 min, 80% B; 27 min, 100% B; 30min, 100% B; 32min, 10%B; 35 min, 10% B. The column oven and autosampler temperatures were maintained at 30 ℃ and 5 ℃, respectively. The Waters Xevo G2-XS QTof mass spectrometer was operated in the electrospray ionization (ESI) mode (Waters, Milford, MA, USA). The positive ion electrospray was selected for data acquisition. The optimized operating parameters were set: cone gas flow, 50 L/h; capillary voltage, 3.0 kV; source temperature, 100 ℃; cone voltage, 20 V; desolvation temperature, 500 ℃; desolvation gas flow, 1000 L/h. The mass ranges were set at m/z 100–1500 for full scan, with scan duration of 1 s. The MSE continuum mode was used to collect data. 2.10.Data Acquisition and Processing From the NCBI Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/GEO/ ), we selected the GSE87466 and GSE3365 datasets for annotation via the GPL13158 Affymetrix chip platform, matching GSE87466 gene probe IDs to “Gene symbols”, and the GPL570 Affymetrix chip platform for annotation, matching GSE3365 gene probe IDs to “Gene symbols”. Similarly, we obtained single-cell RNA sequencing (scRNA-seq) data on ulcerative colitis (UC) from the GEO database: GSE214695. When processing the scRNA-seq data, we retained high-quality cells with mitochondrial gene content below 20% and over 200 expressed genes, focusing on genes with expression levels between 200 and 7000 and active in at least three cells. Subsequently, we performed data integration using the Seurat pipeline [ 12 ]. We standardized and normalized the remaining cells using the “Log-normalization” method and a linear regression model, and detected the most variable genes via the “FindVariablFeatures” function. Then, we reduced the dimensionality of the scRNA-seq data through principal component analysis (PCA) and used the R package “single R” for UMAP dimensionality reduction, dataset integration, and cell type annotation. To eliminate batch effects between samples, we performed soft k-means clustering using the “Harmony” package [ 13 ]. Cell clustering was accomplished with the “FindClusters” function. Cell cluster annotation involved examining highly expressed genes, genes with unique expression patterns, and established classic cell markers [ 14 ]. 2.11.Obtaining Intersection Genes The R package “WGCNA” was used to construct a co-expression network [ 15 ]. Hierarchical clustering analysis of the gene expression data in GSE87466 was performed to identify outliers. In this study, the soft threshold was set at 11. The weighted adjacency matrix was transformed into a topological overlap measure (TOM) matrix to assess connectivity within the network. The average linkage hierarchical clustering method was applied to construct the clustering tree of the TOM matrix. Here, the minimum gene module size was set at 50 to obtain suitable modules, and the threshold for merging similar modules was set at 0.5. Gene significance (GS) and module membership (MM) were calculated to correlate modules with clinical traits. For the identified modules, genes from the two modules most related to UC were selected based on their correlation coefficient r and P-value.Genes related to UC were gathered from databases like CTD ( http://ctdbase.org/ ) and GeneCards ( https://www.genecards.org/ ), focusing on those with counts over 10,000 and above the median. Ferroptosis-related genes, including markers, inhibitors, and drivers, were sourced from FerrDb ( http://zhounan.org/ferrdb ). LOM compounds were identified via UHPLC-QTOF-MS/MS, selecting those with high gastrointestinal absorption scores and meeting at least two “Yes” criteria for drug-likeness on Swiss ADME ( http://www.swissadme.ch/index.php ). The SMILES strings of these compounds were input into the SwissTargetPrediction platform ( http://www.swisstargetprediction.ch/ ) to predict their targets. Then, in R 4.2.1, intersection analysis of ferroptosis, drug, and disease-related genes was performed using the “ggplot2” and “VennDiagram” packages, with results visualized using the “ComplexHeatmap” package. 2.12.Hub genes Selection We constructed a protein - protein interaction (PPI) network based on the STRING database ( https://string-db.org/ ). Also, in Cytoscape 3.10.1, we used the cytoHubba plugin's MCC algorithm to calculate the top 5 genes, which we designated as hub genes. 2.13.Immune Infiltration Analysis To enhance the ability to identify disease heterogeneity, clinical subtypes, and molecular characteristics, the “ConsensusClusterPlus” package was used. We performed unsupervised consensus clustering analysis based on hub genes' expression for disease samples in GSE87466 using the PAM clustering method.The “gsva” package was utilized to calculate immune cell infiltration scores and the activity levels of 13 immune functions for both C1 and C2 groups across all samples and specifically within disease samples. Heatmaps depicting the associations between hub genes, immune cells, and the 13 immune functions were generated using the ggplot2 package. 2.14.Gene set scoring algorithm in scRNA-seq We used six algorithms from the “irGSEA” package to score hub genes in the scRNA-seq dataset: AUCell, UCell, singscore, ssgsea, JASMINE, and viper. AUCell and UCell were chosen for their unique ability to quantify gene set activity at the single-cell level, which is crucial for accurately identifying activation patterns in UC cells. AUCell calculates gene set activity in each cell by determining the area under the cumulative distribution curve of gene expression ranks[ 16 ]. UCell assesses gene set activity by computing and normalizing rank scores within single-cell gene expression rankings[ 17 ]. Singscore ranks genes in each cell for a given gene set and calculates the average rank score, based on the difference between the average ranks of positive and negative genes[ 18 ]. Ssgsea determines gene set enrichment by calculating a relative enrichment score that compares the expression values of genes in a set to those of other genes[ 19 ]. JASMINE calculates an approximate average based on gene rankings in expressed genes and the enrichment of gene sets in expressed genes within a single cell, then combines these averages to derive the final gene set enrichment score[ 20 ]. Viper estimates gene set enrichment scores by performing a three-tailed calculation based on gene expression rankings across cells[ 21 ]. 2.15.Differential gene expression and functional enrichment analysis Macrophages were divided into M_HIGH and M_LOW subgroups based on the average AUCell score. The “FindMarkers” function identified differentially expressed genes (DEGs) between these two groups[ 14 ]. For DEGs, GO and KEGG pathway enrichment analyses were performed using the “clusterProfiler” package in R 4.2.1, with visualization done via the “ggplot2” package. 2.16.Pseudo-time analysis The Monocle3 package was used to conduct reverse chronological analysis[ 22 ], aimed at reconstructing the developmental trajectory of cells based on single-cell gene expression data. This intricate process entailed constructing a single-cell expression matrix, categorizing cells into distinct developmental states, and delineating cell developmental trajectories by discerning gene expression patterns. We also evaluated cell maturity or developmental status utilizing the Cytotrace and Diffusion Map method. 2.17.Cell communication The “CellChat” package was used to analyze gene expression data and explore changes in potential cell - cell communication networks. Using the standard CellChat pipeline, we relied on the default CellChatDB for ligand - receptor interactions. By identifying overexpressed ligands or receptors within specific cell populations, we inferred cell - type - specific interactions. Moreover, we used the R package “Nichenet” [ 23 ] to gain deeper insights into the complex relationships between cell-cell communication and gene expression, revealing the roles and interactions of cells and genes in various biological processes. 2.18.H&E staining analysi First, tissue embedding: appropriate colon tissue was embedded in a cassette, labeled with tissue information, rinsed with tap water for half an hour, then transferred to 70%, 80%, 90%, and 95% alcohol for 1 hour each, followed by absolute ethanol for 2 hours, xylene for 20 minutes, and finally immersed in wax for 3 hours before embedding. The section thickness was 5 µm. Briefly, nuclear cell staining with hematoxylin solution for 2 minutes, eosin staining for 1 minute, water rinsing for 5 minutes, then dehydration through 70%, 80%, 90%, 95%, and 100% alcohol for 30 seconds each, and xylene for 10 minutes. 2.19.Western Blot Analysis (WB) Cells were lysed with RIPA lysis buffer containing protease inhibitors on ice for 30 min to extract total proteins. The lysates were centrifuged at 4 ℃, 12,000 rpm for 10 min, and the supernatants were collected. Protein concentrations were determined using a BCA protein quantification kit. Total proteins were separated by 10% SDS-PAGE gel electrophoresis, and then transferred onto a polyvinylidene fluoride (PVDF) membrane using a wet transfer method. The PVDF membrane was blocked with 5% skim milk at room temperature for 2 h, followed by removal of the blocking solution and washing with TBST three times (10 min each). The membrane was then incubated with primary antibodies overnight at 4 ℃, followed by three washes with TBST (10 min each) after antibody retrieval. Subsequently, the membrane was incubated with HRP-conjugated goat anti-rabbit IgG secondary antibody (1:10,000) at room temperature for 1 h and washed three times with TBST (10 min each). Protein bands were visualized using the highly sensitive ECL chemiluminescence method, and exposed and developed using Image J software for quantitative analysis of protein band grayscale values with β-actin as an internal reference. 2.20.Statistical Methods ImageJ software and GraphPad Prism 9.0 were used for statistical analysis. Results are presented as X ± S, and intergroup comparisons were performed using t-tests. 3. Results 3.1. LOM relieved the symptoms of DSS-induced UC in mice The DSS-induced mouse UC model is widely used and closely mimics human UC pathology. During the experiment, the 3% DSS group showed sustained weight loss compared to the normal group. However, the LOM treatment group significantly alleviated this weight loss (Fig. 1 B). Additionally, the LOM group had a marked increase in colon length and a reduction in DAI scores compared to the 3% DSS group, indicating relief from weight loss, diarrhea, and bloody stools (Figs. 1 C-D). Compared to the normal group, the DSS group had significantly increased TNF-α levels and decreased IL-10 levels, and LOM treatment inhibited these changes (Figs. 1 E-F) .DSS caused loss of crypt glands, mucosal damage, and inflammatory cell infiltration in the colon (Fig. 1 G). H&E staining showed that, compared to the normal group, the 3% DSS group had these pathological changes, while the LOM group significantly improved them (Fig. 1 G). Thus, LOM alleviated the histological damage and inflammatory response in UC. 3.2.LOM Inhibited LPS-Induced Inflammation and ferroptosis Recent studies have observed hallmark signs of ferroptosis—such as iron deposition and lipid peroxide accumulation—during the onset of disease in mouse models of ulcerative colitis [ 24 ]. Accumulating evidence [ 25 , 26 ] indicated that pharmacological modulation of ferroptosis attenuated experimental ulcerative colitis, positioning this pathway as a compelling therapeutic target. Whether LOM mediates its protective effects via ferroptosis remains unknown. We therefore employed in vitro models to evaluate LOM’s ability to regulate ferroptosis and to quantify its influence on critical inflammatory mediators. As illustrated in Fig. 2 A, LOM at 50, 100, and 200 µg/mL exerted no significant effect on cell viability; therefore, these concentrations were selected for subsequent experiments. Figures 2 B-D showed that, compared to the control group, the model group had significantly elevated levels of NO and TNF-α, while the anti-inflammatory factor IL-10 was markedly downregulated. LOM treatment significantly reduced TNF-α and NO levels and increased IL-10 levels. Figure 2 E demonstrated that LOM significantly reversed the LPS-induced increase in Fe 2+ levels. Nrf2 is a key regulator of lipid peroxidation and ferroptosis, with many proteins and enzymes that prevent lipid peroxidation and thus trigger ferroptosis being Nrf2 target genes [ 27 ]. HO-1, a target gene of Nrf2, may offer protection, but its excessive accumulation and activation can lead to ferroptosis [ 28 ]. As shown in Figs. 2 F-G, LOM significantly upregulated the relative expression levels of Nrf2 and HO-1 compared to the model group. These findings indicated that LOM can markedly inhibit inflammation and ferroptosis, suggesting its potential for UC treatment. 3.3.UHPLC-QTOF-MS/MS Analysis The compounds of LOM were analyzed using UHPLC-QTOF-MS/MS with MassLynx 4.1 (Waters, USA) as the data acquisition software, identifying a total of 141compounds, predominantly flavonoids and polyphenol, as shown in Figs. 3 A-C. Specific details of these compounds can be found in Supplementary Table 2. We identified the compounds that met the screening criteria under the “Acquisition of Intersection Genes”section and marked them with an asterisk (*).During identification, the mass spectrum data were matched with the combined mass spectrum information in the database (HMDB ( https://hmdb.ca/ ), COCOUNT ( https://coconut.naturalproducts.net/ ), NANPDB ( https://african-compounds.org/anpdb/ ), etc.), and preliminary screening was conducted according to the excimolecular ion peaks and element compositions, and further confirmation was conducted according to the primary and secondary information of each chromatographic peak. 3.4.Construction of weighted gene coexpression networks and Hub Gene Selection To screen out the key genes of UC targeted by LOM, we used the GSE 87466 dataset. In this study, WGCNA clustered UC-related highly correlated genes. We chose 11 as the soft threshold (R² = 0.719) to build a scale - free network (Fig. 4 A), and then merged modules per the cutoff, screening out 10 co - expression modules (Fig. 4 B). Module correlation analysis revealed Megrey60 (cor = 0.71, p = 5e − 18) and MEtan (cor = 0.67, p = 2e − 15) had the highest correlation with UC. Consequently, the 3,126 genes in Megrey60 and MEtan were taken as target genes (Fig. 4 C). By intersecting WGCNA - related genes with ferroptosis - related, LOM - related, and UC - related genes, we obtained 31 intersection genes (Fig. 4 D). Using the MCC algorithm, the top five intersection genes were identified as TLR4, HIF1A, IL1B, STAT3, and TNF (Fig. 4 D). 3.5.Construction of the ROC Diagnostic Model To evaluate the diagnostic accuracy of the five core genes in predicting ulcerative colitis (UC) - related outcomes, we constructed a ROC curve analysis. Genes with an area under the curve (AUC) > 0.7 are considered to have significant predictive value. In the GSE87466 dataset, TLR4 (AUC = 0.755), HIF1A (AUC = 0.950), IL1B (AUC = 0.966), TNF (AUC = 0.817), and STAT3 (AUC = 0.882) all showed significant predictive value (Fig. 5 A). Based on logistic regression analysis of these five genes, we constructed a diagnostic nomogram (Fig. 5 B) and further developed a ROC diagnostic model. The ROC curve of combination with 5 genes in the training dataset (GSE87466) showed an AUC of 0.933, and in the testing dataset (GSE3365), the AUC was 0.891 (Fig. 5 C), confirming the high predictive value of hub genes for UC. 3.6Immune Infiltration Analysis We performed consensus clustering analysis on UC samples from GSE87466. The results showed the classification was highly reliable and stable when k = 2, so we divided the UC samples into subgroups C1 and C2 (Supplementary Fig. 1A). In C1, the expression levels of aDC, B cells, CD8 T cells, cytotoxic cells, Macrophages, T cells, T helper cells, Tcm, TFH, and Th1 cells were higher than in C2 (Supplementary Fig. 1B). Interestingly, the expression level of Th17 cells in C2 was higher than in C1 (Supplementary Fig. 1B). The correlation analysis showed the expression of hub genes was closely related to the activation of Macrophages, aDC, and Th1 cells (Supplementary Fig. 1C). Moreover, C1 patients had significantly higher expression of multiple immune pathways than C2 patients (Supplementary Fig. 1D). The correlation analysis showed the expression of hub genes was associated with the activation of parainflammation and CCR pathwaysSupplementary Fig. 1E). The immune infiltration analysis indicated that the pathological mechanism of UC is closely related to the human immune system. 3.7.The scRNA-seq profiling of UC Bulk transcriptome sequencing has certain limitations and cannot characterize on which cells genes are expressed. Therefore, we utilized single-cell technology to identify cells expressing five core genes for the study of the mechanism by which LOM alleviates UC. Prior to further analysis, quality control of sample data was performed (Fig. 6 A), and batch effect correction was applied. Results showed a relatively stable overall distribution with low sensitivity to batch effects (Fig. 6 B). Cells were divided into 11 subgroups and annotated based on marker genes (Fig. 6 C), with cell types determined by specific marker genes (Fig. 6 D). The distribution of hub genes across different cell subgroups is shown (Fig. 6 E). Using algorithms such as AUCell, UCell, singscore, ssgsea, JASMINE, and viper, we analyzed the expression of hub genes in different celltypes. As shown in Fig. 6 F, the five core genes are mainly expressed on macrophages, which also determines the direction for our subsequent research. 3.8.Trajectory analysis of macrophage differentiation and development To elucidate the biological roles of the hub genes on macrophage, we stratified macrophages into two functionally distinct subgroups—M_HIGH and M_LOW, based on AUCell-derived gene-set activity scores. UMAP visualizations and violin plots confirmed pronounced enrichment of all five hub genes in the M_HIGH subset (Fig. 7 A). Unsupervised trajectory inference with Monocle 3 revealed a continuous pseudotime trajectory in which macrophages progressively transition from M_LOW to M_HIGH, with M_HIGH occupying the distal end, indicative of a terminally differentiated state (Fig. 7 B). CytoTRACE analysis independently corroborated this finding, assigning the lowest differentiation potential to M_HIGH and positioning M_LOW at the trajectory origin (Figs. 7 C-D). Collectively, these data establish M_HIGH as the terminal differentiation node of macrophages and suggest that the abundance of this subgroup increased during UC progression. The distinct transcriptional program of M_HIGH was intimately associated with UC pathogenesis, and the elevated expression of the hub genes within this subset not only reinforces their identity as key UC drivers but also implicates them in orchestrating terminal macrophage differentiation and functional polarization, thereby driving immune dysregulation and tissue injury in UC. 3.9.Cell Communication Analysis To elucidate the mechanism of hub genes, we performed a KEGG pathway enrichment analysis on differentially expressed genes between M_HIGH and M_LOW macrophage subgroups. The results indicated that the occurrence of UC is closely associated with the significant activation of multiple signaling pathways, including the MAPK, NF-κB, TNF, and Toll-like receptor pathways, which were highly relevant to inflammation. The genes involved in these pathways significantly overlaped with our core genes of interest. Based on the pivotal role of these pathways in inflammation and the marked enrichment of core genes, we further analyzed exactly by which intercellular communications M-HIGH regulates to affect the pathological progression of UC. Consequently, we systematically assessed the differences in communication in cell types. The findings revealed that M_HIGH macrophages exhibit significantly higher signal input and output intensities compared to other cells (Fig. 8 B), with a notably stronger communication strength than M_LOW macrophages. Given the pronounced advantage of M_HIGH macrophages in signal transduction, we further investigated their specific role within the intercellular communication network, particularly their interactions with key cell types and how these interactions affect the pathogenesis of UC. As shown in 8C, M-HIGH macrophages mainly have a regulatory relationship with endothelial cells. And the VEGF pathway is the key for M-HIGH macrophages to regulate endothelial cells (Fig. 8 D). Further analysis using the NicheNet algorithm got the same result (Fig. 8 E). The above results suggest that M-HIG promotes the progression of UC by stimulating endothelial cells by secreting VEGF. 3.10. In Vivo and In Vitro Validation VEGF is a cytokine that promotes angiogenesis in endothelial cells, and enteritis is accompanied by a large number of new blood vessels [ 29 , 30 ]. As is well established, NF-κB is a critical family of transcription factors that regulate inflammation and immune responses by controlling the expression of a plethora of downstream target genes in response to environmental changes [ 31 ]. Existing studies have shown that the activation of NF-κB can modulate VEGFA expression [ 32 , 33 ]. And STAT3 is a classic downstream pathway of NF-κB. At first, we conducted in vitro experiments. As shown in Fig. 9 A, the viability of RAW264.7 cells remained unaffected across a range of LOM treatment concentrations from 10 to 80 µg/mL, indicating no influence on cell activity. The co-culture system of LPS-Caco2 and RAW264.7 was constructed to evaluate the effect of LOM on macrophages. After the co-culture was completed, RAW264.7 cells were collected for the WB experiment. The expression of protein level of TLR4, HIF1A, p-STAT3 and p-NF-κB (p-p65) were significantly increased under co-culture, and were markedly reversed by LOM treatment, as illustrated in Figs. 9 B-F. To further confirm the conclusion, we verified hub genes and NF-κB with specimens from Fig. 1 . Results showed that after 3% DSS treatment, the relative protein expression levels of TLR4, HIF1A, and IL-1β in colon tissue were significantly increased, and the phosphorylation of NF-κB P65 and STAT3 was activated (Figs. 10 A-F). LOM, however, markedly reversed these effects. In summary, LOM combats UC by inhibiting the TLR4/NF-κB pathway, HIF1A expression, and STAT3 phosphorylation, thus reducing inflammation, oxidation, and ferroptosis. We detected the expression of VEGF and found that VEGFA was highly expressed in the model group, while LOM significantly reversed this high expression (Figs. 10 G-H). The above results confirm the conclusion of the single-cell analysis: Macrophages participate in the development process of UC by secreting VEGF through activating the NF-κB/STAT3 pathway, while LOM can reverse this phenomenon. 4. Discussion UC is a chronic non-specific inflammatory disease of the intestinal tract, often associated with shortened life expectancy in affected individuals and significantly increased risk of colorectal cancer in advanced stages. Therefore, identifying key biomarkers of UC to develop safer and more effective therapies remains a pressing research priority. Currently, approved drugs for UC mainly include antibiotics, aminosalicylates, and glucocorticoids, but their use is restricted due to severe adverse reactions, drug resistance, and lengthy treatment durations [ 34 ]. Ferroptosis, a newly discovered regulated form of cell death, is characterized by iron-dependent lipid peroxidation [ 35 ], which has been implicated in the pathogenesis of many diseases [ 36 – 38 ]. Increasing evidence suggests that the pathogenesis of UC is associated with ferroptosis [ 4 ].Studies have increasingly highlighted the close relationship between mitochondria and ferroptosis. Imbalance in iron homeostasis not only triggers ferroptosis but also constitutes a crucial factor leading to mitochondrial dysfunction[ 39 – 41 ]. Diseases typically caused by mitochondrial dysfunction are attributed to excessive production of free radicals within mitochondria, which may be exacerbated by disrupted iron homeostasis, thereby increasing oxidative stress and further impairing mitochondrial function [ 42 – 44 ]. In recent years, the application of Traditional Chinese Medicine in the treatment of UC has become increasingly prominent [ 45 ]. Amomum tsao-ko, as a traditional Chinese medicine with dual food-medicine origins, is commonly employed to treat malaria, dyspepsia, gastric disorders, and diarrhea [ 46 ]. In this study, we demonstrated through transcriptome, single-cell, and in vitro experiments that LOM regulates macrophage interaction with vascular endothelial cells through the ferroptosis pathway for the treatment of colitis. In vivo experiments, LOM significantly improved diarrhea and rectal bleeding in mice and effectively improved colonic pathology, suggesting that Lanxangia tsaoko is a highly potential drug for the treatment of UC. To further clarify the key targets and pathways of LOM in the treatment of UC, we employed HPLC-MS, bioinformatics, and in vivo and in vitro experiments. At first, 141 chemical components of LOM were characterized by using UHPLC-QTOF-MS/MS. By integrating WGCNA analysis, compound target genes from public databases, differentially expressed genes from transcriptomic analysis, and genes associated with the ferroptosis pathway, we successfully identified five hub genes: TLR4, HIF1A, IL1B, STAT3, and TNF. The ROC results of the combined analysis of the five hub genes also confirmed its good predictive value and its important role in the progression of UC. TLR4, central to innate immune signaling, coordinates inflammatory responses by influencing transcription factor activity. Changes in TLR4 activity regulate NF-κB and MAPK activation through both MyD88-dependent and independent pathways, thereby impacting the progression of various diseases by altering the expression of pro-inflammatory cytokines such as IL-1β, IL-6, TNF-α, and type I interferons [ 47 ].In UC development, excessive ROS accumulation and disruption of the colonic epithelial barrier lead to inflammatory factor release [ 48 ]. NF-κB pathway activation results in the secretion of inflammatory cytokines like IL-1β, IL-6, TNF-α, and IFN-γ, which are crucial for maintaining gastrointestinal anti-inflammatory homeostasis [ 49 ]. IL-6, by activating the STAT3 signaling pathway, regulates the recruitment of myeloid cells and neutrophils to inflammatory sites and promotes Th17 cell differentiation. The IL-6/STAT3 signaling axis is a key regulator in intestinal inflammation and plays a critical role in the transition from intestinal inflammation to cancer [ 50 ]. Based on hub genes, UC disease samples were divided into two subgroups. Immune infiltration analysis showed that the expression of hub genes is related to various immune cells and pathways. To further clarify the mechanism by which LOM alleviates UC, we characterized these five core genes using a single-cell dataset. scRNA-seq data was identified 11 major celltypes. Various algorithms showed that hub genes were highly expressed on macrophages. Macrophages, abundant in the colon, play a pivotal role in modulating local mucosal immune responses through their phenotypic diversity. As integral components of the immune system, macrophages exhibit plasticity, polarizing into either the M1 (pro-inflammatory) or M2 (anti-inflammatory) phenotype in response to diverse stimuli. Persistent activation of M1 macrophages triggers an excessive release of pro-inflammatory cytokines, disrupting colonic homeostasis and compromising the barrier function, which in turn amplifies intestinal inflammation. Conversely, M2 macrophages mitigate UC progression by secreting anti-inflammatory cytokines [ 51 , 52 ]. In the pathogenesis of UC, overactivation of M1 macrophages leads to the production of pro-inflammatory cytokines like TNF-α, IL-1β, and IL-6, exacerbating intestinal inflammation. Glycolysis, a key metabolic pathway in M1 macrophages [ 53 ], is essential for their function. Inhibiting glycolysis significantly impacts typical inflammatory functions, including phagocytosis, ROS production, and the secretion of pro-inflammatory cytokines [ 54 , 55 ]. This metabolic process is HIF1A-dependent, and in macrophages, the TLR/NF-κB signaling pathway can regulate HIF1A transcription in an oxygen-independent manner [ 56 ]. Various inflammatory signals ultimately converge on NF-κB activation, a master regulator of macrophage function that modulates HIF1A gene expression [ 57 , 58 ].HIF1A also contributes to ferroptosis by elevating ROS and depleting glutathione (GSH). To further explore the pathological mechanism of macrophages in UC, we divided macrophages into M_HIGH and M_LOW subtypes based on the median expression of hub genes. Notably, hub genes were highly expressed in M_HIGH, which was the terminal differentiation stage of macrophages and closely related to disease occurrence. In cell communication analysis, M_HIGH showed extremely high communication strength with endothelial cells, and VEGFA is the key important cytokine. VEGFA is key for angiogenesis and vascular permeability [ 59 ], both essential for inflammation and tissue repair. Research [ 60 ] indicates that the HIF1A/VEGF signaling pathway is implicated in tumor immunity, inflammation, ischemia-reperfusion injury, oxidative stress, and other angiogenesis-related processes, and is closely related to the pathological mechanism of UC. The interplay between macrophages and endothelial cells is significant, as M1 macrophages perpetuate inflammation through cytokine secretion, endothelial cell activation, and the recruitment of additional immune cells to the inflamed sites [ 53 ]. Finally, it was confirmed in an in vitro co-culture system that LPS-Caco2 cells could significantly activate TLR4/NF-κB in macrophages, induces STAT3 phosphorylation and increases the expression of HIF1A and related inflammatory factors TNF-α and IL-1β, while LOM can reverse this phenomenon. Meanwhile, the same results were verified in the specimens of animal models. Moreover, the histochemical results showed that VEGF was highly expressed in the model group, and LOM could also inhibit its expression. Therefore, our research results confirm that ferroptosis is indeed involved in the development process of UC and coordinates the interaction between macrophages and endothelial cells with core genes as the central nodes of the regulatory network. In summary, this study combined the chemical analysis of UPLC-MS with transcriptomics (RNA-seq) and single-cell (scRNA-seq) data to demonstrate that LOM exerts therapeutic effects by inhibiting the central gene regulation of the TLR4/NF-κB/VEGF pathway in macrophages, thereby alleviating UC symptoms and inflammatory responses. 5. Conclusions This study, based on UHPLC-QTOF-MS/MS and multi-omics analysis methods, explored the therapeutic potential of LOM in UC treatment through in vitro and in vivo experiments. Given that Lanxangia tsaoko is a traditional Chinese medicine that is both edible and medicinal, the results of this study suggest that daily consumption of Lanxangia tsaoko is beneficial for the prevention and treatment of UC, and also provide new ideas and methods for the subsequent screening of traditional Chinese medicines for the treatment of UC. However, our study has some limitations that should be acknowledged. This study did not screen out the key compounds or compound combinations for executive function. This will be further explored in subsequent research based on the pathways discovered in this study. Declarations CRediT authorship contribution statement Yi Liu: designed experiments, analyzed and drafted original draft. Jun Ge: performed the experiments and drafted original draft. Xingke Zhu: contributed to do experiments and software technical assistance. Li Cheng: designed experiments,contributed to experiments method. Xianxian Liu: data curated assist and photos edited. Cheng Chen: validated data and draft. XingYi Zhang and Lei Guo: literature search. Zhengzheng Wu and Meili Liu: reviewed the manuscript. Funding This work was supported by the National Science Foundation of Hubei (Nos. 2023AFD155). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was supported by the National Science Foundation of Hubei. The materials for the graphical abstract were sourced from the websites SciDraw and BioRender. Data availability Data will be made available on request. Author details Corresponding Author: Yi Liu( [email protected] );Cheng Chen( [email protected] );Xianxian Liu( [email protected] ). Co-First Authors: Jun Ge( [email protected] );Xingke Zhu( [email protected] );Li Cheng( [email protected] ). Other authors: XingYi Zhang( [email protected] );Lei Guo( [email protected] );Zhengzheng Wu( [email protected] );Meili liu( [email protected] ). Affiliation: 1 Hubei Provincial Key Laboratory for Chinese Medicine Resources and Chinese Medicine Chemistry, School of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China; 2 Hubei Shizhen Laboratory, Wuhan 430065, China; 3 Key Laboratory of Chinese Medicinal Resource and Chinese Herbal Compound of the Ministry of Education, Wuhan 430065, China; 4 Fifth Hospital in Wuhan，Wuhan 430050, China. References da Silva BC, Lyra AC, Rocha R, Santana GO. Epidemiology, demographic characteristics and prognostic predictors of ulcerative colitis. World J Gastroenterol. 2014;20(28):9458–67. Peng S, Shen L, Yu X, Zhang L, Xu K, Xia Y, et al. The role of Nrf2 in the pathogenesis and treatment of ulcerative colitis. Front Immunol. 2023;14:1200111. Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet. 2017;389(10080):1756–70. Long D, Mao C, Huang Y, Xu Y, Zhu Y. Ferroptosis in ulcerative colitis: Potential mechanisms and promising therapeutic targets. Biomed Pharmacother. 2024;175:116722. Kucharzik T, Koletzko S, Kannengiesser K, Dignass A. Ulcerative Colitis-Diagnostic and Therapeutic Algorithms. Dtsch Arztebl Int. 2020;117(33–34):564–74. Jing X, Guan H, Du H, Han X. Discussion on pathogenesis of ulcerative colitis based on traditional Chinese medicine theory. Shaanxi J Traditional Chin Med. 2024;45(10):1391–4. Yang S, Xue Y, Chen D, Wang Z. Amomum tsao-ko Crevost & Lemarié: a comprehensive review on traditional uses, botany, phytochemistry, and pharmacology. Phytochem Rev. 2022;21(5):1487–521. Imran S, Bibi Y, Yang LE, Qayyum A, He W, Yang J, et al. Health-promoting compounds in Amomum villosum Lour and Amomum tsao-ko: Fruit essential oil exhibiting great potential for human health. Heliyon. 2024;10(5):e27492. Jialing L, Yangyang G, Jing Z, Xiaoyi T, Ping W, Liwei S, et al. Changes in serum inflammatory cytokine levels and intestinal flora in a self-healing dextran sodium sulfate-induced ulcerative colitis murine model. Life Sci. 2020;263:118587. Park JH, Ahn EK, Hwang MH, Park YJ, Cho YR, Ko HJ et al. Improvement of Obesity and Dyslipidemic Activity of Amomum tsao-ko in C57BL/6 Mice Fed a High-Carbohydrate Diet. Molecules. 2021;26(6). Shim KS, Hwang YH, Jang SA, Kim T, Ha H. Ethanol Extract of Amomum tsao-ko Ameliorates Ovariectomy-Induced Trabecular Loss and Fat Accumulation. Molecules. 2021;26(4):784. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96. Chen G, Qi H, Jiang L, Sun S, Zhang J, Yu J, et al. Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis. J Transl Med. 2024;22(1):1121. Wang Z, Hu D, Pei G, Zeng R, Yao Y. Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning. Front Immunol. 2023;14:1288699. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083–6. Andreatta M, Carmona SJ, UCell. Robust and scalable single-cell gene signature scoring. Comput Struct Biotechnol J. 2021;19:3796–8. Bhuva DD, Cursons J, Davis MJ. Stable gene expression for normalisation and single-sample scoring. Nucleic Acids Res. 2020;48(19):e113. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108–12. Noureen N, Ye Z, Chen Y, Wang X, Zheng S. Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data. Elife. 2022;11. Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838–47. Lu L, Wang JR, Henderson YC, Bai S, Yang J, Hu M et al. Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics. J Clin Invest. 2023;133(11). Lu Y, Chen Y, Wang Z, Shen H, Xu L, Huang C, et al. Single-cell and spatial transcriptome profiling reveal CTHRC1 + fibroblasts promote EMT through WNT5A signaling in colorectal cancer. J Transl Med. 2025;23(1):282. Dong S, Lu Y, Peng G, Li J, Li W, Li M, et al. Furin inhibits epithelial cell injury and alleviates experimental colitis by activating the Nrf2-Gpx4 signaling pathway. Dig Liver Dis. 2021;53(10):1276–85. Li Y, Ma M, Wang X, Li J, Fang Z, Li J, et al. Celecoxib alleviates the DSS-induced ulcerative colitis in mice by enhancing intestinal barrier function, inhibiting ferroptosis and suppressing apoptosis. Immunopharmacol Immunotoxicol. 2024;46(2):240–54. Deng B, Wang K, He H, Xu M, Li J, He P, et al. Biochanin A mitigates colitis by inhibiting ferroptosis-mediated intestinal barrier dysfunction, oxidative stress, and inflammation via the JAK2/STAT3 signaling pathway. Phytomedicine. 2025;141:156699. Dodson M, Castro-Portuguez R, Zhang DD. NRF2 plays a critical role in mitigating lipid peroxidation and ferroptosis. Redox Biol. 2019;23:101107. Puentes-Pardo JD, Moreno-SanJuan S, Carazo Á, León J. Heme Oxygenase-1 in Gastrointestinal Tract Health and Disease. Antioxid (Basel). 2020;9(12). Pousa ID, Maté J, Gisbert JP. Angiogenesis in inflammatory bowel disease. Eur J Clin Invest. 2008;38(2):73–81. Peng K, Bai Y, Zhu Q, Hu B, Xu Y. Targeting VEGF-neuropilin interactions: a promising antitumor strategy. Drug Discov Today. 2019;24(2):656–64. Queiro R, Coto P, González-Lara L, Coto E. Genetic Variants of the NF-κB Pathway: Unraveling the Genetic Architecture of Psoriatic Disease. Int J Mol Sci. 2021;22(23). Wang R, Ma Y, Zhan S, Zhang G, Cao L, Zhang X, et al. B7-H3 promotes colorectal cancer angiogenesis through activating the NF-κB pathway to induce VEGFA expression. Cell Death Dis. 2020;11(1):55. Huang Y, Liu Z, Li L, Jiang M, Tang Y, Zhou L, et al. Sesamin inhibits hypoxia-stimulated angiogenesis via the NF-κB p65/HIF-1α/VEGFA signaling pathway in human colorectal cancer. Food Funct. 2022;13(17):8989–97. Biasi F, Leonarduzzi G, Oteiza PI, Poli G. Inflammatory bowel disease: mechanisms, redox considerations, and therapeutic targets. Antioxid Redox Signal. 2013;19(14):1711–47. Li J, Cao F, Yin HL, Huang ZJ, Lin ZT, Mao N, et al. Ferroptosis: past, present and future. Cell Death Dis. 2020;11(2):88. Chen T, Liang L, Wang Y, Li X, Yang C. Ferroptosis and cuproptposis in kidney Diseases: dysfunction of cell metabolism. Apoptosis. 2024;29(3–4):289–302. Liu Y, Fang Y, Zhang Z, Luo Y, Zhang A, Lenahan C, et al. Ferroptosis: An emerging therapeutic target in stroke. J Neurochem. 2022;160(1):64–73. Tong J, Lan XT, Zhang Z, Liu Y, Sun DY, Wang XJ, et al. Ferroptosis inhibitor liproxstatin-1 alleviates metabolic dysfunction-associated fatty liver disease in mice: potential involvement of PANoptosis. Acta Pharmacol Sin. 2023;44(5):1014–28. Ding SB, Chu XL, Jin YX, Jiang JJ, Zhao X, Yu M. Epigallocatechin gallate alleviates high-fat diet-induced hepatic lipotoxicity by targeting mitochondrial ROS-mediated ferroptosis. Front Pharmacol. 2023;14:1148814. Adegboro AG, Afolabi IS. Molecular mechanisms of mitochondria-mediated ferroptosis: a potential target for antimalarial interventions. Front Cell Dev Biol. 2024;12:1374735. Glover HL, Schreiner A, Dewson G, Tait SWG. Mitochondria and cell death. Nat Cell Biol. 2024;26(9):1434–46. Chen QM. Nrf2 for protection against oxidant generation and mitochondrial damage in cardiac injury. Free Radic Biol Med. 2022;179:133–43. Zeng W, Long X, Liu PS, Xie X. The interplay of oncogenic signaling, oxidative stress and ferroptosis in cancer. Int J Cancer. 2023;153(5):918–31. Zhu J, Sun R, Sun K, Yan C, Jiang J, Kong F, et al. The deubiquitinase USP11 ameliorates intervertebral disc degeneration by regulating oxidative stress-induced ferroptosis via deubiquitinating and stabilizing Sirt3. Redox Biol. 2023;62:102707. Liu Y, Li BG, Su YH, Zhao RX, Song P, Li H, et al. Potential activity of Traditional Chinese Medicine against Ulcerative colitis: A review. J Ethnopharmacol. 2022;289:115084. Sun F, Yan C, Lv Y, Pu Z, Liao Z, Guo W, et al. Genome Sequencing of Amomum tsao-ko Provides Novel Insight Into Its Volatile Component Biosynthesis. Front Plant Sci. 2022;13:904178. Kim HJ, Kim H, Lee JH, Hwangbo C. Toll-like receptor 4 (TLR4): new insight immune and aging. Immun Ageing. 2023;20(1):67. Li B, Alli R, Vogel P, Geiger TL. IL-10 modulates DSS-induced colitis through a macrophage-ROS-NO axis. Mucosal Immunol. 2014;7(4):869–78. Karin M. Nuclear factor-kappaB in cancer development and progression. Nature. 2006;441(7092):431–6. Wang K, Grivennikov SI, Karin M. Implications of anti-cytokine therapy in colorectal cancer and autoimmune diseases. Ann Rheum Dis. 2013;72(Suppl 2):ii100–3. Formentini L, Santacatterina F, Núñez de Arenas C, Stamatakis K, López-Martínez D, Logan A, et al. Mitochondrial ROS Production Protects the Intestine from Inflammation through Functional M2 Macrophage Polarization. Cell Rep. 2017;19(6):1202–13. Bain CC, Scott CL, Uronen-Hansson H, Gudjonsson S, Jansson O, Grip O, et al. Resident and pro-inflammatory macrophages in the colon represent alternative context-dependent fates of the same Ly6Chi monocyte precursors. Mucosal Immunol. 2013;6(3):498–510. Viola A, Munari F, Sánchez-Rodríguez R, Scolaro T, Castegna A. The Metabolic Signature of Macrophage Responses. Front Immunol. 2019;10:1462. Pavlou S, Wang L, Xu H, Chen M. Higher phagocytic activity of thioglycollate-elicited peritoneal macrophages is related to metabolic status of the cells. J Inflamm (Lond). 2017;14:4. Michl J, Ohlbaum DJ, Silverstein SC. 2-Deoxyglucose selectively inhibits Fc and complement receptor-mediated phagocytosis in mouse peritoneal macrophages II. Dissociation of the inhibitory effects of 2-deoxyglucose on phagocytosis and ATP generation. J Exp Med. 1976;144(6):1484–93. van Uden P, Kenneth NS, Rocha S. Regulation of hypoxia-inducible factor-1alpha by NF-kappaB. Biochem J. 2008;412(3):477–84. Rius J, Guma M, Schachtrup C, Akassoglou K, Zinkernagel AS, Nizet V, et al. NF-kappaB links innate immunity to the hypoxic response through transcriptional regulation of HIF-1alpha. Nature. 2008;453(7196):807–11. Blouin CC, Pagé EL, Soucy GM, Richard DE. Hypoxic gene activation by lipopolysaccharide in macrophages: implication of hypoxia-inducible factor 1alpha. Blood. 2004;103(3):1124–30. Liu ZL, Chen HH, Zheng LL, Sun LP, Shi L. Angiogenic signaling pathways and anti-angiogenic therapy for cancer. Signal Transduct Target Ther. 2023;8(1):198. Chen W, Wu P, Yu F, Luo G, Qing L, Tang J. HIF-1α Regulates Bone Homeostasis and Angiogenesis, Participating in the Occurrence of Bone Metabolic Diseases. Cells. 2022;11(22). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMateria.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7446251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531159023,"identity":"4ed9c8a1-6124-4f15-b2b0-846ebba94544","order_by":0,"name":"Jun Ge","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ge","suffix":""},{"id":531159024,"identity":"4507ba7f-1323-43eb-a704-e841db86df9e","order_by":1,"name":"Xingke Zhu","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xingke","middleName":"","lastName":"Zhu","suffix":""},{"id":531159025,"identity":"62a32801-d4ca-4304-a410-e6b95e63d2a6","order_by":2,"name":"Li Cheng","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Cheng","suffix":""},{"id":531159026,"identity":"3ef9545b-d23f-4c54-b83e-2335a0a37ad0","order_by":3,"name":"XingYi Zhang","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"XingYi","middleName":"","lastName":"Zhang","suffix":""},{"id":531159027,"identity":"a0822a06-72ca-438e-b595-0633cad89729","order_by":4,"name":"Lei Guo","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Guo","suffix":""},{"id":531159028,"identity":"16aa3ebe-f842-4ac5-84ab-b81255f742b5","order_by":5,"name":"Zhengzheng Wu","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhengzheng","middleName":"","lastName":"Wu","suffix":""},{"id":531159029,"identity":"15a0997b-23b8-415a-83de-680f91d726ce","order_by":6,"name":"Meili Liu","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Meili","middleName":"","lastName":"Liu","suffix":""},{"id":531159030,"identity":"11ed692a-4cdb-4a24-b8d8-1de73432bda4","order_by":7,"name":"Xianxian Liu","email":"","orcid":"","institution":"Fifth Hospital in Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Xianxian","middleName":"","lastName":"Liu","suffix":""},{"id":531159031,"identity":"e4401d96-0f01-4274-8fb2-6a44df359694","order_by":8,"name":"Cheng Chen","email":"","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Chen","suffix":""},{"id":531159032,"identity":"6c3696a9-5003-4982-8f3b-9ddb7adcda2d","order_by":9,"name":"Yi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACfv7mAwc+VLDZ8ROtRXLGscSDM87wJUs2EKvF4ECO8WHeNjnGDQeItubAAQOgLWbMxseTNzD8qNhGWAdjc0MC0C9pfGZnnhUw9py5TVgLM9AaoC3HmM1u5BgwM7YRoYWNIbEB6Jf/jJtnEKuFhyGZAaiFjXGDBLFaJCSOMQAdxpYsAfTLQaL8Yn++//MHcFS2J2988KOCCC1IIMHgAEnqwVpI1TEKRsEoGAUjBAAAp+VFVptwPggAAAAASUVORK5CYII=","orcid":"","institution":"Hubei University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-24 12:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7446251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7446251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93933963,"identity":"bda8857f-96ff-4291-86d1-03f1f712210c","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5951639,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/8e22bca0f77cc0ff06220b96.docx"},{"id":93933955,"identity":"8d93bd62-d683-46bc-a728-094f25537f5c","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10560,"visible":true,"origin":"","legend":"","description":"","filename":"dd23f86e4f68481590070efc43990860.json","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/f261b6bbafa2db0465f3bd76.json"},{"id":93933964,"identity":"037571e5-0f82-420b-97fe-de17a68f5841","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":512253,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMateria.docx","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/b54ac0c3dce4d29c214e3a89.docx"},{"id":93934481,"identity":"70a879f5-d22e-44a6-8eb0-4d07a30606de","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152896,"visible":true,"origin":"","legend":"","description":"","filename":"dd23f86e4f68481590070efc439908601enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/b6e14f0fcbe88ddb23bd7383.xml"},{"id":93934480,"identity":"fadececf-a42d-489f-87db-07373f394413","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1596020,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/b73800d6d3290293a12ac6b1.jpeg"},{"id":93934482,"identity":"cb1433fd-3930-466d-9848-af27400de143","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1001335,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/1e69205af467b9be7041cdb5.jpeg"},{"id":93934484,"identity":"464f3e17-8d31-414d-b0ab-bbe616492e99","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217970,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/c625e0043c244d1d89af035c.jpeg"},{"id":93934487,"identity":"7bd86171-62af-4fe7-837c-132f12304700","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":501977,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/b5e6aa916056655d802e295e.jpeg"},{"id":93933961,"identity":"00f4168d-f19f-4c68-9299-edd1725c5f1a","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":731916,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/3c0b0b207f0629693dbf69b0.jpeg"},{"id":93934488,"identity":"a3fdcd51-29cb-4507-93bf-df9bd5cb76f3","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":364648,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/39960192e8cd1dab6f4baf53.jpeg"},{"id":93933970,"identity":"acd018fb-cd7a-4c4b-95b7-8ac5f783eed8","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":814596,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/ddad675abb6015592634e72c.jpeg"},{"id":93935502,"identity":"c91ef160-d84b-4a50-a161-50cf66d5a0f4","added_by":"auto","created_at":"2025-10-20 12:43:15","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":470123,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/2b0819a120c80e81236b840c.jpeg"},{"id":93933969,"identity":"f0e095f3-1bcf-4520-b35e-2e7d8957077e","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":650952,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/39c7d21bbb9811baa551cc31.jpeg"},{"id":93933973,"identity":"6a1d3a02-367e-477d-80dc-94926809693e","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":371076,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/06c6e6fe836f2cbfc3fc0b79.jpeg"},{"id":93934485,"identity":"e00317d3-9d7c-4a15-ab29-6b3a97e19b76","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":493092,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/73e433d963516dc80f5f1ba1.png"},{"id":93934486,"identity":"4e3ab0ef-322e-40b3-a74b-25a0c9893343","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":264473,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/22e4e283598e356895cc5d59.png"},{"id":93933976,"identity":"c2cc85e2-94ad-452f-bf48-4598807e6eb6","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35029,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/9f6c54ba516b8ad2253ac1d8.png"},{"id":93933984,"identity":"a84dacb9-ed99-487b-aced-57c24f6eb1b4","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106504,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/66891ba4dc4bab52be1f970f.png"},{"id":93933978,"identity":"c9bc446b-24e7-4fab-acd1-3e9914a2a317","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147303,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/6a770831ac16b93b9e2eda28.png"},{"id":93933977,"identity":"1a1ce8a3-2656-409b-b1b6-b55315f481cf","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77000,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/28b45cc5edcd3f045b7d6430.png"},{"id":93934489,"identity":"f28b9544-4d4f-43b7-9c56-a45442644cb7","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181069,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/87bb9759d7f1eb7928353159.png"},{"id":93933980,"identity":"387c5f6b-8304-4805-8dd0-f798657c3951","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128642,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/4c75e0ff6fe320a8257c2878.png"},{"id":93933983,"identity":"18650ff1-252d-45d4-a783-2e41379a9bba","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97887,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/369fc62385b9edb4a4294ae1.png"},{"id":93933986,"identity":"dc2192a9-2ce3-4ff7-91d0-ddc7347f8187","added_by":"auto","created_at":"2025-10-20 12:27:16","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72462,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/ad4b4dfb70c142a481d79f94.png"},{"id":93933982,"identity":"4e22b481-e8d1-4c4c-805d-939b39c3b708","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150833,"visible":true,"origin":"","legend":"","description":"","filename":"dd23f86e4f68481590070efc439908601structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/2e50a026ddb32e340e056613.xml"},{"id":93933985,"identity":"4fe37a17-a6f9-409d-9298-943a416c468b","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161211,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/f3a6ac7fb2f51a87562e99cc.html"},{"id":93933953,"identity":"8f1efac3-748c-4ec1-bfab-3737fe0d5091","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":390222,"visible":true,"origin":"","legend":"\u003cp\u003eLOM relieved the symptoms of DSS-induced UC in mice. (A) \u003cem\u003eIn vivo\u003c/em\u003e experiment procedure; (B) Body weight; (C) DAI score; (D) Colonic length; (E) TNF-α level; (F) IL-10 level;(G) H\u0026amp;E staining (200× magnification). Data are presented as mean ± SD, n=3; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs. Con; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05;\u003csup\u003e **\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001 vs. MOD; ns, not significant. (a: Control; b: 3%DSS; c: 3%DSS+LOM 300 mg/kg; d: 3%DSS+LOM 600 mg/kg; e:3%DSS+LOM 1200 mg/kg; f: 3%DSS+MMX 600 mg/kg).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/53de5db1c5d3e1df836330db.png"},{"id":93933951,"identity":"9eb85fac-538d-41ce-99a8-08819f8940a8","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68642,"visible":true,"origin":"","legend":"\u003cp\u003eLOM effectively ameliorates LPS-induced cellular injury. (A) CCK8 proliferation assay; (B) NO level; (C) TNF-α level; (D) IL-10 level; (E) ferrous ion level;(F) Western Blot assay;(G)Nrf2 and HO-1 protein expression .Data are presented as mean ± SD, n=3; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs. Con; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05;\u003csup\u003e **\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001 vs. MOD; ns, not significant.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/dce7ae9fca82635c88ea3724.png"},{"id":93933952,"identity":"b3f1c3f0-6ed7-4497-8daf-11bd64d69745","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103901,"visible":true,"origin":"","legend":"\u003cp\u003eThe extracted ion chromatograms of the compounds by UHPLC-QTOF-MS/MS with one injected analysis in positive ion mode. (A) Intensity between 0.1-6.4e\u003csup\u003e6\u003c/sup\u003e cps; (\u003c/p\u003e\n\u003cp\u003eB) Intensity between 0.1-1.0e\u003csup\u003e5\u003c/sup\u003e cps;(C) Intensity between 0.1-1.0e\u003csup\u003e4\u003c/sup\u003e cps.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/9a949956ffd69aca226b4d57.png"},{"id":93933954,"identity":"64408d99-322d-42b0-899c-3082d0b56ae2","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170623,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes screening. (A) Soft threshold power (left) and mean connectivity (right) of the WGCNA network; (B) Gene dendrogram from average linkage hierarchical clustering; (C) Module correlation with UC; (D) Module gene scatter plot; (E) Intersection gene Venn diagram; (F) Intersection gene PPI network.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/8a4f3023acb9e761945a9311.png"},{"id":93934479,"identity":"747b36ff-97cb-4429-b1af-e38642bcec2f","added_by":"auto","created_at":"2025-10-20 12:35:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86147,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic model construction. (A) ROC curves of hub genes in GSE87466 dataset. (B) Construction of a nomogram model with hub genes in GSE87466 dataset. (C) ROC curve for evaluating and validating the diagnostic model in GSE87466 and GSE3365 datasets.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/595cfe4489968f9edab548ba.png"},{"id":93933957,"identity":"8fc8cda8-40d9-4d8f-9b7d-ed7b96883e98","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":220418,"visible":true,"origin":"","legend":"\u003cp\u003eThe hub gene mainly expressed on macrophages (A) Quality control for inclusive data; (B) Seurat clusters of eligible cells in UMAP plot; (C) Cellular annotations revealing 11 distinct cell phenotypes; (D) Bubble plot of marker gene expression for each cell type; (E) Hub gene distribution across different cell subpopulations; (F) Six algorithms to calculate hub gene expression in different cell subpopulations.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/16343c7f9c99e444fc6bbb76.png"},{"id":93933972,"identity":"1d5f6937-2b2d-48d8-a70a-74e76618ab54","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":142258,"visible":true,"origin":"","legend":"\u003cp\u003eM-HIGH macrophages involved in the progression of UC. (A) Hub genes expression in macrophage subtypes; (B) Monocle-revealed macrophage differentiation trajectory, pseudotime distribution, and clusters; (C) Differentiation potential of diverse macrophage phenotypes; (D) Cytotrace-revealed differentiation potential of macrophage subtypes.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/5f3286a05d887c0e5fa508b9.png"},{"id":93933959,"identity":"7aa8f7aa-d937-488a-8c17-4eca730cadba","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":220001,"visible":true,"origin":"","legend":"\u003cp\u003eM-HIGH macrophages regulate endothelial cells through the VEGF pathway. (A) KEGG pathway enrichment of differentially expressed genes in macrophage subtypes; (B) Interaction kinetics between cell types; (C) Heatmap of cell signal input/output strength; (D) Bubble plot of signal output strength in macrophage subtypes; ;(E) Bubble plot of signal strength between cells and downstream factors.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/e0b2aa8c0c2e4522836eb16f.png"},{"id":93933967,"identity":"6d246736-2e68-4653-8eed-e976533048c0","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":90488,"visible":true,"origin":"","legend":"\u003cp\u003eIn \u003cem\u003evitro\u003c/em\u003e Validation Results. (A) CCK8 proliferation assay; (B) Western blot of relevant genes; (C) HIF1A protein expression; (D) TLR4 protein expression; (E) P-P65 protein expression; (F) P-STAT3 protein expression. Data are presented as mean ± SD, n=3; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs. Con; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05;\u003csup\u003e **\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001 vs. MOD; ns, not significant.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/ba33de46506e3fa2f434efc8.png"},{"id":93933981,"identity":"e5a7b556-4730-41d2-bc76-89b74db81ddd","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":247877,"visible":true,"origin":"","legend":"\u003cp\u003eIn \u003cem\u003evivo\u003c/em\u003e Validation Results. (A) Western blot of relevant genes; (B) HIF1A protein expression; (C) IL-1β protein expression; (D) P-P65 protein expression; (E) TLR4 protein expression; (F) P-STAT3 protein expression. Data are presented as mean ± SD, n=3; \u003csup\u003e#\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e##\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 vs. Con; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05;\u003csup\u003e **\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001 vs. MOD; ns, not significant.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/a9cafd409a34c507112d559f.png"},{"id":96708331,"identity":"05b31595-2cb9-494e-a0ab-0a896f45ef36","added_by":"auto","created_at":"2025-11-25 10:01:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2695649,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/6fb2d43d-785e-4f1b-84a6-f2a3af9edd26.pdf"},{"id":93933958,"identity":"fbe7d8c0-fa4c-4aed-bb05-74e452ac71fa","added_by":"auto","created_at":"2025-10-20 12:27:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":512253,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMateria.docx","url":"https://assets-eu.researchsquare.com/files/rs-7446251/v1/725ad5b56ba1270744bf92bc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting Macrophage Ferroptosis: A Mechanism of Lanxangia tsaoko methanol extract Inhibits VEGF to Attenuates ulcerative colitis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUC is a complex immune-mediated inflammatory bowel disease characterized by diffuse inflammation of the colon and rectal mucosa associated with barrier dysfunction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The fundamental causes of UC are multifaceted and elusive. However, increasing evidence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] suggests that the pathogenesis of UC is closely linked to oxidative stress and inflammation, in addition to genetics, gut microbiota, the host immune system and environmental factors. Furthermore, ferroptosis, a novel iron-dependent form of regulated cell death distinct from apoptosis and autophagy, plays an important role in UC pathogenesis through its involvement in iron metabolism, lipid peroxidation, and imbalance of antioxidant systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, the clinical treatment of UC predominantly relies on pharmacotherapy, including aminosalicylates, glucocorticoids, immunosuppressants, and biologics[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which are associated with significant side effects and prolonged treatment periods. Therefore, the exploration of herb with homology of medicine and food that are safe, effective, and readily extractable has emerged as a new direction for treating UC.\u003c/p\u003e\u003cp\u003eIn Traditional Chinese Medicine, UC is categorized under conditions such as \u0026ldquo;xia li\u0026rdquo; (diarrhea) and \u0026ldquo;xie xie\u0026rdquo; (loose stools), which are typically attributed to a combination of constitutional deficiencies, weakened spleen Qi, and the external invasion of dampness, often aggravated by irregular diet and lifestyle [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eLanxangia tsaoko\u003c/em\u003e (Crevost \u0026amp; Lemari\u0026eacute;) M.F.Newman \u0026amp; Škorničk.( On May 1, 2025, the name was verified by consulting the \u0026ldquo;World Flora Online\u0026rdquo;) is a traditional Chinese medicine that is both medicinal and culinary. It has low toxicity and is used both as a therapeutic agent and as a food seasoning due to its unique pungent aroma. Its medicinal and culinary values are widely recognized, and its low toxicity and diverse functionalities make it an important component in both traditional Chinese medicine and everyday diets. \u003cem\u003eLanxangia tsaoko\u003c/em\u003e, with its warm nature and affinity for the spleen and stomach meridians, is renowned for its ability to dispel dampness and warm these organs, and is commonly used in combination with other herbs to treat gastrointestinal disorders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. From the perspective of Traditional Chinese Medicine, \u003cem\u003eLanxangia tsaoko\u003c/em\u003e shows considerable therapeutic potential in the treatment of UC. Previous studies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have reported that \u003cem\u003eLanxangia tsaoko\u003c/em\u003e contains abundant essential oils, terpenoids, flavonoids, and phenolic compounds. Using bibliometric methods, it was found that about 500 monomeric compounds have been characterised from the alcoholic extract of \u003cem\u003eLanxangia tsaoko\u003c/em\u003e, among which flavonoids and polyphenols are more prevalent. Moreover, the ethanol extract of \u003cem\u003eLanxangia tsaoko\u003c/em\u003e is rich in flavonoids and polyphenolic compounds, exhibiting potent anti-inflammatory and antioxidant activities, which align with the pathogenesis of UC.\u003c/p\u003e\u003cp\u003eThis study aimed to comprehensively investigate the therapeutic effects and mechanisms of LOM in UC using integrated multi-omics approaches, including transcriptomics and single-cell analyses. By exploring the regulatory effects of LOM on key signaling pathways and hub genes, we seek to provide a scientific basis for its clinical application. The findings of this study are expected to contribute to the development of novel therapeutic strategies for UC and offer insights into the mechanisms underlying the efficacy of natural products in inflammatory bowel diseases.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1.Animals and animal model\u003c/h2\u003e\u003cp\u003eSixty SPF-grade male mice, aged 5 weeks and weighing 25\u0026ndash;27 g, were obtained from Hunan Silek Jingda Laboratory Animal Co., Ltd. (Production License No.: SCXK (Xiang) 2021-0002, Quality Certificate No.: 430727241102279125). The research protocol was approved by the Ethics Committee of the Animal Experimentation Center, issuance number 2025090P, ensuring that all procedures were carried out in strict compliance with the European Community guidelines, thereby meeting all ethical standards. Throughout the study, except for special conditions required for animal model establishment, all mice were kept in a temperature - and humidity - controlled room (21\u0026ndash;22 ℃, 55\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) with a 12 h light-dark cycle, and had free access to food and deionized water.After one week of adaptive feeding, the mice were randomly divided into several groups: a control group, a model group (3% DSS), low -, medium -, and high - dose LOM treatment groups (50, 100, 200 \u0026micro;g/mL), and a mesalazine - treated group (MMX). The blank group was given deionized water for 9 days, while the model group, LOM groups, and MMX group were provided with 3% DSS in drinking water for 9 days. Notably, on the third day, the LOM and MMX groups were treated with the corresponding drugs via gavage.During the experiment, the weight of each mouse was recorded daily, and the severity of colitis was assessed based on stool consistency, presence of bloody stool, and weight loss. By combining individual scores, the disease activity index (DAI) for each mouse was calculated daily according to Supplementary Table\u0026nbsp;1, using the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:DAI\\:=\\:(S1\\:+\\:S2\\:+\\:S3)/3\\:\\)\u003c/span\u003e\u003c/span\u003e[9].At the end of the experiment, the mice were euthanized under anesthesia, their weights were measured, blood samples were collected for serum preparation, colons were dissected out, quickly frozen in liquid nitrogen, and stored at -80 ℃ for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2.Materials\u003c/h2\u003e\u003cp\u003eMouse TNF-α ELISA Kit (Catalog No. MU30030) and Mouse IL-10 ELISA Kit (MU30055) were purchased from Wuhan Beinle Biotech Co., Ltd.; CCK-8 assay kit, NO detection kit (Lot No.: 112223240219), Fe\u003csup\u003e2+\u003c/sup\u003e content detection kit (Catalog No; BC5415), DSS(C16458324), Fecal occult blood kit(Lot.NO.20240628),ROS detection kit (BL714A) were purchased from Wuhan Hongrui Biotech Co., Ltd.; LPS (GC205009-10 mg), BCA protein concentration assay kit (Lot No.: G2026-1000T), HRP-conjugated goat anti-rabbit IgG (Lot No: GB23303), hypersensitive ECL chemiluminescence detection (G2020-50 mL), 50\u0026times;Cocktail protease inhibitor (CR2211028), ice-free rapid transfer buffer (powder, Lot No: G2028-1 L), electrophoresis buffer (powder, Lot No: G2018-2 L), 0.45 \u0026micro;M PVDF membrane (Lot No: F2203402), prestained protein marker IV (8-200 ku, G2083-250 uL), bovine serum albumin BSA (Lot No: CR2204075), DMEM/High Glucose GlutaMax-I supplement (Lot No: G4511) were purchased from Wuhan Sevier Biotech Co., Ltd.; \u003cem\u003eLanxangia tsaoko\u003c/em\u003e (Lot No:191101;Dried mature fruit identified by Professor Yang Hongbing of the School of Pharmacy, Hubei University of Chinese Medicine, and produced by Hubei Chenmei Pharmaceutical Co., Ltd.); The Milli-Q water system was used to produce deionized water in this study (Millipore, Bedford, MA, USA); Methanol, acetonitrile, and formic acid were all obtained from Merck KGaA (Darmstadt, Germany).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3.Preparation of the methanol extract\u003c/h2\u003e\u003cp\u003e\u003cem\u003eLanxangia tsaoko\u003c/em\u003e was crushed and reflux-extracted with petroleum ether (1:10) for 2 h to eliminate fat. Afterward, \u003cem\u003eLanxangia tsaoko\u003c/em\u003e was air-dried at room temperature. The dried material was subsequently reflux-extracted twice with methanol (1:10), each extraction lasting 2 h. The resulting methanol extracts were combined, filtered, and concentrated under reduced pressure. Finally, the concentrated solution was freeze-dried to obtain a dry powder, referred to as LOM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4.Cell culture and grouping\u003c/h2\u003e\u003cp\u003eCaco2 and RAW264.7 cells, derived from Professor Yi Liu, Director of the Graduate Program in Medicinal Chemistry at Hubei University of Traditional Chinese Medicine.Caco2 cellswere cultured in high-glucose DMEM supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 mg/L streptomycin. RAW264.7 were cultured with specialized medium.The cultures of two cells were maintained at 37℃ in a 5% CO\u003csub\u003e2\u003c/sub\u003e humidified atmosphere.\u003c/p\u003e\u003cp\u003eCaco2 cells were seeded in DMEM medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin, and cultured at 37℃ in 5% CO\u003csub\u003e2\u003c/sub\u003e. The culture medium was changed every 2 days. Cells in logarithmic growth phase were selected and randomly divided into blank control group(Con), model group(Mod), and drug-treated groups with different concentration gradients (10, 20, 40,80,100 \u0026micro;g/mL). The drug concentrations were determined based on literature[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]and preliminary experiments. After stimulating the model and drug-treated groups with 20 \u0026micro;g/mL LPS for 24 h to induce modeling, cells were treated with various concentrations of LOM for 24 h. The RAW264.7 cells were seeded in their specific medium, and both cell types were cultured under conditions of 37\u0026deg;C and 5% CO2, with the medium changed every 2 days. Cells in the logarithmic growth phase were harvested and randomly divided into a control group and different concentration gradient groups treated with LOM (10, 20, 40, 80 \u0026micro;g/mL). The concentrations of the drug were derived from literature and preliminary experiments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Cell co-culture\u003c/h2\u003e\u003cp\u003eThe logarithmically growing RAW264.7 cells were added to the upper chamber of the Transwell, and logarithmically growing Caco2 cells were added to the lower chamber to simulate the co-culture environment of RAW264.7 and Caco2. The cells were randomly divided into control group, model group, and low, medium, and high LOM groups (10, 20, 40 mg\u0026middot;L^-1). The low, medium, and high LOM groups were cultured for 24 hours to establish the co-culture model, followed by treatment with different doses of LOM for an additional 24 hours; the control group was directly cultured with 100% of the RAW264.7 specific medium for 48 hours under normal conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6.Cell proliferation assay\u003c/h2\u003e\u003cp\u003eCell proliferation was assessed using the CCK-8 assay. Caco2 cells in logarithmic growth phase were seeded at a density of 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/mL in 96-well plates. Cells were grouped according to the method described in section \u0026ldquo; Cell Culture and Grouping\u0026rdquo;(with 3 replicate wells per group). Subsequently, 10 \u0026micro;L of CCK-8 reagent was added to each well, and PBS was added to the outermost wells of the 96-well plate to seal it. The plate was then incubated at 37℃ for 1.5 h. The optical density (OD) values of each well were measured at a wavelength of 450 nm using an enzyme-linked immunosorbent assay (ELISA) reader to reflect the proliferation capacity of the cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7.Enzyme-Linked Immunosorbent Assay (ELISA)\u003c/h2\u003e\u003cp\u003eAfter incubating the blank control group, model group, and drug-treated groups at a constant temperature for 48 h, supernatants were collected and centrifuged at 4℃, 5,000 rpm for 10 min to remove insoluble materials. The supernatants were filtered through a 0.22 \u0026micro;M microporous membrane. Following the manufacturer's instructions, the levels of TNF-α, IL-10, and NO in the supernatants of the blank control group, model group, and various drug-treated groups were detected using ELISA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8.Detection of Fe\u003csup\u003e2+\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eFe\u003csup\u003e2+\u003c/sup\u003e levels in Caco2 cells were detected using an Fe\u003csup\u003e2+\u003c/sup\u003e content detection kit. Cells were grouped according to the method described in section \u0026ldquo; Cell Culture and Grouping\u0026rdquo; (with 3 replicate wells per group) and treated accordingly. Following removal of the culture medium, 1 mL of reagent 1 was added, and cells were subjected to ice-cold homogenization. The homogenates were then centrifuged at 4℃ and 10,000 rpm for 10 min, and the supernatants were collected and processed according to the manufacturer's instructions. Finally, optical density (OD) values at 593 nm were measured using an ELISA reader to quantify intracellular Fe\u003csup\u003e2+\u003c/sup\u003e levels in each well.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9.UHPLC-QTOF-MS/MS analysis conditions\u003c/h2\u003e\u003cp\u003eThe ACQUITY UPLC M\u0026thinsp;\u0026minus;\u0026thinsp;Class system was used for UHPLC-MS/MS analysis. A Waters ACQUITY UPLC BEH C\u003csub\u003e18\u003c/sub\u003e column (100 \u0026times; 2.1 mM, 1.7 \u0026micro;M) was used for separation. The injection volume was 2.0 \u0026micro;L and the flow rate was 0.3 mL/min. Mobile phase A was water-formic acid (1000:1,v/v) and mobile phase B was acetonitrile. The following binary gradient with linear interpolation was used: 0.01 min, 10% B; 5 min, 20% B; 22 min, 80% B; 27 min, 100% B; 30min, 100% B; 32min, 10%B; 35 min, 10% B. The column oven and autosampler temperatures were maintained at 30 ℃ and 5 ℃, respectively.\u003c/p\u003e\u003cp\u003eThe Waters Xevo G2-XS QTof mass spectrometer was operated in the electrospray ionization (ESI) mode (Waters, Milford, MA, USA). The positive ion electrospray was selected for data acquisition. The optimized operating parameters were set: cone gas flow, 50 L/h; capillary voltage, 3.0 kV; source temperature, 100 ℃; cone voltage, 20 V; desolvation temperature, 500 ℃; desolvation gas flow, 1000 L/h. The mass ranges were set at m/z 100\u0026ndash;1500 for full scan, with scan duration of 1 s. The MSE continuum mode was used to collect data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10.Data Acquisition and Processing\u003c/h2\u003e\u003cp\u003eFrom the NCBI Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/GEO/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/GEO/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we selected the GSE87466 and GSE3365 datasets for annotation via the GPL13158 Affymetrix chip platform, matching GSE87466 gene probe IDs to \u0026ldquo;Gene symbols\u0026rdquo;, and the GPL570 Affymetrix chip platform for annotation, matching GSE3365 gene probe IDs to \u0026ldquo;Gene symbols\u0026rdquo;.\u003c/p\u003e\u003cp\u003eSimilarly, we obtained single-cell RNA sequencing (scRNA-seq) data on ulcerative colitis (UC) from the GEO database: GSE214695. When processing the scRNA-seq data, we retained high-quality cells with mitochondrial gene content below 20% and over 200 expressed genes, focusing on genes with expression levels between 200 and 7000 and active in at least three cells. Subsequently, we performed data integration using the Seurat pipeline [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We standardized and normalized the remaining cells using the \u0026ldquo;Log-normalization\u0026rdquo; method and a linear regression model, and detected the most variable genes via the \u0026ldquo;FindVariablFeatures\u0026rdquo; function. Then, we reduced the dimensionality of the scRNA-seq data through principal component analysis (PCA) and used the R package \u0026ldquo;single R\u0026rdquo; for UMAP dimensionality reduction, dataset integration, and cell type annotation. To eliminate batch effects between samples, we performed soft k-means clustering using the \u0026ldquo;Harmony\u0026rdquo; package [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Cell clustering was accomplished with the \u0026ldquo;FindClusters\u0026rdquo; function. Cell cluster annotation involved examining highly expressed genes, genes with unique expression patterns, and established classic cell markers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11.Obtaining Intersection Genes\u003c/h2\u003e\u003cp\u003eThe R package \u0026ldquo;WGCNA\u0026rdquo; was used to construct a co-expression network [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hierarchical clustering analysis of the gene expression data in GSE87466 was performed to identify outliers. In this study, the soft threshold was set at 11. The weighted adjacency matrix was transformed into a topological overlap measure (TOM) matrix to assess connectivity within the network. The average linkage hierarchical clustering method was applied to construct the clustering tree of the TOM matrix. Here, the minimum gene module size was set at 50 to obtain suitable modules, and the threshold for merging similar modules was set at 0.5. Gene significance (GS) and module membership (MM) were calculated to correlate modules with clinical traits.\u003c/p\u003e\u003cp\u003eFor the identified modules, genes from the two modules most related to UC were selected based on their correlation coefficient r and P-value.Genes related to UC were gathered from databases like CTD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ctdbase.org/\u003c/span\u003e\u003cspan address=\"http://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), focusing on those with counts over 10,000 and above the median. Ferroptosis-related genes, including markers, inhibitors, and drivers, were sourced from FerrDb (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://zhounan.org/ferrdb\u003c/span\u003e\u003cspan address=\"http://zhounan.org/ferrdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). LOM compounds were identified via UHPLC-QTOF-MS/MS, selecting those with high gastrointestinal absorption scores and meeting at least two \u0026ldquo;Yes\u0026rdquo; criteria for drug-likeness on Swiss ADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/index.php\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The SMILES strings of these compounds were input into the SwissTargetPrediction platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict their targets. Then, in R 4.2.1, intersection analysis of ferroptosis, drug, and disease-related genes was performed using the \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;VennDiagram\u0026rdquo; packages, with results visualized using the \u0026ldquo;ComplexHeatmap\u0026rdquo; package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12.Hub genes Selection\u003c/h2\u003e\u003cp\u003eWe constructed a protein - protein interaction (PPI) network based on the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Also, in Cytoscape 3.10.1, we used the cytoHubba plugin's MCC algorithm to calculate the top 5 genes, which we designated as hub genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13.Immune Infiltration Analysis\u003c/h2\u003e\u003cp\u003eTo enhance the ability to identify disease heterogeneity, clinical subtypes, and molecular characteristics, the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package was used. We performed unsupervised consensus clustering analysis based on hub genes' expression for disease samples in GSE87466 using the PAM clustering method.The \u0026ldquo;gsva\u0026rdquo; package was utilized to calculate immune cell infiltration scores and the activity levels of 13 immune functions for both C1 and C2 groups across all samples and specifically within disease samples. Heatmaps depicting the associations between hub genes, immune cells, and the 13 immune functions were generated using the ggplot2 package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14.Gene set scoring algorithm in scRNA-seq\u003c/h2\u003e\u003cp\u003eWe used six algorithms from the \u0026ldquo;irGSEA\u0026rdquo; package to score hub genes in the scRNA-seq dataset: AUCell, UCell, singscore, ssgsea, JASMINE, and viper. AUCell and UCell were chosen for their unique ability to quantify gene set activity at the single-cell level, which is crucial for accurately identifying activation patterns in UC cells. AUCell calculates gene set activity in each cell by determining the area under the cumulative distribution curve of gene expression ranks[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. UCell assesses gene set activity by computing and normalizing rank scores within single-cell gene expression rankings[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Singscore ranks genes in each cell for a given gene set and calculates the average rank score, based on the difference between the average ranks of positive and negative genes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ssgsea determines gene set enrichment by calculating a relative enrichment score that compares the expression values of genes in a set to those of other genes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. JASMINE calculates an approximate average based on gene rankings in expressed genes and the enrichment of gene sets in expressed genes within a single cell, then combines these averages to derive the final gene set enrichment score[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Viper estimates gene set enrichment scores by performing a three-tailed calculation based on gene expression rankings across cells[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15.Differential gene expression and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eMacrophages were divided into M_HIGH and M_LOW subgroups based on the average AUCell score. The \u0026ldquo;FindMarkers\u0026rdquo; function identified differentially expressed genes (DEGs) between these two groups[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For DEGs, GO and KEGG pathway enrichment analyses were performed using the \u0026ldquo;clusterProfiler\u0026rdquo; package in R 4.2.1, with visualization done via the \u0026ldquo;ggplot2\u0026rdquo; package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16.Pseudo-time analysis\u003c/h2\u003e\u003cp\u003eThe Monocle3 package was used to conduct reverse chronological analysis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], aimed at reconstructing the developmental trajectory of cells based on single-cell gene expression data. This intricate process entailed constructing a single-cell expression matrix, categorizing cells into distinct developmental states, and delineating cell developmental trajectories by discerning gene expression patterns. We also evaluated cell maturity or developmental status utilizing the Cytotrace and Diffusion Map method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17.Cell communication\u003c/h2\u003e\u003cp\u003eThe \u0026ldquo;CellChat\u0026rdquo; package was used to analyze gene expression data and explore changes in potential cell - cell communication networks. Using the standard CellChat pipeline, we relied on the default CellChatDB for ligand - receptor interactions. By identifying overexpressed ligands or receptors within specific cell populations, we inferred cell - type - specific interactions. Moreover, we used the R package \u0026ldquo;Nichenet\u0026rdquo; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to gain deeper insights into the complex relationships between cell-cell communication and gene expression, revealing the roles and interactions of cells and genes in various biological processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.18.H\u0026amp;E staining analysi\u003c/h2\u003e\u003cp\u003eFirst, tissue embedding: appropriate colon tissue was embedded in a cassette, labeled with tissue information, rinsed with tap water for half an hour, then transferred to 70%, 80%, 90%, and 95% alcohol for 1 hour each, followed by absolute ethanol for 2 hours, xylene for 20 minutes, and finally immersed in wax for 3 hours before embedding. The section thickness was 5 \u0026micro;m. Briefly, nuclear cell staining with hematoxylin solution for 2 minutes, eosin staining for 1 minute, water rinsing for 5 minutes, then dehydration through 70%, 80%, 90%, 95%, and 100% alcohol for 30 seconds each, and xylene for 10 minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.19.Western Blot Analysis (WB)\u003c/h2\u003e\u003cp\u003eCells were lysed with RIPA lysis buffer containing protease inhibitors on ice for 30 min to extract total proteins. The lysates were centrifuged at 4 ℃, 12,000 rpm for 10 min, and the supernatants were collected. Protein concentrations were determined using a BCA protein quantification kit. Total proteins were separated by 10% SDS-PAGE gel electrophoresis, and then transferred onto a polyvinylidene fluoride (PVDF) membrane using a wet transfer method. The PVDF membrane was blocked with 5% skim milk at room temperature for 2 h, followed by removal of the blocking solution and washing with TBST three times (10 min each). The membrane was then incubated with primary antibodies overnight at 4 ℃, followed by three washes with TBST (10 min each) after antibody retrieval. Subsequently, the membrane was incubated with HRP-conjugated goat anti-rabbit IgG secondary antibody (1:10,000) at room temperature for 1 h and washed three times with TBST (10 min each). Protein bands were visualized using the highly sensitive ECL chemiluminescence method, and exposed and developed using Image J software for quantitative analysis of protein band grayscale values with β-actin as an internal reference.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e2.20.Statistical Methods\u003c/h2\u003e\u003cp\u003eImageJ software and GraphPad Prism 9.0 were used for statistical analysis. Results are presented as X\u0026thinsp;\u0026plusmn;\u0026thinsp;S, and intergroup comparisons were performed using t-tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.1. LOM relieved the symptoms of DSS-induced UC in mice\u003c/h2\u003e\u003cp\u003eThe DSS-induced mouse UC model is widely used and closely mimics human UC pathology. During the experiment, the 3% DSS group showed sustained weight loss compared to the normal group. However, the LOM treatment group significantly alleviated this weight loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, the LOM group had a marked increase in colon length and a reduction in DAI scores compared to the 3% DSS group, indicating relief from weight loss, diarrhea, and bloody stools (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D). Compared to the normal group, the DSS group had significantly increased TNF-α levels and decreased IL-10 levels, and LOM treatment inhibited these changes (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F) .DSS caused loss of crypt glands, mucosal damage, and inflammatory cell infiltration in the colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). H\u0026amp;E staining showed that, compared to the normal group, the 3% DSS group had these pathological changes, while the LOM group significantly improved them (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Thus, LOM alleviated the histological damage and inflammatory response in UC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.2.LOM Inhibited LPS-Induced Inflammation and ferroptosis\u003c/h2\u003e\u003cp\u003eRecent studies have observed hallmark signs of ferroptosis\u0026mdash;such as iron deposition and lipid peroxide accumulation\u0026mdash;during the onset of disease in mouse models of ulcerative colitis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Accumulating evidence [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] indicated that pharmacological modulation of ferroptosis attenuated experimental ulcerative colitis, positioning this pathway as a compelling therapeutic target. Whether LOM mediates its protective effects via ferroptosis remains unknown. We therefore employed in vitro models to evaluate LOM\u0026rsquo;s ability to regulate ferroptosis and to quantify its influence on critical inflammatory mediators. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, LOM at 50, 100, and 200 \u0026micro;g/mL exerted no significant effect on cell viability; therefore, these concentrations were selected for subsequent experiments. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D showed that, compared to the control group, the model group had significantly elevated levels of NO and TNF-α, while the anti-inflammatory factor IL-10 was markedly downregulated. LOM treatment significantly reduced TNF-α and NO levels and increased IL-10 levels. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE demonstrated that LOM significantly reversed the LPS-induced increase in Fe\u003csup\u003e2+\u003c/sup\u003e levels. Nrf2 is a key regulator of lipid peroxidation and ferroptosis, with many proteins and enzymes that prevent lipid peroxidation and thus trigger ferroptosis being Nrf2 target genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. HO-1, a target gene of Nrf2, may offer protection, but its excessive accumulation and activation can lead to ferroptosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G, LOM significantly upregulated the relative expression levels of Nrf2 and HO-1 compared to the model group. These findings indicated that LOM can markedly inhibit inflammation and ferroptosis, suggesting its potential for UC treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.3.UHPLC-QTOF-MS/MS Analysis\u003c/h2\u003e\u003cp\u003eThe compounds of LOM were analyzed using UHPLC-QTOF-MS/MS with MassLynx 4.1 (Waters, USA) as the data acquisition software, identifying a total of 141compounds, predominantly flavonoids and polyphenol, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C. Specific details of these compounds can be found in Supplementary Table\u0026nbsp;2. We identified the compounds that met the screening criteria under the \u0026ldquo;Acquisition of Intersection Genes\u0026rdquo;section and marked them with an asterisk (*).During identification, the mass spectrum data were matched with the combined mass spectrum information in the database (HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/\u003c/span\u003e\u003cspan address=\"https://hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), COCOUNT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coconut.naturalproducts.net/\u003c/span\u003e\u003cspan address=\"https://coconut.naturalproducts.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), NANPDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://african-compounds.org/anpdb/\u003c/span\u003e\u003cspan address=\"https://african-compounds.org/anpdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), etc.), and preliminary screening was conducted according to the excimolecular ion peaks and element compositions, and further confirmation was conducted according to the primary and secondary information of each chromatographic peak.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.4.Construction of weighted gene coexpression networks and Hub Gene Selection\u003c/h2\u003e\u003cp\u003eTo screen out the key genes of UC targeted by LOM, we used the GSE 87466 dataset. In this study, WGCNA clustered UC-related highly correlated genes. We chose 11 as the soft threshold (R\u0026sup2; = 0.719) to build a scale - free network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and then merged modules per the cutoff, screening out 10 co - expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Module correlation analysis revealed Megrey60 (cor\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;=\u0026thinsp;5e \u0026minus;\u0026thinsp;18) and MEtan (cor\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;=\u0026thinsp;2e \u0026minus;\u0026thinsp;15) had the highest correlation with UC. Consequently, the 3,126 genes in Megrey60 and MEtan were taken as target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). By intersecting WGCNA - related genes with ferroptosis - related, LOM - related, and UC - related genes, we obtained 31 intersection genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Using the MCC algorithm, the top five intersection genes were identified as TLR4, HIF1A, IL1B, STAT3, and TNF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.5.Construction of the ROC Diagnostic Model\u003c/h2\u003e\u003cp\u003eTo evaluate the diagnostic accuracy of the five core genes in predicting ulcerative colitis (UC) - related outcomes, we constructed a ROC curve analysis. Genes with an area under the curve (AUC)\u0026thinsp;\u0026gt;\u0026thinsp;0.7 are considered to have significant predictive value. In the GSE87466 dataset, TLR4 (AUC\u0026thinsp;=\u0026thinsp;0.755), HIF1A (AUC\u0026thinsp;=\u0026thinsp;0.950), IL1B (AUC\u0026thinsp;=\u0026thinsp;0.966), TNF (AUC\u0026thinsp;=\u0026thinsp;0.817), and STAT3 (AUC\u0026thinsp;=\u0026thinsp;0.882) all showed significant predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Based on logistic regression analysis of these five genes, we constructed a diagnostic nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and further developed a ROC diagnostic model. The ROC curve of combination with 5 genes in the training dataset (GSE87466) showed an AUC of 0.933, and in the testing dataset (GSE3365), the AUC was 0.891 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), confirming the high predictive value of hub genes for UC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.6Immune Infiltration Analysis\u003c/h2\u003e\u003cp\u003eWe performed consensus clustering analysis on UC samples from GSE87466. The results showed the classification was highly reliable and stable when k\u0026thinsp;=\u0026thinsp;2, so we divided the UC samples into subgroups C1 and C2 (Supplementary Fig.\u0026nbsp;1A). In C1, the expression levels of aDC, B cells, CD8 T cells, cytotoxic cells, Macrophages, T cells, T helper cells, Tcm, TFH, and Th1 cells were higher than in C2 (Supplementary Fig.\u0026nbsp;1B). Interestingly, the expression level of Th17 cells in C2 was higher than in C1 (Supplementary Fig.\u0026nbsp;1B). The correlation analysis showed the expression of hub genes was closely related to the activation of Macrophages, aDC, and Th1 cells (Supplementary Fig.\u0026nbsp;1C). Moreover, C1 patients had significantly higher expression of multiple immune pathways than C2 patients (Supplementary Fig.\u0026nbsp;1D). The correlation analysis showed the expression of hub genes was associated with the activation of parainflammation and CCR pathwaysSupplementary Fig.\u0026nbsp;1E). The immune infiltration analysis indicated that the pathological mechanism of UC is closely related to the human immune system.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.7.The scRNA-seq profiling of UC\u003c/h2\u003e\u003cp\u003eBulk transcriptome sequencing has certain limitations and cannot characterize on which cells genes are expressed. Therefore, we utilized single-cell technology to identify cells expressing five core genes for the study of the mechanism by which LOM alleviates UC. Prior to further analysis, quality control of sample data was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and batch effect correction was applied. Results showed a relatively stable overall distribution with low sensitivity to batch effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Cells were divided into 11 subgroups and annotated based on marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), with cell types determined by specific marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The distribution of hub genes across different cell subgroups is shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Using algorithms such as AUCell, UCell, singscore, ssgsea, JASMINE, and viper, we analyzed the expression of hub genes in different celltypes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, the five core genes are mainly expressed on macrophages, which also determines the direction for our subsequent research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.8.Trajectory analysis of macrophage differentiation and development\u003c/h2\u003e\u003cp\u003eTo elucidate the biological roles of the hub genes on macrophage, we stratified macrophages into two functionally distinct subgroups\u0026mdash;M_HIGH and M_LOW, based on AUCell-derived gene-set activity scores. UMAP visualizations and violin plots confirmed pronounced enrichment of all five hub genes in the M_HIGH subset (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Unsupervised trajectory inference with Monocle 3 revealed a continuous pseudotime trajectory in which macrophages progressively transition from M_LOW to M_HIGH, with M_HIGH occupying the distal end, indicative of a terminally differentiated state (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). CytoTRACE analysis independently corroborated this finding, assigning the lowest differentiation potential to M_HIGH and positioning M_LOW at the trajectory origin (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). Collectively, these data establish M_HIGH as the terminal differentiation node of macrophages and suggest that the abundance of this subgroup increased during UC progression. The distinct transcriptional program of M_HIGH was intimately associated with UC pathogenesis, and the elevated expression of the hub genes within this subset not only reinforces their identity as key UC drivers but also implicates them in orchestrating terminal macrophage differentiation and functional polarization, thereby driving immune dysregulation and tissue injury in UC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e3.9.Cell Communication Analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the mechanism of hub genes, we performed a KEGG pathway enrichment analysis on differentially expressed genes between M_HIGH and M_LOW macrophage subgroups. The results indicated that the occurrence of UC is closely associated with the significant activation of multiple signaling pathways, including the MAPK, NF-κB, TNF, and Toll-like receptor pathways, which were highly relevant to inflammation. The genes involved in these pathways significantly overlaped with our core genes of interest. Based on the pivotal role of these pathways in inflammation and the marked enrichment of core genes, we further analyzed exactly by which intercellular communications M-HIGH regulates to affect the pathological progression of UC. Consequently, we systematically assessed the differences in communication in cell types. The findings revealed that M_HIGH macrophages exhibit significantly higher signal input and output intensities compared to other cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), with a notably stronger communication strength than M_LOW macrophages.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the pronounced advantage of M_HIGH macrophages in signal transduction, we further investigated their specific role within the intercellular communication network, particularly their interactions with key cell types and how these interactions affect the pathogenesis of UC. As shown in 8C, M-HIGH macrophages mainly have a regulatory relationship with endothelial cells. And the VEGF pathway is the key for M-HIGH macrophages to regulate endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Further analysis using the NicheNet algorithm got the same result (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). The above results suggest that M-HIG promotes the progression of UC by stimulating endothelial cells by secreting VEGF.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e3.10. In Vivo and In Vitro Validation\u003c/h2\u003e\u003cp\u003eVEGF is a cytokine that promotes angiogenesis in endothelial cells, and enteritis is accompanied by a large number of new blood vessels [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As is well established, NF-κB is a critical family of transcription factors that regulate inflammation and immune responses by controlling the expression of a plethora of downstream target genes in response to environmental changes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Existing studies have shown that the activation of NF-κB can modulate VEGFA expression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. And STAT3 is a classic downstream pathway of NF-κB. At first, we conducted \u003cem\u003ein vitro\u003c/em\u003e experiments. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, the viability of RAW264.7 cells remained unaffected across a range of LOM treatment concentrations from 10 to 80 \u0026micro;g/mL, indicating no influence on cell activity. The co-culture system of LPS-Caco2 and RAW264.7 was constructed to evaluate the effect of LOM on macrophages. After the co-culture was completed, RAW264.7 cells were collected for the WB experiment. The expression of protein level of TLR4, HIF1A, p-STAT3 and p-NF-κB (p-p65) were significantly increased under co-culture, and were markedly reversed by LOM treatment, as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-F.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further confirm the conclusion, we verified hub genes and NF-κB with specimens from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Results showed that after 3% DSS treatment, the relative protein expression levels of TLR4, HIF1A, and IL-1β in colon tissue were significantly increased, and the phosphorylation of NF-κB P65 and STAT3 was activated (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-F). LOM, however, markedly reversed these effects. In summary, LOM combats UC by inhibiting the TLR4/NF-κB pathway, HIF1A expression, and STAT3 phosphorylation, thus reducing inflammation, oxidation, and ferroptosis. We detected the expression of VEGF and found that VEGFA was highly expressed in the model group, while LOM significantly reversed this high expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG-H). The above results confirm the conclusion of the single-cell analysis: Macrophages participate in the development process of UC by secreting VEGF through activating the NF-κB/STAT3 pathway, while LOM can reverse this phenomenon.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUC is a chronic non-specific inflammatory disease of the intestinal tract, often associated with shortened life expectancy in affected individuals and significantly increased risk of colorectal cancer in advanced stages. Therefore, identifying key biomarkers of UC to develop safer and more effective therapies remains a pressing research priority. Currently, approved drugs for UC mainly include antibiotics, aminosalicylates, and glucocorticoids, but their use is restricted due to severe adverse reactions, drug resistance, and lengthy treatment durations [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Ferroptosis, a newly discovered regulated form of cell death, is characterized by iron-dependent lipid peroxidation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which has been implicated in the pathogenesis of many diseases [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Increasing evidence suggests that the pathogenesis of UC is associated with ferroptosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Studies have increasingly highlighted the close relationship between mitochondria and ferroptosis. Imbalance in iron homeostasis not only triggers ferroptosis but also constitutes a crucial factor leading to mitochondrial dysfunction[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Diseases typically caused by mitochondrial dysfunction are attributed to excessive production of free radicals within mitochondria, which may be exacerbated by disrupted iron homeostasis, thereby increasing oxidative stress and further impairing mitochondrial function [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In recent years, the application of Traditional Chinese Medicine in the treatment of UC has become increasingly prominent [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Amomum tsao-ko, as a traditional Chinese medicine with dual food-medicine origins, is commonly employed to treat malaria, dyspepsia, gastric disorders, and diarrhea [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, we demonstrated through transcriptome, single-cell, and \u003cem\u003ein vitro\u003c/em\u003e experiments that LOM regulates macrophage interaction with vascular endothelial cells through the ferroptosis pathway for the treatment of colitis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn vivo\u003c/em\u003e experiments, LOM significantly improved diarrhea and rectal bleeding in mice and effectively improved colonic pathology, suggesting that \u003cem\u003eLanxangia tsaoko\u003c/em\u003e is a highly potential drug for the treatment of UC. To further clarify the key targets and pathways of LOM in the treatment of UC, we employed HPLC-MS, bioinformatics, and \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments. At first, 141 chemical components of LOM were characterized by using UHPLC-QTOF-MS/MS. By integrating WGCNA analysis, compound target genes from public databases, differentially expressed genes from transcriptomic analysis, and genes associated with the ferroptosis pathway, we successfully identified five hub genes: TLR4, HIF1A, IL1B, STAT3, and TNF. The ROC results of the combined analysis of the five hub genes also confirmed its good predictive value and its important role in the progression of UC. TLR4, central to innate immune signaling, coordinates inflammatory responses by influencing transcription factor activity. Changes in TLR4 activity regulate NF-κB and MAPK activation through both MyD88-dependent and independent pathways, thereby impacting the progression of various diseases by altering the expression of pro-inflammatory cytokines such as IL-1β, IL-6, TNF-α, and type I interferons [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].In UC development, excessive ROS accumulation and disruption of the colonic epithelial barrier lead to inflammatory factor release [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. NF-κB pathway activation results in the secretion of inflammatory cytokines like IL-1β, IL-6, TNF-α, and IFN-γ, which are crucial for maintaining gastrointestinal anti-inflammatory homeostasis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. IL-6, by activating the STAT3 signaling pathway, regulates the recruitment of myeloid cells and neutrophils to inflammatory sites and promotes Th17 cell differentiation. The IL-6/STAT3 signaling axis is a key regulator in intestinal inflammation and plays a critical role in the transition from intestinal inflammation to cancer [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Based on hub genes, UC disease samples were divided into two subgroups. Immune infiltration analysis showed that the expression of hub genes is related to various immune cells and pathways.\u003c/p\u003e\u003cp\u003eTo further clarify the mechanism by which LOM alleviates UC, we characterized these five core genes using a single-cell dataset. scRNA-seq data was identified 11 major celltypes. Various algorithms showed that hub genes were highly expressed on macrophages. Macrophages, abundant in the colon, play a pivotal role in modulating local mucosal immune responses through their phenotypic diversity. As integral components of the immune system, macrophages exhibit plasticity, polarizing into either the M1 (pro-inflammatory) or M2 (anti-inflammatory) phenotype in response to diverse stimuli. Persistent activation of M1 macrophages triggers an excessive release of pro-inflammatory cytokines, disrupting colonic homeostasis and compromising the barrier function, which in turn amplifies intestinal inflammation. Conversely, M2 macrophages mitigate UC progression by secreting anti-inflammatory cytokines [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the pathogenesis of UC, overactivation of M1 macrophages leads to the production of pro-inflammatory cytokines like TNF-α, IL-1β, and IL-6, exacerbating intestinal inflammation. Glycolysis, a key metabolic pathway in M1 macrophages [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], is essential for their function. Inhibiting glycolysis significantly impacts typical inflammatory functions, including phagocytosis, ROS production, and the secretion of pro-inflammatory cytokines [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This metabolic process is HIF1A-dependent, and in macrophages, the TLR/NF-κB signaling pathway can regulate HIF1A transcription in an oxygen-independent manner [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Various inflammatory signals ultimately converge on NF-κB activation, a master regulator of macrophage function that modulates HIF1A gene expression [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].HIF1A also contributes to ferroptosis by elevating ROS and depleting glutathione (GSH).\u003c/p\u003e\u003cp\u003eTo further explore the pathological mechanism of macrophages in UC, we divided macrophages into M_HIGH and M_LOW subtypes based on the median expression of hub genes. Notably, hub genes were highly expressed in M_HIGH, which was the terminal differentiation stage of macrophages and closely related to disease occurrence. In cell communication analysis, M_HIGH showed extremely high communication strength with endothelial cells, and VEGFA is the key important cytokine. VEGFA is key for angiogenesis and vascular permeability [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], both essential for inflammation and tissue repair. Research [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] indicates that the HIF1A/VEGF signaling pathway is implicated in tumor immunity, inflammation, ischemia-reperfusion injury, oxidative stress, and other angiogenesis-related processes, and is closely related to the pathological mechanism of UC. The interplay between macrophages and endothelial cells is significant, as M1 macrophages perpetuate inflammation through cytokine secretion, endothelial cell activation, and the recruitment of additional immune cells to the inflamed sites [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Finally, it was confirmed in an \u003cem\u003ein vitro\u003c/em\u003e co-culture system that LPS-Caco2 cells could significantly activate TLR4/NF-κB in macrophages, induces STAT3 phosphorylation and increases the expression of HIF1A and related inflammatory factors TNF-α and IL-1β, while LOM can reverse this phenomenon. Meanwhile, the same results were verified in the specimens of animal models. Moreover, the histochemical results showed that VEGF was highly expressed in the model group, and LOM could also inhibit its expression. Therefore, our research results confirm that ferroptosis is indeed involved in the development process of UC and coordinates the interaction between macrophages and endothelial cells with core genes as the central nodes of the regulatory network.\u003c/p\u003e\u003cp\u003eIn summary, this study combined the chemical analysis of UPLC-MS with transcriptomics (RNA-seq) and single-cell (scRNA-seq) data to demonstrate that LOM exerts therapeutic effects by inhibiting the central gene regulation of the TLR4/NF-κB/VEGF pathway in macrophages, thereby alleviating UC symptoms and inflammatory responses.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study, based on UHPLC-QTOF-MS/MS and multi-omics analysis methods, explored the therapeutic potential of LOM in UC treatment through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Given that \u003cem\u003eLanxangia tsaoko\u003c/em\u003e is a traditional Chinese medicine that is both edible and medicinal, the results of this study suggest that daily consumption of \u003cem\u003eLanxangia tsaoko\u003c/em\u003e is beneficial for the prevention and treatment of UC, and also provide new ideas and methods for the subsequent screening of traditional Chinese medicines for the treatment of UC. However, our study has some limitations that should be acknowledged. This study did not screen out the key compounds or compound combinations for executive function. This will be further explored in subsequent research based on the pathways discovered in this study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYi Liu: designed experiments, analyzed and drafted original draft. Jun Ge: performed the experiments and drafted original draft. Xingke Zhu: contributed to do experiments and software technical assistance. Li Cheng: designed experiments,contributed to experiments method. Xianxian Liu: data curated assist and photos edited. Cheng Chen: validated data and draft. XingYi Zhang and Lei Guo: literature search. Zhengzheng Wu and Meili Liu: reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation of Hubei (Nos. 2023AFD155).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation of Hubei. The materials for the graphical abstract were sourced from the websites SciDraw and BioRender.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author:\u003c/strong\u003eYi Liu([email protected]);Cheng Chen([email protected]);Xianxian Liu([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-First Authors:\u003c/strong\u003eJun Ge([email protected]);Xingke Zhu([email protected]);Li Cheng([email protected]\u003c/p\u003e\n\u003cp\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther authors: \u003c/strong\u003eXingYi Zhang([email protected]);Lei Guo([email protected]);Zhengzheng Wu([email protected]);Meili liu([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliation:\u003c/strong\u003e\u003csup\u003e1\u003c/sup\u003e Hubei Provincial Key Laboratory for Chinese Medicine Resources and Chinese Medicine Chemistry, School of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China;\u003csup\u003e2 \u003c/sup\u003eHubei Shizhen Laboratory, Wuhan 430065, China;\u003csup\u003e3 \u003c/sup\u003eKey Laboratory of Chinese Medicinal Resource and Chinese Herbal Compound of the Ministry of Education, Wuhan 430065, China;\u003csup\u003e4 \u003c/sup\u003eFifth Hospital in Wuhan，Wuhan 430050, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eda Silva BC, Lyra AC, Rocha R, Santana GO. Epidemiology, demographic characteristics and prognostic predictors of ulcerative colitis. World J Gastroenterol. 2014;20(28):9458\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng S, Shen L, Yu X, Zhang L, Xu K, Xia Y, et al. The role of Nrf2 in the pathogenesis and treatment of ulcerative colitis. Front Immunol. 2023;14:1200111.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUngaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet. 2017;389(10080):1756\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong D, Mao C, Huang Y, Xu Y, Zhu Y. Ferroptosis in ulcerative colitis: Potential mechanisms and promising therapeutic targets. Biomed Pharmacother. 2024;175:116722.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKucharzik T, Koletzko S, Kannengiesser K, Dignass A. Ulcerative Colitis-Diagnostic and Therapeutic Algorithms. Dtsch Arztebl Int. 2020;117(33\u0026ndash;34):564\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing X, Guan H, Du H, Han X. Discussion on pathogenesis of ulcerative colitis based on traditional Chinese medicine theory. Shaanxi J Traditional Chin Med. 2024;45(10):1391\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang S, Xue Y, Chen D, Wang Z. Amomum tsao-ko Crevost \u0026amp; Lemari\u0026eacute;: a comprehensive review on traditional uses, botany, phytochemistry, and pharmacology. Phytochem Rev. 2022;21(5):1487\u0026ndash;521.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImran S, Bibi Y, Yang LE, Qayyum A, He W, Yang J, et al. Health-promoting compounds in Amomum villosum Lour and Amomum tsao-ko: Fruit essential oil exhibiting great potential for human health. Heliyon. 2024;10(5):e27492.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJialing L, Yangyang G, Jing Z, Xiaoyi T, Ping W, Liwei S, et al. Changes in serum inflammatory cytokine levels and intestinal flora in a self-healing dextran sodium sulfate-induced ulcerative colitis murine model. Life Sci. 2020;263:118587.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark JH, Ahn EK, Hwang MH, Park YJ, Cho YR, Ko HJ et al. Improvement of Obesity and Dyslipidemic Activity of Amomum tsao-ko in C57BL/6 Mice Fed a High-Carbohydrate Diet. Molecules. 2021;26(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShim KS, Hwang YH, Jang SA, Kim T, Ha H. Ethanol Extract of Amomum tsao-ko Ameliorates Ovariectomy-Induced Trabecular Loss and Fat Accumulation. Molecules. 2021;26(4):784.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen G, Qi H, Jiang L, Sun S, Zhang J, Yu J, et al. Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis. J Transl Med. 2024;22(1):1121.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Z, Hu D, Pei G, Zeng R, Yao Y. Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning. Front Immunol. 2023;14:1288699.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAibar S, Gonz\u0026aacute;lez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndreatta M, Carmona SJ, UCell. Robust and scalable single-cell gene signature scoring. Comput Struct Biotechnol J. 2021;19:3796\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhuva DD, Cursons J, Davis MJ. Stable gene expression for normalisation and single-sample scoring. Nucleic Acids Res. 2020;48(19):e113.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoureen N, Ye Z, Chen Y, Wang X, Zheng S. Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data. Elife. 2022;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu L, Wang JR, Henderson YC, Bai S, Yang J, Hu M et al. Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics. J Clin Invest. 2023;133(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu Y, Chen Y, Wang Z, Shen H, Xu L, Huang C, et al. Single-cell and spatial transcriptome profiling reveal CTHRC1\u0026thinsp;+\u0026thinsp;fibroblasts promote EMT through WNT5A signaling in colorectal cancer. J Transl Med. 2025;23(1):282.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong S, Lu Y, Peng G, Li J, Li W, Li M, et al. Furin inhibits epithelial cell injury and alleviates experimental colitis by activating the Nrf2-Gpx4 signaling pathway. Dig Liver Dis. 2021;53(10):1276\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Ma M, Wang X, Li J, Fang Z, Li J, et al. Celecoxib alleviates the DSS-induced ulcerative colitis in mice by enhancing intestinal barrier function, inhibiting ferroptosis and suppressing apoptosis. Immunopharmacol Immunotoxicol. 2024;46(2):240\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng B, Wang K, He H, Xu M, Li J, He P, et al. Biochanin A mitigates colitis by inhibiting ferroptosis-mediated intestinal barrier dysfunction, oxidative stress, and inflammation via the JAK2/STAT3 signaling pathway. Phytomedicine. 2025;141:156699.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDodson M, Castro-Portuguez R, Zhang DD. NRF2 plays a critical role in mitigating lipid peroxidation and ferroptosis. Redox Biol. 2019;23:101107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuentes-Pardo JD, Moreno-SanJuan S, Carazo \u0026Aacute;, Le\u0026oacute;n J. Heme Oxygenase-1 in Gastrointestinal Tract Health and Disease. Antioxid (Basel). 2020;9(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePousa ID, Mat\u0026eacute; J, Gisbert JP. Angiogenesis in inflammatory bowel disease. Eur J Clin Invest. 2008;38(2):73\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng K, Bai Y, Zhu Q, Hu B, Xu Y. Targeting VEGF-neuropilin interactions: a promising antitumor strategy. Drug Discov Today. 2019;24(2):656\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQueiro R, Coto P, Gonz\u0026aacute;lez-Lara L, Coto E. Genetic Variants of the NF-κB Pathway: Unraveling the Genetic Architecture of Psoriatic Disease. Int J Mol Sci. 2021;22(23).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang R, Ma Y, Zhan S, Zhang G, Cao L, Zhang X, et al. B7-H3 promotes colorectal cancer angiogenesis through activating the NF-κB pathway to induce VEGFA expression. Cell Death Dis. 2020;11(1):55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Liu Z, Li L, Jiang M, Tang Y, Zhou L, et al. Sesamin inhibits hypoxia-stimulated angiogenesis via the NF-κB p65/HIF-1α/VEGFA signaling pathway in human colorectal cancer. Food Funct. 2022;13(17):8989\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiasi F, Leonarduzzi G, Oteiza PI, Poli G. Inflammatory bowel disease: mechanisms, redox considerations, and therapeutic targets. Antioxid Redox Signal. 2013;19(14):1711\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Cao F, Yin HL, Huang ZJ, Lin ZT, Mao N, et al. Ferroptosis: past, present and future. Cell Death Dis. 2020;11(2):88.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen T, Liang L, Wang Y, Li X, Yang C. Ferroptosis and cuproptposis in kidney Diseases: dysfunction of cell metabolism. Apoptosis. 2024;29(3\u0026ndash;4):289\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Fang Y, Zhang Z, Luo Y, Zhang A, Lenahan C, et al. Ferroptosis: An emerging therapeutic target in stroke. J Neurochem. 2022;160(1):64\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTong J, Lan XT, Zhang Z, Liu Y, Sun DY, Wang XJ, et al. Ferroptosis inhibitor liproxstatin-1 alleviates metabolic dysfunction-associated fatty liver disease in mice: potential involvement of PANoptosis. Acta Pharmacol Sin. 2023;44(5):1014\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing SB, Chu XL, Jin YX, Jiang JJ, Zhao X, Yu M. Epigallocatechin gallate alleviates high-fat diet-induced hepatic lipotoxicity by targeting mitochondrial ROS-mediated ferroptosis. Front Pharmacol. 2023;14:1148814.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdegboro AG, Afolabi IS. Molecular mechanisms of mitochondria-mediated ferroptosis: a potential target for antimalarial interventions. Front Cell Dev Biol. 2024;12:1374735.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlover HL, Schreiner A, Dewson G, Tait SWG. Mitochondria and cell death. Nat Cell Biol. 2024;26(9):1434\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen QM. Nrf2 for protection against oxidant generation and mitochondrial damage in cardiac injury. Free Radic Biol Med. 2022;179:133\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng W, Long X, Liu PS, Xie X. The interplay of oncogenic signaling, oxidative stress and ferroptosis in cancer. Int J Cancer. 2023;153(5):918\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Sun R, Sun K, Yan C, Jiang J, Kong F, et al. The deubiquitinase USP11 ameliorates intervertebral disc degeneration by regulating oxidative stress-induced ferroptosis via deubiquitinating and stabilizing Sirt3. Redox Biol. 2023;62:102707.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Y, Li BG, Su YH, Zhao RX, Song P, Li H, et al. Potential activity of Traditional Chinese Medicine against Ulcerative colitis: A review. J Ethnopharmacol. 2022;289:115084.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun F, Yan C, Lv Y, Pu Z, Liao Z, Guo W, et al. Genome Sequencing of Amomum tsao-ko Provides Novel Insight Into Its Volatile Component Biosynthesis. Front Plant Sci. 2022;13:904178.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim HJ, Kim H, Lee JH, Hwangbo C. Toll-like receptor 4 (TLR4): new insight immune and aging. Immun Ageing. 2023;20(1):67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi B, Alli R, Vogel P, Geiger TL. IL-10 modulates DSS-induced colitis through a macrophage-ROS-NO axis. Mucosal Immunol. 2014;7(4):869\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarin M. Nuclear factor-kappaB in cancer development and progression. Nature. 2006;441(7092):431\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang K, Grivennikov SI, Karin M. Implications of anti-cytokine therapy in colorectal cancer and autoimmune diseases. Ann Rheum Dis. 2013;72(Suppl 2):ii100\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFormentini L, Santacatterina F, N\u0026uacute;\u0026ntilde;ez de Arenas C, Stamatakis K, L\u0026oacute;pez-Mart\u0026iacute;nez D, Logan A, et al. Mitochondrial ROS Production Protects the Intestine from Inflammation through Functional M2 Macrophage Polarization. Cell Rep. 2017;19(6):1202\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBain CC, Scott CL, Uronen-Hansson H, Gudjonsson S, Jansson O, Grip O, et al. Resident and pro-inflammatory macrophages in the colon represent alternative context-dependent fates of the same Ly6Chi monocyte precursors. Mucosal Immunol. 2013;6(3):498\u0026ndash;510.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eViola A, Munari F, S\u0026aacute;nchez-Rodr\u0026iacute;guez R, Scolaro T, Castegna A. The Metabolic Signature of Macrophage Responses. Front Immunol. 2019;10:1462.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePavlou S, Wang L, Xu H, Chen M. Higher phagocytic activity of thioglycollate-elicited peritoneal macrophages is related to metabolic status of the cells. J Inflamm (Lond). 2017;14:4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichl J, Ohlbaum DJ, Silverstein SC. 2-Deoxyglucose selectively inhibits Fc and complement receptor-mediated phagocytosis in mouse peritoneal macrophages II. Dissociation of the inhibitory effects of 2-deoxyglucose on phagocytosis and ATP generation. J Exp Med. 1976;144(6):1484\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Uden P, Kenneth NS, Rocha S. Regulation of hypoxia-inducible factor-1alpha by NF-kappaB. Biochem J. 2008;412(3):477\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRius J, Guma M, Schachtrup C, Akassoglou K, Zinkernagel AS, Nizet V, et al. NF-kappaB links innate immunity to the hypoxic response through transcriptional regulation of HIF-1alpha. Nature. 2008;453(7196):807\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlouin CC, Pag\u0026eacute; EL, Soucy GM, Richard DE. Hypoxic gene activation by lipopolysaccharide in macrophages: implication of hypoxia-inducible factor 1alpha. Blood. 2004;103(3):1124\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu ZL, Chen HH, Zheng LL, Sun LP, Shi L. Angiogenic signaling pathways and anti-angiogenic therapy for cancer. Signal Transduct Target Ther. 2023;8(1):198.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Wu P, Yu F, Luo G, Qing L, Tang J. HIF-1α Regulates Bone Homeostasis and Angiogenesis, Participating in the Occurrence of Bone Metabolic Diseases. Cells. 2022;11(22).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lanxangia tsaoko, Ulcerative colitis, UHPLC-QTOF-MS/MS, Multi-omics analysis, VEGF","lastPublishedDoi":"10.21203/rs.3.rs-7446251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7446251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003e\u003cem\u003eLanxangia tsaoko\u003c/em\u003e is a kind of traditional Chinese medicine that is both food and medicine and has a long history in the treatment of gastrointestinal diseases. Recent studies have found that the methanol extract of \u003cem\u003eLanxangia tsaoko\u003c/em\u003e(LOM) exhibits potent anti-inflammatory and antioxidant properties. These properties align with the pathophysiological mechanisms underlying ulcerative colitis(UC), suggesting that LOM may offer a promising therapeutic avenue for UC treatment.This study aimed to identify a low-toxicity, daily edible extract of traditional Chinese medicine as a candidate drug for the prevention and treatment of UC, and to clarify the mechanism of drug treatment for the disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study utilized UHPLC-QTOF-MS/MS, RNA-seq, and scRNA-seq analyses to determine the potential targets and mechanisms of LOM in the treatment of UC. Animal experiments, cell experiments and various molecular biology techniques were employed to evaluate the effects of LOM on UC symptoms, inflammation and ferroptosis, and to verify the related targets and pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWGCNA and immune infiltration analysis RNA-seq data identified five hub genes targeted by LOM. scRNA-seq analysis confirmed that the hub gene is mainly expressed in macrophages and affects endothelial cells to participate in disease progression through TLR4/NF-KB/VEGF.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLOM affects endothelial cells in alleviating the symptoms of UC by regulating the key and signaling pathways of macrophage hub genes. Based on the characteristic that Lanxangia tsaoko is both edible and medicinal, it has a very high potential for the prevention and treatment of UC, providing a scientific basis for its clinical application and subsequent development.\u003c/p\u003e","manuscriptTitle":"Targeting Macrophage Ferroptosis: A Mechanism of Lanxangia tsaoko methanol extract Inhibits VEGF to Attenuates ulcerative colitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 12:27:10","doi":"10.21203/rs.3.rs-7446251/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e5d9ec4-71c6-42f8-9345-65bb722ffcf3","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T09:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 12:27:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7446251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7446251","identity":"rs-7446251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}