Elucidating the gastroprotective mechanisms of Imperata cylindrica Beauv.var. major (Nees) C.E.Hubb through UHPLC-MS/MS and systems network pharmacology

Abstract

Imperata cylindrica Beauv.var. major (Nees) C.E.Hubb., commonly known as BaiMaoGen (BMG), a medicinal and edible traditional Chinese medicinal (TCM) herb commonly used in health supplements, has been observed to offer protective effects against gastrointestinal disorders. However, the specific bioactive compounds and their molecular mechanisms have not been fully elucidated. This study employed ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and systematic network pharmacology to analyze and identify the key active components and their interactions with biological targets. Thirty-six main active compounds, including 3,4-dihydroxybenzoic acid and p-hydroxybenzoic acid, were identified and analyzed for their interaction with key protein targets using molecular docking and dynamic simulations. This combined approach highlighted the therapeutic pathways involved, particularly the PI3K/AKT signaling pathways, providing new insights into the molecular basis of BMG’s gastroprotective effects. Our findings suggested that BMG’s complex multi-target action can potentially be harnessed to develop effective treatments for gastrointestinal toxicity.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-79483-z.

Introduction

Gastrointestinal disorders are prevalent and arise from a myriad of sources, including dietary habits, gastritis, gastric ulcers, emotional stress, and excessive physical or mental exertion. These conditions typically manifest with symptoms such as nausea, vomiting, abdominal pain, diarrhea, and fatigue^1–3. Despite the commonality of these symptoms, the underlying molecular mechanisms of gastrointestinal toxicity remain largely unexplored.

Network pharmacology, an emerging field underpinned by artificial intelligence and large-scale biomedical data, offers a powerful approach to deciphering these mechanisms. It leverages data mining and computational simulations to identify crucial biological targets, predict signaling pathways, and elucidate drug actions at the molecular level, thereby facilitating the rapid discovery of new biomarkers and therapeutic targets⁴. UHPLC-MS/MS complements this approach by providing precise molecular weights and structural data of compounds under investigation, enhancing both the sensitivity and selectivity of biochemical analyses. The technique’s capacity to monitor parent and product ions simultaneously significantly improves the reliability of compound identification, with detection limits reaching into the ng/mL range⁵.

TCM has gained international recognition, particularly its role in managing COVID-19 symptoms, due to its multi-target therapeutic strategies, minimal side effects, and cost-effectiveness. These characteristics make TCM a viable option for improving patient outcomes and quality of life⁶. BMG, the dried rhizome of the Gramineae plant, Imperata cylindrica Beauv.var. major(Nees) C.E.Hubb, is noted in the Chinese Pharmacopoeia for its properties of cooling the blood, stopping bleeding, clearing heat, and promoting urination. It is associated with the lung, stomach, and bladder meridian systems⁷, and its beneficial effects on the gastrointestinal system have led to its popularity in health supplements and beverages. According to certain research, they have a particular soothing impact on the gastrointestinal system⁸. However, the detailed molecular mechanisms by which BMG exerts its effects remain unclear.

This study aimed to dissect the active chemical constituents of BMG and their potential targets using UHPLC-MS/MS and bioinformatics techniques. By understanding these molecular interactions, we strived to lay a robust scientific foundation for subsequent pharmacological research on BMG’s efficacy in treating gastrointestinal toxicity. Furthermore, our findings were intended to guide the clinical application of TCM and contribute to the elucidation of its underlying molecular mechanisms, as depicted in the research flowchart (Fig. 1).

Fig. 1.

Workflow of the network pharmacological investigation strategy of BMG in the treatment of Gastrointestinal Toxicity.

Materials and methods

Materials and reagents

Materials and sample preparation

UHPLC-MS/MS (Agilent QTOF G6545B), acetonitrile (Merck, Germany); formic acid (Merck, Germany); BMG was purchased from Beijing Tongren Tang (Bozhou) Yinpian Co., Ltd., and identified as the dried rhizome of the Gramineae plant, Imperata cylindrica Beauv.var. major (Nees) C.E.Hubb. by Zhu Meili, a pharmacist at Jiangxi University of Chinese Medicine.

One gram of BMG powder (passed through a 40-mesh sieve) was precisely weighed and dissolved in a methanol solution (40: 1, mL/g). This mixture underwent ultrasound-assisted extraction for two hours, followed by centrifugation at 3500 r/min for 20 min to separate the supernatant. The supernatant was filtered through a 0.22 μm membrane for subsequent UHPLC-MS/MS analysis.

Chromatography conditions and MS parameters

Chromatographic separation of compounds was achieved on an Agilent ZORBAX Eclipse XDB-C18 (2.1 × 100 mm, 1.8 μm) for UHPLC-MS/MS. Column temperature (Temp) is 30 °C. Mobile phase A was 0.1% formic acid in the water, and phase B was acetonitrile; Gradient elution: 0–11 min, 5-98% B; 11–13 min, 98% B; Flow rate was 0.3 mL/min, the injection volume was 1 µL.

MS parameters: ESI ion source in positive and negative mode, Gas Temp (℃) was 325, Gas Flow (I/min) was 8, SheathGasTemp (℃) was 320, Nebulizer (psig) was 35, SheathGasFlow was 11, Column Temp: 45 ℃, Scan range was 50-1200 Da, Collision Energies: 10 ~ 40 eV.

Data Collection

Screening of active compounds and targets of BMG

Active compounds were identified through consultations of the UHPLC-MS/MS, TCMSP (https://old.tcmsp-e.com/tcmsp.php), and HERB (http://herb.ac.cn/) databases, as well as relevant literature. Compounds were recorded in the standard SMILES format obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and ChemDraw software. The absorption, distribution, metabolism, and excretion (ADME) properties were assessed using SwissADME (https://www.swissadme.ch/)⁹. Thirty-six active compounds were prioritized based on high gastrointestinal absorption and drug-likeness, with at least three affirmative drug-likeness properties. The potential targets of these compounds were predicted with a probability > 0.1 ¹⁰.

Establishment of a database for gastrointestinal toxicology targets

Differentially expressed genes (DEGs) associated with gastrointestinal toxicity were extracted from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database, Series: GSE54236. A batch effect correction was applied, and DEGs were selected based on a threshold of |logFC|>1 and P < 0.05 ¹¹. These DEGs were visualized using the R software package ggplot2 to create a volcano plot. A heatmap displayed the expression patterns of the top 50 DEGs. Additionally, disease targets related to gastrointestinal toxicity were compiled from the GeneCards (https://www.genecards.org) and OMIM (https://www.omim.org/) databases¹². Redundant targets were removed to create a consolidated gastrointestinal toxicity target library.

Establishment of the PPI network

An intersection analysis was conducted between the identified targets related to gastrointestinal toxicity and the predicted active targets of BMG using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). These intersecting targets were then uploaded to the STRING (https://string-db.org) database to construct a PPI (protein-protein interaction) network specific to Homo sapiens, with a confidence index threshold of ≥ 0.4¹³. Key targets within the PPI network were analyzed using Cytoscape 3.10.1 (https://www.cytoscape.org/) based on degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC)¹⁴, with parameters set to more than twice the median value¹⁵. Further refinement of central targets was achieved through maximum clique centrality (MCC) calculations using the Cytohubba plugin¹⁶. The MCODE plugin facilitated clustering analysis within the network in Cytoscape¹⁷.

Selection and analysis of key targets

Topological analysis of the PPI network, utilizing the MCC algorithm from the Cytohubba plugin, identified key genes. Expression profiles of these genes were obtained from the Human Protein Atlas (https://www.proteinatlas.org/) database, considering both gene and tissue expression levels¹⁸.

GO and KEGG enrichment analysis

The DAVID (https://david.ncifcrf.gov/) database was the functional annotation and integration tool, facilitating the biological significance analysis of extensive gene lists. This analysis resulted in GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments for the identified targets, filtered by FDR < 0.05 and P< 0.05. The analysis was categorized into molecular function (MF), cellular component (CC), and biological process (BP)¹⁹. The top 20 enriched GO terms and KEGG pathways were visualized online (https://www.bioinformatics.com.cn/).

Network construction

To elucidate BMG’s comprehensive molecular mechanism in mitigating gastrointestinal toxicity, B-C-T (BMG-compound-target) and C-T-P (compound-target-pathway) networks were constructed using Cytoscape 3.10.1. These networks integrated the top 20 pathways with their associated targets and compounds, facilitating a deeper understanding of the relationships between the pathways, compounds, and targets.

Molecular docking validation

Molecular docking is a pivotal technique in drug discovery that investigates the interactions between receptors (proteins) and ligands (small molecules) to predict their binding affinities and modes. In this investigation, we employed molecular docking to determine the binding efficacy of selected compounds from the BMG, as identified within the B-C-T network, against ten key targets. We utilized AutoDockTools 1.5.7 and AutoDock 4 for the docking studies²⁰. The process began with the acquisition of SDF files of the active compounds from PubChem and ChemDraw, along with the crystal structures of target proteins from the RCSB Protein Data Bank (https://www.rcsb.org/). Initial steps involved the preparation of the receptor proteins through dehydration and hydrogenation, followed by calculating Gasteiger charges using AutoDockTools 1.5.7. Both ligands and receptors were formatted into PDBQT for docking, which facilitates the assessment of binding feasibility based on spatial and energetic criteria. AutoDock 4 predicted the appropriate docking grid and macromolecular configurations. Ligand flexibility was considered by allowing free rotation of all rotatable bonds while the receptor remained rigid. A lower docking score indicates a higher likelihood of effective binding, and the conformation displaying the most favorable binding energy was further analyzed. The molecular docking results were visualized using Ligplot + 2.2.8 and PyMOL to generate detailed 2D and 3D images.

Molecular dynamics simulation

Molecular dynamics (MD) simulation is extensively used to model the behavior of atoms and molecules within a simulated system, providing insights into protein stability and interactions post-molecular docking²¹. This study utilized the GROMACS software platform with the CHARMM 36 force field to conduct MD simulations. The protein was solvated in a cubic box using the TIP3P water model under periodic boundary conditions. Electrostatic interactions were computed using the Particle-Mesh Ewald (PME) method, with a non-bonding distance cutoff set at 10 Ų². The MD simulation process included several phases: initially, the system’s energy was minimized using the steepest descent method, followed by a gradual temperature increase from 0 to 300 K over 60 ps. Simulations were performed under both NVT (constant number of particles, volume, and temperature) and NPT (constant number of particles, pressure, and temperature) conditions, with positional constraints applied during the first 5000 steps (2 fs each). A long-term simulation of 100 ns was executed with a timestep of 2 fs, recording trajectory data every 10 ps. The visualization of simulation outcomes was facilitated by QtGrace and PyMOL, providing a comprehensive analysis of the dynamics and stability of protein-ligand interactions²³.

Results

Screening active compounds and potential targets of BMG, and constructing a “B-C-T” network

Using UHPLC-Q-TOF-MS/MS technology (Fig. 2) and integrating both databases and published literature, we successfully identified 67 compounds in BMG, including MLT (Malathion), HMF (5-Hydroxymethylfurfural), Camphor (D), acetoin, 3,4-dihydroxybenzoic acid, p-hydroxybenzoic acid, 4-hydroxybenzaldehyde, Isovanillin, p-cymen-2-ol, and 1-monolinolein. The further determined by matching the MS/MS information of the reference compounds (Fig. S1). For detailed listings, refer to Supplementary Table S1.

Fig. 2.

The 2D and 3D total ion current chromatograms (A: Positive, B: Negative) of BMG by UHPLC-Q-TOF-MS/MS.

Following analysis with the SwissADME tool, 36 active compounds were selected based on high gastrointestinal absorption and drug-likeness criteria, each containing three or more affirmative responses in the drug-likeness category. The targets for these compounds were predicted, and after removing duplicates, 386 unique targets were determined with a probability > 0.1. In the GEO dataset GSE54236, we identified 870 DEGs (differentially expressed genes) linked to gastrointestinal toxicity. A volcanogram displaying these DEGs was shown in Fig. 3A, and a heatmap representing the expression patterns of the top 50 DEGs was depicted in Fig. 3B. Box plot indicated the distribution range and central tendency of 40 datasets (Fig. 3C). Furthermore, a comprehensive extraction of 3,275 targets associated with gastrointestinal toxicity was conducted using the GeneCards and OMIM databases. After deduplication, 1,892 disease targets were identified. Comparative analysis between the 386 BMG compound targets and the 1,892 disease targets yielded 160 common targets (Fig. 3D and Supplementary Tables S2, S3, and S4).

Fig. 3.

Screening of common targets for gastrointestinal toxicity and BMG: (A) Volcano plot displaying the distribution of genes in disease samples. Red and blue represent upregulated and downregulated genes, respectively, and gray indicates no significant difference. (B) Heatmap showing the expression patterns of the 50 DEGs. Columns correspond to samples and rows correspond to genes. (C) Box plot displaying the distribution range and central tendency of 40 datasets. (D) Venn diagram representing 160 common targets between active compound targets of BMG and gastrointestinal toxicity targets.

The B-C-T network, consisting of 195 nodes and 622 edges, as illustrated in Fig. S2 Notable compounds such as BMG32 (5-Methoxyflavone (degree: 57)), BMG30 (6-Hydroxy-5-methoxyflavone (degree:54)), BMG33 (2-Phenethyl-6-hydroxychromone (degree:45)), BMG35 (2-(2-Phenylethyl) chromone (degree:29)), BMG17 (3,4-Dimethoxyphenyl glucoside (degree:27)), BMG15 (Ethylparaben (degree:23)), BMG34 (5,2’-Dimethoxyflavone (degree:11)), BMG28 (Bifendate (degree:10)), BMG27 (Imperphenol B (degree:9)), BMG26 (Imperphenol C (degree:8)), and BMG22 (Vanillic Acid (degree:8)) showed high connectivity with protein targets, indicating a multifaceted role for BMG in mitigating gastrointestinal toxicity through multiple bioactive components.

PPI network of common targets

To delineate the mechanism by which BMG alleviates gastrointestinal toxicity, 160 common targets were analyzed within the STRING database to establish a PPI network comprising 159 nodes and 2213 edges. Key targets were identified through their central roles in the network based on metrics such as betweenness, closeness, and degree (Fig. 4A). Sub-network cluster analysis using the MCODE tool divided the targets into 8 distinct groups, further highlighting the complex interplay within the network (Fig. 4B). The top 10 central genes, determined via the MCC method from the CytoHubba plugin, were presented in Fig. 4C.

Fig. 4.

The process of identifying candidate targets through PPI analysis: 32 core targets were obtained from the 160 common targets selected by DC, BC, and CC. The size of the node was proportional to the target degree in the network. (A) The topological screening process of the PPI network. (B) The PPI network was clustered based on the MCODE plugin. (C) The central genes were selected from the PPI network using the CytoHubba plugin.

GO enrichment analysis

GO enrichment analysis was conducted on the 160 identified targets to uncover the broad spectrum of biological mechanisms through which BMG might exert its gastroprotective effects. This analysis resulted in the enrichment of 844 GO terms categorized into BP, MF, and CC. The findings, detailed in Supplementary Tables S5, S6, and S7, underscored significant involvement in processes like xenobiotic stimulus-response, protein phosphorylation, and activation of MAP kinase activity. The top 20 enriched BP, MF, and CC terms, highlighting the most gene-rich categories, were shown in Fig. 5A and summarized in a bar chart in Fig. 5B. The results indicated a predominant localization of target proteins to the plasma membrane, cytosol, and receptor complexes, elucidating the molecular landscape of BMG’s action in gastrointestinal protection.

Fig. 5.

GO enrichment analysis results: (A) Bubble plot of the top 10 BP, CC, and MF terms in the GO enrichment analysis. (B) Bar chart displaying the top 20 BP, CC, and MF terms in the GO enrichment analysis, respectively represented by green, orange, and purple bars.

KEGG analysis

KEGG pathway analysis was utilized to identify the pathways significantly impacted by BMG in mitigating gastrointestinal toxicity. We determined that 153 pathways were significantly enriched (Supplementary Table S8). The top 20 pathways, selected based on their count value, were detailed in Table 1. A B-T-P (BMG-compound-target-pathway) network comprising 219 nodes and 1036 edges was constructed using Cytoscape 3.10.1 to illustrate these pathways (Fig. S3A). A Sankey bubble diagram on the Microbial Information Platform visually represented the relationships between the enriched pathways and their corresponding targets (Fig. S3B). Key pathways implicated in the therapeutic effects of BMG included the Pathways in Cancer, PI3K-Akt signaling pathway, Prostate cancer, Bladder cancer, EGFR tyrosine kinase inhibitor resistance, Chemical carcinogenesis-receptor activation, MAPK signaling pathway, Proteoglycans in cancer, and HIF-1 signaling pathway, among others. Visualizations of the Pathways in Cancer and PI3K-Akt signaling pathway were also provided (Fig. 6A-B).

Table 1.

Enrichment ranking of the top 20 pathways.

Pathway ID	Pathways name	Count	Genes	P-Value
hsa05200	Pathways in cancer	44	RET, CAMK2B, CSF1R, GSK3B, HSP90AB1, FLT3, HDAC1, PTGS2, EGFR, IKBKB, CCND1, TERT, ERBB2, AKT1, MAPK1, EP300, JAK3, JAK1, PDGFRB, PDGFRA, JUN, MAP2K1, CREBBP, NOS2, DAPK1, MMP1, MMP2, STAT3, PRKCA, BRAF, F2, MMP9, ESR1, TGFBR1, PTK2, VEGFA, AR, CDK6, KIT, CDK2, PPARG, MET, FGFR1, PPARD	2.90E-18
hsa04151	PI3K-Akt signaling pathway	31	RET, CSF1R, GSK3B, FLT1, HSP90AB1, FLT3, EGFR, PIK3CG, IKBKB, CCND1, ERBB2, KDR, AKT1, MAPK1, JAK3, JAK1, MCL1, PDGFRB, PDGFRA, NTRK2, MAP2K1, SYK, PRKCA, PTK2, VEGFA, CDK6, KIT, CDK2, MET, TLR4, FGFR1	4.45E-13
hsa05215	Prostate cancer	18	PDGFRB, PDGFRA, GSK3B, MAP2K1, CREBBP, HSP90AB1, BRAF, MMP9, EGFR, IKBKB, AR, CCND1, ERBB2, CDK2, AKT1, EP300, MAPK1, FGFR1	6.05E-13
hsa05219	Bladder cancer	13	MAP2K1, DAPK1, MMP1, SRC, MMP2, BRAF, MMP9, EGFR, TYMP, VEGFA, CCND1, ERBB2, MAPK1	2.66E-12
hsa01521	EGFR tyrosine kinase inhibitor resistance	16	PDGFRB, PDGFRA, GSK3B, MAP2K1, SRC, STAT3, BRAF, PRKCA, EGFR, VEGFA, ERBB2, KDR, AKT1, MAPK1, MET, JAK1	4.41E-12
hsa05207	Chemical carcinogenesis - receptor activation	23	MAP2K1, JUN, CHRNA3, HSP90AB1, SRC, EPHX2, NR1I3, EPHX1, STAT3, PRKCA, AHR, ESR1, EGFR, VEGFA, AR, CCND1, CYP1A2, CYP1A1, CYP1B1, AKT1, MAPK1, PGR, PPARA	1.21E-11
hsa04010	MAPK signaling pathway	26	RET, CSF1R, FLT1, FLT3, EGFR, IKBKB, ERBB2, KDR, AKT1, MAPK1, PDGFRB, PDGFRA, HSPA8, NTRK2, JUN, MAP2K1, PLA2G4A, PRKCA, BRAF, MAPK14, TGFBR1, VEGFA, KIT, MAPT, MET, FGFR1	5.87E-11
hsa05205	Proteoglycans in cancer	21	CAMK2B, MAP2K1, SRC, MMP2, STAT3, BRAF, PRKCA, MAPK14, ESR1, MMP9, EGFR, PTK2, VEGFA, CCND1, ERBB2, KDR, AKT1, MAPK1, MET, TLR4, FGFR1	3.45E-10
hsa04066	HIF-1 signaling pathway	16	CAMK2B, MAP2K1, CREBBP, FLT1, NOS2, STAT3, SERPINE1, PRKCA, EGFR, VEGFA, ERBB2, AKT1, EP300, MAPK1, GAPDH, TLR4	5.30E-10
hsa05161	Hepatitis B	18	MAP2K1, CREBBP, JUN, SRC, STAT3, BRAF, PRKCA, MAPK14, MMP9, TGFBR1, IKBKB, CDK2, AKT1, EP300, MAPK1, JAK3, TLR4, JAK1	2.61E-09
hsa05223	Non-small cell lung cancer	13	RET, MAP2K1, STAT3, BRAF, PRKCA, EGFR, CDK6, CCND1, ERBB2, AKT1, MAPK1, JAK3, MET	3.25E-09
hsa05212	Pancreatic cancer	13	MAP2K1, STAT3, BRAF, EGFR, TGFBR1, VEGFA, IKBKB, CDK6, CCND1, ERBB2, AKT1, MAPK1, JAK1	6.18E-09
hsa01522	Endocrine resistance	14	MAP2K1, JUN, SRC, MMP2, BRAF, MAPK14, ESR1, MMP9, EGFR, PTK2, CCND1, ERBB2, AKT1, MAPK1	1.26E-08
hsa04510	Focal adhesion	19	PDGFRB, PDGFRA, GSK3B, MAP2K1, JUN, FLT1, SRC, BRAF, PRKCA, EGFR, PTK2, MYLK, VEGFA, CCND1, ERBB2, KDR, AKT1, MAPK1, MET	1.30E-08
hsa05206	MicroRNAs in cancer	23	PDGFRB, HDAC4, PDGFRA, MAP2K1, ABCC1, CREBBP, ABCB1, HDAC1, STAT3, PRKCA, PTGS2, MMP9, EGFR, VEGFA, IKBKB, CDK6, CCND1, ERBB2, CYP1B1, EP300, MAPK1, MET, MCL1	1.77E-08
hsa05221	Acute myeloid leukemia	12	IKBKB, CSF1R, MAP2K1, CCND1, FLT3, STAT3, KIT, MAPK1, AKT1, BRAF, MPO, PPARD	1.78E-08
hsa04015	Rap1 signaling pathway	19	PDGFRB, PDGFRA, CSF1R, MAP2K1, FLT1, SRC, BRAF, PRKCA, MAPK14, EGFR, VEGFA, CNR1, KIT, KDR, AKT1, MAPK1, DRD2, MET, FGFR1	2.21E-08
hsa05230	Central carbon metabolism in cancer	12	PDGFRB, RET, PDGFRA, MAP2K1, FLT3, ERBB2, KIT, MAPK1, AKT1, MET, EGFR, FGFR1	2.87E-08
hsa05417	Lipid and atherosclerosis	19	CAMK2B, HSPA8, GSK3B, JUN, HSP90AB1, HSPA5, MMP1, SRC, STAT3, PRKCA, MAPK14, MMP9, PTK2, IKBKB, CYP1A1, AKT1, MAPK1, PPARG, TLR4	3.18E-08
hsa05167	Kaposi sarcoma-associated herpesvirus infection	18	GSK3B, MAP2K1, CREBBP, JUN, SYK, SRC, STAT3, PTGS2, MAPK14, PIK3CG, VEGFA, IKBKB, CDK6, CCND1, AKT1, EP300, MAPK1, JAK1	4.03E-08

Fig. 6.

Distribution of key target points in the most relevant pathways. (A) Distribution of key target points in the signaling pathway of pathways in cancer. (B) Distribution of key target points in the PI3K-AKT signaling pathway. Red rectangles represent key targets. Assuming that the targets and genes involved in the pathways were shown in red.

Molecular docking

Molecular docking assessed the interaction patterns between ten key targets and eleven previously identified active compounds. Detailed spatial coordinates and other relevant data concerning these interactions were provided in Supplementary Table S9. The binding energies, indicative of interaction strength, ranged from − 0.99 to -9.24 kcal/mol, with lower energies suggesting more stable complexes (Fig. 7). AKT1 and Amphenol C notably demonstrated the lowest binding energy (-9.24 kcal/mol), indicating strong affinity. Amphenol C was observed to form hydrogen bonds and salt bridges with residues such as LYS, GLU, ARG, and ASN (Fig. 8A). Other significant interactions included hydrogen bonds, hydrophobic interactions, and π-stacking between various ligands and protein targets, detailed across Fig. 8B and J. These results underscored the potential of AKT1 and Amphenol C as key molecules for targeting gastrointestinal toxicity.

Fig. 7.

Heatmap of molecular docking score: Affinity energy (kcal/mol) of key targets and active compounds of herbs.

Fig. 8.

Docking patterns of key targets and specific active compounds with two-dimensional patterns of bond: AKT1-Imperphenol C(A), EGFR-Imperphenol B(B), ESR1-2-(2-Phenylethyl)chromone(C), GAPDH-2-(2-Phenylethyl)chromone (D), HSP90AB1-2-(2-Phenylethyl)chromone (E), JUN-Imperphenol B (F), PPARG-2-(2-Phenylethyl)chromone (G), PTGS2-2-(2-Phenylethyl)chromone (H), SRC-Imperphenol C (I), STAT3-Imperphenol B (J).

Molecular dynamics (MD)

MD simulations were conducted to evaluate the stability of the protein-ligand complexes, particularly focusing on the AKT1-Imphenol C complex. The simulation spanned 100 ns, providing insights into the post-docking mobility, trajectory, and conformation changes. The Root Mean Square Fluctuation (RMSF) and Root Mean Square Deviation (RMSD) analyses of AKT1-Imphenol C complex (Fig. 9A, B) and Imphenol C (Fig. 9C, D) were used to assess the dynamic behavior and stability. The RMSF values of the AKT1-Imphenol C complex ranged between 3.2 and 3.4 nm, indicating localized fluctuations within certain protein residues (Fig. 9A). The RMSD results demonstrated that the AKT1 protein maintained stability throughout the simulation, with the ligand achieving stability after 50 ns and maintaining it until the simulation’s conclusion (Fig. 9B). The findings suggest that the AKT1-Imphenol C complex is both stable and maintains a consistent conformation over time, affirming the reliability of the docking results.

Fig. 9.

RMSF and RMSD of MD simulation: (A) The RMSF of AKT1-Imperphenol C. (B) The RMSD of AKT1-Imperphenol C. (C) The RMSF of Imperphenol C. (D) The RMSD of Imperphenol C.

Discussion

TCM has a rich history spanning thousands of years, particularly noted for its efficacy in treating gastrointestinal disorders. BMG, a well-recognized medicinal and edible herb, has gained widespread usage as a functional health product. Despite its historical and contemporary applications, the specific bioactive compounds and their underlying molecular mechanisms contributing to their effectiveness in alleviating gastrointestinal toxicity have remained elusive. This study employed UHPLC-MS/MS, network pharmacology, and molecular docking approaches to elucidate the active compounds and their potential mechanisms in BMG.

Our comprehensive analysis identified 67 compounds in BMG utilizing UHPLC-Q-TOF-MS/MS, supplemented by database searches and literature reviews. Of these, 36 compounds were predicted as active based on SwissADME evaluations, targeting 386 biological targets. The construction of the B-C-T network revealed significant compounds such as 5-methoxyflavone, 6-Hydroxy-5-methoxyflavone, and 2-phenethyl-6-hydroxychromone, among others, which were intricately linked to gastrointestinal functionalities.

Empirical studies corroborate the efficacy of these compounds. For instance, 5-methoxyflavone has been shown to enhance gastric vascular perfusion and reduce leukocyte adhesion in mesenteric veins, offering protective effects against gastrointestinal damage²⁴. Vanillic acid has demonstrated the ability to protect intestinal epithelial cells by attenuating the virulence of Salmonella serotype Typhimurium, a known pathogenic bacterium²⁵. Similarly, MLT influences rumen microbiota, fostering an environment that suppresses harmful bacteria while enhancing beneficial ones^26,27. Furthermore, 3,4-dihydroxybenzoic acid has been associated with animal gut health, significantly correlating with wellness in domestic cats²⁸. 4-Hydroxybenzaldehyde and HMF have been noted to modulate gut microbiota composition and functionality^29,30. Notably, prolonged exposure to Ethylparaben is linked to changes in intestinal flora composition³¹. while Camphor has been observed to impact ruminal fermentation in vitro³². Therefore, BMG may act on the gastrointestinal tract through various active compounds and multiple pathways.

These findings suggested that BMG’s therapeutic effects on the gastrointestinal system were mediated through a spectrum of bioactive compounds acting via multiple molecular pathways. This multifaceted interaction underscores the complexity and efficacy of BMG as a traditional medicinal product, providing a strong foundation for its continued use and further scientific exploration within the framework of modern pharmacology.

In this study, we systematically analyzed ten key targets (GAPDH, AKT1, ESR1, JUN, PTGS2, SRC, PPARG, EGFR, STAT3, HSP90AB1) identified through the target interaction network due to their high centrality measures. The stability of GAPDH under human gastrointestinal conditions was assessed using a dynamic in vitro digestion model combined with mass spectrometry analysis, which showed rapid degradation upon exposure to stomach-like conditions³³. This underscores its antimicrobial peptide properties, likely integral to its clinical efficacy.

Variants rs1130233 and rs17431184 of AKT1 have been linked to an increased risk of gastrointestinal toxicity, suggesting a potential genetic predisposition that exacerbates condition severity³⁴. Our network pharmacology analysis further established a strong association between core targets such as JUN and PTGS2 and ulcerative colitis (UC)³⁵, pointing to a systemic impact on inflammatory bowel diseases. Additionally, ESR1 has been highlighted as a potential therapeutic target for gastric cancer³⁶, and the SFK member SRC is known to influence epithelial cell dynamics significantly, particularly in intestinal tissue regeneration and colon cancer metastasis³⁷.

PPARG co-activator 1 alpha (PPARGC1A) expression is notably altered in inflammatory bowel disease (IBD), with reduced levels observed in clinical IBD samples and animal models of colitis³⁸. This modulation of PPARGC1A activity appears critical for the therapeutic efficacy of treatments for acute colitis. Furthermore, the efficacy of anti-EGFR re-activation therapy in metastatic colorectal cancer showcases the potential of targeting specific molecular pathways in complex gastrointestinal diseases³⁹. The modulation of JAK2/STAT3 phosphorylation also presents a promising avenue for reducing intestinal inflammation⁴⁰. while the role of HSP90AB1 in cellular processes and cancer progression underscores the therapeutic potential of targeting heat shock proteins in oncology⁴¹.

Using KEGG pathway analysis, we identified several significantly altered pathways that were pivotal in mediating BMG’s gastroprotective effects. Notably, the Pathways in Cancer and PI3K-Akt signaling pathways were among the most impacted, illustrating the molecular basis through which BMG may exert its effects (Fig. 6A-B). Molecular docking and subsequent MD simulations affirmed the strong binding affinities and stability of interactions between key targets and active compounds, particularly highlighting the role of Imperphenol C in modulating these effects.

As a functional health beverage, BMG is currently well-liked on the market. Examples of these include BMG-sugarose water etc. BMG was described as having the nature of sweet and cold, returning to the stomach meridian, and having the effect of clearing stomach heat and producing stomach fluid⁷. It was also described as having the effect of detoxifying, drinking less alcohol, and halting vomiting in the Compendium of Materia Medica. According to TCM theory, dispelling stomach heat can alleviate belching and vomiting brought on by stomach heat. This has an impact akin to modern pharmaceuticals’ anti-inflammatory properties and won’t negatively impact the body. Promoting stomach fluid has the same effects of protecting the stomach mucosa and encouraging mucosal healing. Recent clinical findings demonstrated that BMG could help patients with their diarrhea problems, which were caused by their gut flora. These support the gastrointestinal protective effect of BMG and were consistent with the active components and molecular pathways we discovered, suggesting that our research is feasible and scientific.

Despite obtaining some important preliminary findings, there were still some limitations to this study. To investigate the role of BMG in gastrointestinal toxicity, we used advanced UHPLC-MS/MS, bioinformatics, and computational techniques. Therefore, the reliability and accuracy of the predictions need to be further verified by in vivo animal experiments.

Conclusion

This study has effectively integrated UHPLC-MS/MS, systems pharmacology, and molecular docking methodologies to elucidate the bioactive compounds, key targets, and underlying molecular mechanisms of BMG in treating gastrointestinal toxicity. Our findings suggest that BMG may modulate gastrointestinal toxicity through pathways typically associated with oncological processes, notably the Pathways in Cancer and the PI3K-Akt signaling pathway. The elucidation of these pathways and associated molecular interactions contributes significantly to our understanding of the pharmacodynamics of TCM in the context of gastrointestinal health. This study not only broadens the scope of scientific inquiry into the clinical applications of TCM but also lays a foundational framework for subsequent in vivo studies. By bridging the gap between traditional remedies and contemporary scientific validation, our research offers a compelling narrative for the continued exploration and integration of TCM in modern therapeutic regimens.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

CRediT authorship contribution statement：Jiaxin Zhou & Jianping Hu: Conceptualization, Data curation, Writing-original draft, Visualization, Investigation, Validation, Software. Jiancheng Liu: Conceptualization, Data curation, Visualization, Investigation, Validation. Wenchun Zhang: Funding acquisition, Writing-review editing, Investigation, Formal analysis, Methodology, Project administration.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82360893) and the Science and Technology Project of the Jiangxi Provincial Department of Education (No. GJJ211203).

CRediT authorship contribution statement.

Jiaxin Zhou&Jianping Hu: Conceptualization, Data curation, Writing-original draft, Visualization, Investigation, Validation, Software. Jiancheng Liu: Conceptualization, Data curation, Visualization, Investigation, Validation. Wenchun Zhang: Funding acquisition, Writing-review editing, Investigation, Formal analysis, Methodology, Project administration.

Data availability

The data extracted from the included studies and data used for analysis are provided in the supplementary material.

Declarations

Competing interests

The authors declare no competing interests.