Unraveling the mechanism of malancao in treating ulcerative colitis: A multi-omics approach

Abstract

BACKGROUND

Malancao (MLC) is a traditional Chinese medicine with a long history of utilization in treating ulcerative colitis (UC). Nevertheless, the precise molecular mechanisms underlying its efficacy remain elusive. This study leveraged ultra-high-performance liquid chromatography coupled with exactive mass spectrometry (UHPLC-QE-MS), network pharmacology, molecular docking (MD), and gene microarray analysis to discern the bioactive constituents and the potential mechanism of action of MLC in UC management.

AIM

To determine the ingredients related to MLC for treatment of UC using multiple databases to obtain potential targets for fishing.

METHODS

This research employs UHPLC-QE-MS for the identification of bioactive compounds present in MLC plant samples. Furthermore, the study integrates the identified MLC compound-related targets with publicly available databases to elucidate common drug disease targets. Additionally, the R programming language is utilized to predict the central targets and molecular pathways that MLC may impact in the treatment of UC. Finally, MD are conducted using AutoDock Vina software to assess the affinity of bioactive components to the main targets and confirm their therapeutic potential.

RESULTS

Firstly, through a comprehensive analysis of UHPLC-QE-MS data and public database resources, we identified 146 drug-disease cross targets related to 11 bioactive components. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis highlighted that common disease drug targets are primarily involved in oxidative stress management, lipid metabolism, atherosclerosis, and other processes. They also affect AGE-RAGE and apoptosis signaling pathways. Secondly, by analyzing the differences in diseases, we identified key research targets. These core targets are related to 11 active substances, including active ingredients such as quercetin and luteolin. Finally, MD analysis revealed the stability of compound-protein binding, particularly between JUN-Luteolin, JUN-Quercetin, HSP90AA1-Wogonin, and HSP90AA1-Rhein. Therefore, this suggests that MLC may help alleviate intestinal inflammation in UC, restore abnormal lipid accumulation, and regulate the expression levels of core proteins in the intestine.

CONCLUSION

The utilization of MLC has demonstrated notable therapeutic efficacy in the management of UC by means of the compound target interaction pathway. The amalgamation of botanical resources, metabolomics, natural products, MD, and gene chip technology presents a propitious methodology for investigating therapeutic targets of herbal medicines and discerning novel bioactive constituents.

Key Words: Malancao, Ulcerative colitis, Mass spectrum, Network pharmacology, Molecular docking

Core Tip: Eleven primary active constituents (nicotinic acid, luteolin, kaempferol, genkwanin, bessisterol, aloe emodin, wogonin, stigmasterol, rhein, quercetin, oroxylin A) and five key genes (AKT1, JUN, HSP90AA1, CASP3, IL6) were identified in malancao (MLC). Furthermore, three principal signaling pathways were determined through enrichment analysis from the Kyoto Encyclopedia of Genes and Genomes, suggesting that MLC exhibits a multi-component, multi-target, and multi-pathway approach in the treatment of ulcerative colitis.

INTRODUCTION

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by colonic ulceration. The cause of its pathogenesis remains unclear but is typically associated with factors such as genetics, immune dysregulation, external environmental stimuli, and intestinal flora[1,2]. Currently, UC has emerged as a significant threat to human health, primarily characterized by intestinal inflammation and elevated levels of inflammatory factors and increased macrophage activation[3,4]. The prevalence of UC is 0.3%, and it is steadily rising, imposing a serious burden on individuals, families, and society as a whole[5,6]. Present treatments for UC include drugs such as sulfonamide antibiotics, peroxisome proliferator-activated receptor γ-specific agonists, glucocorticoids, and immune stimulants, which regulate the levels of inflammatory factors through different mechanisms of action to exert efficacy[7,8]. However, due to significant concerns such as unsatisfactory patient outcomes, high costs, and severe side effects, there is a clinical imperative to explore alternative therapeutic strategies to improve the prognosis of individuals with UC.

Traditional Chinese medicine (TCM) is perceived as a potent therapeutic option, utilized for thousands of years to address a range of clinical conditions[9]. Malancao (MLC), a TCM, has been extensively used in Chinese medicine practice. Numerous long-term clinical investigations indicate that MLC dramatically lowers atypical colonic tissue proliferation and suppresses pro-inflammatory factor expression, thereby reducing symptoms and slowing the course of UC[10]. Pharmacologically, MLC exerts anti-inflammatory and immunomodulatory effects[11]. The fundamental rationale and prospective processes underpinning MLC's efficacy in UC, however, remain unknown.

Network pharmacology (NP) is an approach that studies the modulation of disease targets by drug chemistry[12,13]. Analyzes systematic bioinformation and constructs biological interaction networks using public data in conjunction with multi-omics, further identifying target proteins and signaling pathways related to drug efficacy and disease[14,15]. Therefore, the goal of this study is to employ an innovative strategy to identify the molecular mechanisms and potential targets of MLC through plant resources, metabolomics, NP, molecular docking (MD), and gene microarray technologies to gain insights into the therapeutic properties of MLC in the treatment of UC (Figure 1).

Figure 1 Technical roadmap of malancao in treating ulcerative colitis. GEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; UC: Ulcerative colitis; UHPLC-QE-MS: Ultra-high-performance liquid chromatography coupled with exactive mass spectrometry; MLC: Malancao.

MATERIALS AND METHODS

Preparation of MLC

MLC, which is widely distributed in various districts and counties in southern China, belongs to Kalimeris indica (L) Sch-Bip with a height of 30 cm-80 cm. MLC has slender underground rhizomes that are creeping and flat, and white in color. The leaves are simple and alternate, ranging from obovate to elliptic or lanceolate, with flower heads borne on the apices of upper branches, as shown in Figure 2.

Figure 2 Morphological characteristics of malancao. Malancao is a variety of Kalimeris indica (L) Sch-Bip.

Based on the collected data regarding the altitude, latitude, and longitude from ten regions in Dazu, Banan, Bishan, Fuling, Qijiang, Shizhu, Tongnan, Wanzhou, Wulong, and Shapingba districts of Chongqing Municipality, a digital elevation model image was generated using ArcGIS 10.2 software to illustrate the distribution map of the collection area for MLC, depicted in Figure 3. Three sampling points were randomly selected, and 3-10 samples were collected to create specimens at each point. These specimens were verified by Prof. Wei Shenghua from the School of Pharmacy at Guizhou University of Traditional Chinese Medicine and labeled as MLC-2021-09, before being stored in the Department of Pharmacy, Qijiang Hospital of Traditional Chinese Medicine.

Figure 3 The distribution of ten main producing areas of malancao. Sample site: The sampling locations are dispersed across multiple districts and counties within Chongqing, China.

Instruments and reagents

Ultra-high liquid phase (Vanquish, Thermo Fisher Scientific), high-resolution mass spectrometry (Orbitrap Exploris 120, Thermo Fisher Scientific), centrifuge (Heraeus Fresco17, Thermo Fisher Scientific), balance (BSA124S-CW, Thermo Fisher Scientific), ultrasound (PS-60AL, Shenzhen Leidebang Electronics Co., Ltd.).

Methanol and acetonitrile (CNW Technologies), ammonium acetate (SIGMA-ALDRICH), ethanoic acid (Fisher Chemical), ultrapure water (ddH20, Watson's).

Sample treatment

A 20 mg specimen was accurately weighed and amalgamated with 1000 μL of a specified extraction solution, comprising a methanol: Water ratio of 3 : 1 (v/v), and inclusive of an isotope-labeled internal standard mixture. This mixture was subjected to grinding at a frequency of 35 Hz for a duration of 4 min, followed by ultrasonication lasting 5 min within an ice-water bath. This procedure was repeated triply, after which the sample was conserved at -40 °C for an hour. Subsequently, it was centrifuged at a speed of 12000 rpm (equating to a relative centrifugal force of 13800 xg and a radius of 8.6 cm) maintained for 15 min at a temperature of 4 °C. To conclude, the resultant supernatant was methodically filtered utilizing a 0.22 μm microporous membrane and then decanted into a sterile glass vial in preparation for analytical procedures.

Ultra-high-performance liquid chromatography coupled with exactive mass spectrometry analysis

The target compounds were chromatographically separated on a Waters ACQUITY UPLC HSS T3 liquid chromatography column using a Vanquish ultra-high-performance liquid chromatography (UHPLC). The liquid chromatographic phase A was an aqueous phase containing 5 mmol/L ammonium acetate and 5 mmol/L acetic acid, and phase B was acetonitrile, followed by performing gradient elution with a column temperature of 35 °C, sample injection chamber temperature of 4 °C, and sample injection volume of 2 μL.

The Orbitrap Exploris 120 mass spectrometer conducted primary and secondary mass spectrometry data acquisition, controlled by Xcalibur software.

NP

NP, a novel method combining computer science and pharmacology, facilitates comprehensive elucidation of the mechanisms through which drugs act on diseases[16,17]. Currently, combining NP with multi-omics approaches is emerging as a future trend for studying the pharmacological effects and mechanisms of TCMs. Therefore, the active ingredients, targets, and signaling pathways of MLC in treating UC were identified from the perspectives of NP and plant metabolomics. The binding energies between drug molecules and their targets were calculated using MD simulations to further validate the predicted results of NP.

Collection of active compounds

All MLC compounds were identified based on the preliminary mass spectrometry analysis and previous literature studies. The TCM Systematic Pharmacology (TCMSP) database (https://old.tcmsp-e.com/) and SwissADME database (http://www.swissadme.ch/) were applied to screen all the candidate compounds of MLC, with the oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18.

Active compound target prediction

The active compound target prediction function in the TCMSP database was utilized to search for the relevant targets of the active ingredients in MLC. The UniProt database (https://www.uniprot.org/) was employed to convert all target proteins into gene symbols corresponding to the "Homo sapiens" species, standardizing gene names and biological information to prevent over-annotation of similar protein targets.

Prediction of UC-related targets and therapeutic targets

Using "UC" as the search phrase, comprehensive data on genes relevant to UC in "Homo sapiens" were collected from the GeneCards (https://www.genecards.org/), DisGeNET (https://www.disgenet.org/), and Online Mendelian Inheritance in Man databases (https://www.omim.org/). Following that, the Venny 2.1.0 online tool (https://bioinfogp.cnb.csic.es/tools/venny) was used to connect the relevant targets of the active compounds with those linked with UC.

Protein-protein interaction analysis

The STRING search tool (https://cn.string-db.org/) was utilized to query human gene data in order to retrieve protein-protein interaction (PPI) data for subsequent analysis, with a focus on the species "Homo sapiens" and a confidence score threshold of 0.4 or higher.

Network topology analysis

The "UC disease Gene-MLC targets" interaction network was constructed based on the molecular interaction data sourced from the STRING database. All networks were visualized using the Cytoscape 3.8.2 tool, an efficient tool for analyzing and visualizing molecular interaction networks. The topological significance of the central node of the network was evaluated by calculating the three parameters of degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) for each node[18,19]. A node with higher values for these three parameters indicates greater importance within the network, aiding in screening the key targets of MLC action in UC. Ultimately, targets with values ≥2 times the degree median were identified as core targets and incorporated for PPI visualization using cytoscape software.

Enrichment analysis

To explore the biological processes (BP) of UC proteins and their roles in signaling pathway transduction, disease-drug common targets were annotated, visualized, and integrated for discovery using the DAVID database (https://davidbioinformatics.nih.gov/) based on potential target proteins analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, potential targets were classified according to their associated BP, molecular functions (MF), and cellular localization through KEGG analysis.

Microarray and disease differential analysis

To obtain differentially expressed genes in inflammatory and non-inflammatory tissues of UC participants, the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was utilized to access GSE107499 gene chips. Hierarchical cluster analysis was conducted using the heatmap package in R (version 4.2.0), establishing disease-target libraries for subsequent analysis with significant expression variations (|log₂FC| > 1, P < 0.05).

MD

Utilizing AutoDock Vina's MD functions, the interactions between MLC's active components and UC-associated target proteins were investigated via a software platform. Structures of the small molecule compounds were obtained in Mol2 format from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), while the receptor proteins' crystal structures were procured from the Protein Data Bank database (https://www.rcsb.org/). The thorough docking procedure included pre-simulation preparation of receptor proteins and small molecules, docking site identification, and binding energy computation.

RESULTS

Screening and identification of active components

To investigate the active components of MLC, compounds present in MLC were identified using UHPLC coupled with exactive mass spectrometry (UHPLC-QE-MS) secondary mass spectrometry qualitative matching, supplemented by a literature search. Using ProteoWizard software, the raw data were transformed into mzXML format and subsequently analyzed with a tailored R package (kernel XCMS) for peak identification, extraction, alignment, and integration[20]. Thereafter, compounds were cross-referenced against the BiotreeDB secondary mass spectrometry database for substance annotation, using an algorithmic score threshold of 0.3. MLC candidate compounds were rigorously assessed, necessitating the OB of ≥ 30% and the DL of ≥ 0.18, leading to the identification of 11 active ingredients within MLC (Figure 4 and Figure 5).

Figure 4 Total ion chromatogram plot of positive and negative ion modes of malancao detected by ultra-high-performance liquid chromatography coupled with exactive mass spectrometry. A: Positive ion mode mass spectrometry; B: Negative ion mode mass spectrometry.

Figure 5 Bioactive Compouds of malancao, n (%). CID: Pubchem's compound identifier, a non-zero integer for a unique chemical structure; 2D: Two-dimensional flat structures of compounds.

Active ingredient action targets and disease-related targets

MLC primarily contains flavonoids, terpenoids, alkaloids, organic acids, and other chemical constituents. A collection of 133 compounds was gathered from previous literature studies[21-24]. The TCMSP was employed to search for relevant targets of the active ingredients in MLC, resulting in 204 potential targets after duplicate removal.

4844 UC-related targets were found using the search of disease targets in the GeneCards database, utilizing "UC" as the keyword. To further elucidate the mechanism of MLC's action in treating UC, the Venny 2.1.0 online platform was used to match the relevant targets of the active ingredients with those associated with UC, identifying 146 cross-targets. Subsequently, the Cytoscape software facilitated the construction of the drug-compound-target network of MLC against UC (Figure 6 and Figure 7).

Figure 6 Venn diagram of active ingredient targets versus disease targets. Blue area: Targets of the malancao 's action; Yellow area: Protein targets of ulcerative colitis. MLC: Malancao; UC: Ulcerative colitis.

Figure 7 Drug-compound-target network of malancao against ulcerative colitis. Yellow nodes: Drug; Blue nodes: Compounds; Red nodes: Protein targets.

PPI and network topology analysis

To delineate the mechanism of MLC's effect on UC in detail, the final 146 common targets of the compound and the disease were entered into the STRING to create a PPI network. Analysis of the STRING network indicated that, after removing invalid nodes, the network comprised 145 gene nodes and 2805 edge interactions, boasting an average node degree of 38.40. It was noted that the ADRA1B gene did not interact with any other gene. Within the network, network nodes represent protein targets, and edges represent target-protein target associations.

To gauge the significance of nodes within the entire network, network topology analysis was conducted, calculating the DC, BC, and CC parameters for each node, and identifying targets exceeding twice the median degree as core targets. This analysis revealed 28 targets with a degree ≥ 24, representing the network's core targets (Figure 8), which included AKT1, JUN, HSP90AA1, CASP3, IL6, EGFR, RELA, MYC, MAPK1, among other targets.

Figure 8  Protein-protein interaction network between drug-disease related targets and core targets.

Enrichment analysis

Based on GO classification analysis, the target proteins were categorized into primary metabolic process, cellular process, biological regulation, and cellular communication. Regarding BP, 32 proteins were involved in response to lipopolysaccharide, 32 in response to reactive oxygen species, and 32 in response to molecule of bacterial origin. In MF, 18 proteins were implicated in ubiquitin-like protein ligase binding, 10 in nuclear receptor activity, and 10 in ligand-activated transcription factor activity. In CC, 17 proteins participated in membrane raft signaling, 9 in serine/threonine protein kinase complex, and 7 in cyclin-dependent protein kinase holoenzyme complex processes. Consequently, MLC could be utilized for the treatment of UC through several pathways, such as regulating protein secretion, and activating polymerase activity. The final GO classification annotation analysis results are displayed in (Figure 9A).

Figure 9 Visualization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. A: Visualization of Gene Ontology classification annotation analysis; B: Visualization of Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A node between two lines symbolizes interaction, with larger nodes indicating stronger relationships.

To unravel the mechanism of MLC's therapeutic action, the assembled protein targets were inputted into the KEGG database to pinpoint relevant pathways. A total of 137 target genes were enriched based on KEGG analysis with P < 0.05, predominantly involving in lipid, atherosclerosis, diabetes, inflammation, cancer, and virus-related pathways. In the exploration of inflammation-related pathways, AGE-RAGE, TNF, NF-κB, IL-17, and PI3K-Akt were the most prevalent signaling pathways[25]. The top 20 entries encompassing 102 gene targets most significantly enriched by MLC for UC were chosen for KEGG visualization analysis, and the relationship between these enriched pathways and the related targets is shown in (Figure 9B). Within the related signaling pathways, the AGE-RAGE pathway emerged as the most prominent, with 29 related target genes enriched, including CASP3, JUN, AKT1, VEGFA, CCND1, MMP2, MAPK1, CDK4, IL6, MAPK8, STAT1, IL1B, CCL2, IL1A, MAPK14, and others. This suggests that the AGE-RAGE pathway is a potential pathway in the treatment of UC with MLC.

Disease differential analysis

Gene expression profiles pertinent to UC were procured from the GEO database using "UC", "Homo sapiens", and "array expression profiles" as search terms. The microarray data file (GSE107499) was ultimately retrieved for further analysis. The experimental platform file (GPL15207) utilized in the above study was the Affymetrix Human Gene Expression Array, comprising a total sample size of 119 cases, with 51 female patients and 68 male patients. The microarray data was scrutinized for 18835 gene targets, encompassing 849 genes with significant differences, 309 up-regulated genes, and 540 down-regulated genes. Furthermore, the microarray data outlined the comprehensive gene expression in inflamed versus non-inflamed colon tissues in individuals with UC. Differential expression analysis was conducted to ascertain whether PPI core targets exhibited disparate expressions between inflamed and non-inflamed samples, with P < 0.05 indicating statistically significant. Subsequently, box plots were used to show the specific expression of the target genes (Figure 10). The findings indicated that the top 20 core target genes in degree like JUN, HSP90AA1, EGFR, and VEGFA were lowly expressed in inflamed colon tissues, while IL6, MYC, CCND1, IL1B, ESR1, STAT1, MMP9, and HIF1A were markedly expressed in inflamed colon tissues. As the gene expression profiles were sourced from GEO, we were exempt from requiring ethical approval and conducting new experiments on patients or animals.

Figure 10  Disease differential analysis for ulcerative colitis in Gene Expression Omnibus database. A: Heatmap; B: Volcano plot; C-N: Show the differential expression of degree's top 20 core target genes in inflamed and non-inflamed colonic tissues, respectively.

MD validation

Based on the PPI interaction network, key targets were identified by MD to further validate the affinity of the corresponding chemical components of the drug to the protein. The small molecule ligand and protein receptor files necessary for the study were obtained from the Protein Data Bank database. All receptor structures were processed to remove organic matter and water molecules, and to add hydrogenated charge distribution. Ligand small molecule structures were optimized using Chem 3D software, while PyMOL software facilitated the optimization of both ligand compounds and receptors. MD was performed with AutoDock Vina software. The top five core targets were molecularly docked with their corresponding eight active ingredients based on the degree value. MD results indicated binding energies between the active ingredients and the target proteins ranging from -5.9 kcal/mol to -8.6 kcal/mol. Lower binding energies signify greater affinity between the active ingredients and UC's core targets (Table 1). Additionally, four target protein-active molecules with low binding energies were selected to plot a docking pattern, as shown in Figure 11. The results further demonstrated that the active components of MLC, including luteolin, quercetin, wogonin, and rheinic acid, acted on the core targets of protein kinase receptors and exhibited good affinity for the key targets, suggesting that the active components of MLC can treat or alleviate UC through these targets.

Figure 11  Molecular docking of some core targets with active ingredients. A: Docking models for JUN-Luteolin; B: Docking models for JUN-Quercetin; C: Docking models for HSP90AA1-Wogonin; D: Docking models for HSP90AA1-Rhein.

Table 1 Binding energy of eight active components and five target gene.

Chemical name	Chemical formula	Protein data bank ID	Target	Binding energy (kcal/mol)
Luteolin	C15H10O6	1H10	AKT1	-6.2
		1A02	JUN	-8.6
		1BYQ	HSP90AA1	-7.4
		1CP3	CASP3	-7.3
		1ALU	IL6	-7.1
Quercetin	C15H10O7	1H10	AKT1	-5.9
		1A02	JUN	-7.9
		1BYQ	HSP90AA1	-7.4
		1CP3	CASP3	-7.4
		1ALU	IL6	-7.1
Wogonin	C16H12O5	1H10	AKT1	-6.1
		1A02	JUN	-7.7
		1BYQ	HSP90AA1	-7.9
		1CP3	CASP3	-7.4
		1ALU	IL6	-6.0
Kaempferol	C15H10O6	1H10	AKT1	-6.0
		1A02	JUN	-7.1
		1BYQ	HSP90AA1	-7.1
		1CP3	CASP3	-7.6
Rhein	C15H8O6	1A02	JUN	-7.4
		1BYQ	HSP90AA1	-8.0
Aloe-emodin	C15H10O5	1BYQ	HSP90AA1	-6.4
		1CP3	CASP3	-7.0
Genkwanin	C16H12O5	1BYQ	HSP90AA1	-7.0
Oroxylin A	C16H12O5	1BYQ	HSP90AA1	-7.0
		1CP3	CASP3	-7.2
		1ALU	IL6	-6.0

AKT1: AKT serine/threonine kinase 1; JUN: Jun proto-oncogene, activator protein 1 transcription factor subunit; HSP90AA1: Heat shock protein 90 Alpha family class A member 1; CASP3: Caspase 3; IL6: Interleukin 6.

DISCUSSION

UC has emerged as a global disease in recent years, with a growing frequency in developed countries. Inflammation has been linked to the development of numerous cancers, according to epidemiological studies. Chronic inflammation of the intestinal mucosa, a major risk factor for colorectal cancer, is a side effect of UC development, with damage to intestinal epithelial cells being one of the primary causes of UC development[26-28]. Mesalazine (5-ASA) and glucocorticoids are the principal treatments for UC according to current diagnostic and therapeutic guidelines. Mesalazine is the conventional treatment for mild to severe UC that is not complex. Oral budesonide-MMX, a new formulation of budesonide in a multimatrix system, is recommended for mild to moderately active inflammatory UC that has not responded to mesalazine[29,30]. Kucharzik noted that systemic glucocorticoids are recommended for UC treatment and serve as the primary approach for patients with acute, severe UC when other methods fail to induce remission.

The existing pharmacological treatment of UC is accompanied by a series of problems and a high incidence of adverse effects[31,32]. Moreover, multiple morbidities tend to occur during treatment, and the long-term use of drugs such as sulfonamides and glucocorticosteroids can lead to a gradual decrease in medication adherence and a substantial economic burden for patients[33]. Therefore, alternative treatments with plant medicine are particularly important.

Techniques such as proteomics, plant metabolomics, and both in vivo and in vitro assays have been widely employed to explore disease diagnosis and drug therapy mechanisms, as evidenced by prior studies[34-36]. Recent findings indicate that Kuijieling decoction is closely linked with T cell activation, Th17 cell differentiation, and immune response. Furthermore, Kuijieling decoction has been observed to enhance vitamin A metabolism and RA/RARα signaling in UC rats and T lymphocytes[37]. Investigated loganic acid's impact on UC using in vivo and in vitro methods, revealing its potential to diminish oxidative and inflammatory reactions triggered by lipopolysaccharide and dextran sodium sulfate in UC[38]. This action is mediated by the regulation of TLR4/NF-κB and SIRT1/Nrf2 pathways. Similarly, Wang et al[39] discovered that Siwu decoction modulates UC's mechanisms by suppressing the STAT3 and NF-κB pathways, reducing inflammatory cytokine production, and enhancing epithelial repair in experimental colitis.

In the study, HPLC, NP, and bioinformatics were initially applied to successfully identify 11 active molecules and 3 important pathways associated with UC. The GO enrichment analysis indicated that MLC might exert its anti-UC effects by regulating biological functions. Pathway enrichment analysis revealed that many other diseases besides UC were also enriched, possibly due to the presence of the same molecular targets in different pathological processes of the disease. This is both a limitation of NP and a point of divergence for research directions. Therefore, enrichment pathways closely related to UC were selected for our study. Eventually, 28 core targets of the drug-disease target network were identified, and three pathways related to UC, were significantly enriched based on network construction and central network evaluation. Studies have indicated that RAGE signaling plays a role in the development of intestinal inflammation and permeability in both Crohn's disease and UC[40]. TNF-α, a crucial cytokine involved in the pathogenesis of inflammatory bowel disease, is traditionally viewed as a key mediator in amplifying mucosal inflammation, which is a central process of Crohn's disease pathogenesis[41-43]. Emerging evidence supports the significant role of the IL-23/IL-17 axis in the development of UC, demonstrating that inhibiting the IL-23/IL-17 axis and the downstream activation of pro-inflammatory factors is essential for suppressing the inflammatory response and alleviating UC[44-46]. Therefore, it is evident that MLC's treatment of UC primarily directly influences the AGE-RAGE pathway, TNF pathway, and IL-17 pathway, with the AGE-RAGE pathway being the most closely related to UC. Additionally, disease differential analysis revealed variations in the expression of each protein target between inflamed and non-inflamed samples, and identified protein targets with significant differences for subsequent studies. Lastly, MD verified that the binding energy of the core target was lower than -5.9 kcal/mol, suggesting that the compounds adhere tightly to the UC targets, promising better therapeutic outcomes. Using the aforementioned methods, we can more distinctly identify the targets and specific mechanisms of plant medicine in treating diseases and pinpoint key active molecules, which holds considerable significance for the development of plant medicine and disease treatment.

CONCLUSION

In this study, a novel combinatorial strategy based on plant resources, plant metabolomics, NP, MD techniques, and gene chip analysis techniques was first applied to pinpoint potential targets and mechanisms of MLC in treating UC. This will establish a new paradigm for uncovering the potential mechanisms underlying the pharmacological effects of medicinal plants. Furthermore, the study furnishes information and a theoretical basis for deeper exploration of the mechanism, bolstering supports clinical practice. Future systematic molecular biology experiments are necessary to verify the precise mechanisms.