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Abstract

Background

Methicillin-resistant Staphylococcus aureus (MRSA) is a major global health threat, causing infections that are difficult to treat due to resistance to standard antibiotics. The high mortality rates and limited treatment options for MRSA underscore the urgent need for novel therapeutic agents. Chelerythrine, a natural alkaloid, has shown potent antimicrobial activity against MRSA. However, the molecular mechanisms underlying its antibacterial effects, particularly its impact on bacterial proteomes, remain poorly understood. This study investigates chelerythrine's antibacterial efficacy and its time-dependent effects on the MRSA proteome to uncover key metabolic pathways targeted by this compound.

Methods

The minimum inhibitory concentration (MIC) of chelerythrine was determined using a microdilution method. Proteomic changes were analyzed by label-free quantitative mass spectrometry at 30, 60, and 120 min post-exposure. Fuzzy C-means (FCM) clustering was employed to identify protein expression patterns.

Results

Chelerythrine exhibited with a MIC of 19.54 mg/L against MRSA, with dose-dependent growth inhibition. Proteomic analysis identified 1037 proteins, with 447, 340, and 265 proteins showing significant changes at 30, 60, and 120 min, respectively. FCM clustering revealed three protein clusters, with Cluster 3 (36 proteins) showing specific downregulation under chelerythrine treatment. Gene Ontology (GO) and KEGG analyses indicated enrichment in metabolic pathways, particularly glycolysis, the tricarboxylic acid cycle, and arginine catabolism, highlighting chelerythrine's effect on bacterial energy production.

Conclusion

Chelerythrine disrupts key metabolic pathways in MRSA, particularly those involved in energy production and amino acid metabolism, suggesting its potential as an antimicrobial agent.

Keywords

chelerythrine proteomics time-dependent changes metabolic pathways antimicrobial agents

Introduction

Methicillin-resistant Staphylococcus aureus (MRSA) is a major public health concern, causing a wide range of infections in both healthcare and community settings. MRSA infections are associated with high morbidity and mortality, with bloodstream infections having a reported mortality rate of 20%–30% despite antibiotic treatment.^1-4 Invasive MRSA infections, such as bacteremia, endocarditis, pneumonia, and osteomyelitis, lead to prolonged hospitalization, increased healthcare costs, and poor clinical outcomes, particularly in immunocompromised patients.⁵ In addition, community-acquired MRSA (CA-MRSA) has been responsible for outbreaks of skin and soft tissue infections (SSTIs), particularly among individuals in close-contact environments, such as athletes, military personnel, and schoolchildren.⁶

The increasing prevalence of multidrug-resistant MRSA strains has significantly reduced the efficacy of conventional antibiotics. Currently, vancomycin, daptomycin, linezolid, and ceftaroline remain the primary treatment options for MRSA infections.⁷ However, emerging resistance to these antibiotics has been reported, underscoring the urgent need for alternative therapeutic agents. Chelerythrine, a benzophenanthridine alkaloid isolated from Zanthoxylum species, has demonstrated broad-spectrum antimicrobial activity, including efficacy against drug-resistant pathogens such as MRSA and extended-spectrum β-lactamase (ESBL)-producing bacteria.⁸ Previous studies have shown that chelerythrine exerts antibacterial effects by disrupting bacterial membranes, inhibiting biofilm formation, and interfering with key enzymatic processes.^9,10 However, the detailed molecular mechanisms underlying its antibacterial activity remain poorly understood.

Proteomics has emerged as a powerful approach for investigating the global effects of antimicrobial agents on bacterial physiology. By analyzing proteomic changes, researchers can identify disrupted metabolic pathways, stress responses, and resistance mechanisms, offering deeper insights into drug action at the molecular level.¹¹ For example, proteomic studies on Escherichia coli and Pseudomonas aeruginosa have revealed antimicrobial-induced alterations in energy metabolism, protein synthesis, and membrane integrity.^12,13 However, the proteomic response of S. aureus, particularly MRSA, to chelerythrine has not been comprehensively explored.

This study aims to fill this gap by employing label-free quantitative mass spectrometry to investigate time-dependent proteomic changes induced by chelerythrine in MRSA. By mapping alterations in protein expression, we identify key metabolic pathways, such as glycolysis, the tricarboxylic acid (TCA) cycle, and arginine catabolism, that are disrupted by chelerythrine. These findings provide novel insights into its antibacterial mechanisms, potentially guiding the development of synergistic antimicrobial strategies or optimization of chelerythrine-based therapies against multidrug-resistant pathogens.

Materials and Methods

Bacterial Strains and Culture Conditions

Methicillin-resistant Staphylococcus aureus (MRSA) strain ATCC 43300 was used in this study. Bacteria were cultured in Luria-Bertani (LB) broth at 37 °C with shaking at 200 rpm. For experiments, MRSA cultures were grown to mid-logarithmic phase, reaching an optical density at 600 nm (OD600) of approximately 0.6, prior to treatment with chelerythrine.

Chelerythrine Preparation and Treatment

Chelerythrine (purity ≥ 98%, Sigma-Aldrich, St. Louis, MO, USA; CAS No.: 34316-15-9; chemical structure shown in Figure S1) was dissolved in 10% dimethyl sulfoxide (DMSO) to prepare a stock solution of 10 mM (348.37 μg/mL). In preliminary experiments, chelerythrine was serially diluted in LB broth to achieve final concentrations of 175.94, 58.65, 19.54, 6.52, 2.17, and 0.72 μg/mL. The minimum inhibitory concentration (MIC) of chelerythrine against MRSA was determined to be 19.54 μg/mL. Based on this, the working concentration of chelerythrine (19.54 μg/mL) was selected for subsequent experiments. MRSA cultures were treated with chelerythrine for 30, 60, or 120 min at 37 °C. Untreated MRSA cultures served as controls. After treatment, bacterial cells were harvested by centrifugation at 5000 × g for 10 min and stored at −80 °C until proteomic analysis.

Protein Extraction

Proteins were extracted from MRSA cells using a bacterial protein extraction kit (Beyotime, China). Briefly, bacterial pellets were resuspended in 1 mL lysis buffer containing protease inhibitors (Roche, Basel, Switzerland) and subjected to three freeze-thaw cycles. The resulting lysates were sonicated on ice for 5 min (10-s pulse with 10-s rest) and centrifuged at 12,000 × g for 10 min at 4 °C. The supernatants containing the proteins were collected and quantified using the BCA protein assay (Pierce, Rockford, IL, USA).

Label-Free Quantitative Proteomic Analysis

Protein samples were digested with trypsin (Promega, Madison, WI, USA) using the filter-aided sample preparation (FASP) method.^14,15 The resulting peptides were cleaned up using C18 solid-phase extraction (Sep-Pak, Waters, Milford, MA, USA) and dried under vacuum. Peptide concentrations were determined, and an equal amount of peptides from each sample was analyzed by label-free quantitative mass spectrometry. Mass spectrometric analysis was performed on a Q Exactive mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with an Easy nLC-1200 liquid chromatography system (Thermo Fisher Scientific). Peptides were separated on a C18 column (75 μm × 50 cm, 2 μm particle size) with a 120-min gradient of 5–80% acetonitrile in 0.1% formic acid at a flow rate of 300 nL/min. Data acquisition was conducted in a data-dependent mode, with full scan MS spectra acquired at a resolution of 70,000 (m/z 350-1500) and top 10 most intense peptides selected for higher-energy collision dissociation (HCD) fragmentation.

Protein Identification and Quantification

The raw data were processed using the MaxQuant software (version 1.6.0.16) for peptide identification and quantification.¹⁶ Protein identification was performed by searching the data against the Uniprot Staphylococcus aureus database (version 2018) with a false discovery rate (FDR) threshold of 1% for both peptides and proteins.¹⁷ The parameters used for the search included a maximum of two missed cleavages, a minimum peptide length of 7 amino acids, and carbamidomethylation of cysteine as a fixed modification. Oxidation of methionine and acetylation of the protein N-terminus were set as variable modifications. Quantification was performed using label-free intensity-based absolute quantification (iBAQ).

Differential Protein Expression Analysis

Differential protein expression was analyzed using Perseus software (version 1.6.0.7). Only proteins identified with at least two peptides were considered for analysis. Significant changes in protein abundance were determined by one-way analysis of variance (ANOVA) with a p-value < 0.05 and a fold-change threshold of ≥2. Protein clustering was performed using Fuzzy C-means (FCM) analysis to group proteins based on their temporal expression patterns following chelerythrine exposure.

Functional Enrichment Analysis

Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to identify the biological processes and pathways associated with differentially expressed proteins. GO enrichment analysis was conducted using the GO database (Gene Ontology Consortium), while KEGG pathway analysis was performed using the KEGG database. The Fisher's exact test was applied to determine the statistical significance of enrichment, with a p-value threshold of <0.05.

Statistical Analysis

All data were analyzed using GraphPad Prism software (version 8.3, GraphPad Software, San Diego, CA, USA). Data are presented as the mean ± standard deviation (SD) from three independent replicates. For comparison of protein expression at different time points, one-way ANOVA was performed followed by post-hoc Tukey's test. A p-value of <0.05 was considered statistically significant.

Results

Chelerythrine Effectively Inhibits the Growth of MRSA

The antimicrobial activity of chelerythrine against MRSA was assessed using a microdilution method to determine the minimum inhibitory concentration (MIC). The MIC was calculated to be 19.54 mg/L, demonstrating the compound's potent antibacterial activity. To evaluate the dynamic effect of chelerythrine on MRSA growth, bacterial cultures were treated with varying concentrations of the compound (0, 5, 10, 15, 19.54, and 25 mg/L) and monitored for 24 h. The growth curves indicated a clear dose-dependent inhibition (Figure 1). At concentrations below the MIC (5 and 10 mg/L), the bacterial growth rate was reduced but not entirely suppressed, with extended lag phases and slower exponential growth. Near the MIC (15 mg/L), bacterial proliferation was significantly impeded, though not completely halted. At the MIC (19.54 mg/L) and higher concentrations (25 mg/L), bacterial growth was fully inhibited, with no measurable increases in optical density over time.

Figure 1.

Growth inhibition of MRSA by chelerythrine at varying concentrations. Growth curves represent optical density at 600 nm (OD600) over a 24-h period. The untreated control group exhibited normal growth, while chelerythrine treatment at 19.54 mg/L (MIC) and 25 mg/L resulted in complete inhibition of bacterial growth. Data are presented as the mean ± standard deviation (SD) from three independent replicates.

Chelerythrine Induces Time-Dependent Proteome Changes in MRSA

The proteomic impact of chelerythrine on MRSA was investigated using label-free quantitative mass spectrometry, profiling protein expression at 30, 60, and 120 min post-exposure. Untreated MRSA samples at the same time points were used as controls. This analysis identified 8918 unique peptides corresponding to 1037 proteins. Chelerythrine exposure led to significant changes in protein abundances, with 447 proteins affected at 30 min (360 upregulated, 87 downregulated), 340 at 60 min (290 upregulated, 50 downregulated), and 265 at 120 min (210 upregulated, 55 downregulated) (Figure 2A-C, Table S1). However, some protein abundance changes could be attributed to natural temporal dynamics rather than drug-induced effects. To address this, the temporal dynamics of protein expression in both the control and chelerythrine -treated groups were analyzed using a fuzzy C-means (FCM) clustering algorithm. This approach revealed three distinct clusters of proteins based on their expression profiles over time under chelerythrine exposure (Table S2). Cluster 1 consisted of 132 proteins with relatively stable expression levels, regardless of chelerythrine exposure (Figure 3A). Cluster 2 comprised 907 proteins exhibiting oscillatory expression changes over time in both treated and control samples, characterized by downregulation at 30 min, upregulation at 60 min, and a return to downregulation at 120 min (Figure 3B). Cluster 3, consisting of 36 proteins, displayed unique dynamics under chelerythrine treatment, with a pronounced and persistent downregulation beginning at 30 min and continuing through 120 min (Figure 3C). Notably, these proteins showed minimal changes in control samples, suggesting a specific response to chelerythrine.

Figure 2.

Differentially expressed proteins in MRSA at 30, 60, and 120 min post-chelerythrine exposure. Volcano plots showing the distribution of protein expression changes compared to untreated controls. (A) Protein expression at 30 min post-exposure. (B) Protein expression at 60 min post-exposure. (C) Protein expression at 120 min post-exposure. The x-axis represents the log2 fold change, and the y-axis represents the -log10 p-value. Proteins are categorized as upregulated (red), downregulated (green), or not significantly different (gray). Data are derived from three independent biological replicates.

Figure 3.

Temporal dynamics of protein expression under chelerythrine exposure analyzed by fuzzy C-means (FCM) clustering. The expression profiles of proteins in both control and chelerythrine-treated MRSA samples were clustered into three distinct regulatory patterns, indicating different expression dynamics over time. Colors represent membership in each of the clusters. Data are derived from three independent biological replicates.

Functional Enrichment Analysis of Chelerythrine-Specific Responsive Proteins

Gene Ontology (GO) analysis of the 36 proteins identified as specific responders to chelerythrine, exhibiting marked and sustained downregulation during prolonged treatment, revealed significant enrichment in several biological processes, cellular components, and molecular functions (Figure 4). In terms of biological processes, the downregulated proteins were significantly enriched in pathways related to energy metabolism, including the glycolytic process, the tricarboxylic acid (TCA) cycle, and arginine catabolism, specifically its conversion to glutamate and to proline via ornithine. For cellular components, these proteins were primarily localized to the cytosol, highlighting their roles in cytoplasmic metabolic activities. Molecular function analysis revealed enrichment in categories such as identical protein binding and magnesium ion binding, along with specific enzymatic activities, including fructose-bisphosphate aldolase activity, ornithine aminotransferase activity, succinate-CoA ligase activities (GDP- and ADP-forming), and NAD binding. These findings indicate that chelerythrine impacts key metabolic pathways and enzymatic functions, particularly those critical to energy metabolism and amino acid processing, providing insights into its bactericidal mechanism.

Figure 4.

Go analysis on the chelerythrine-specific responsive proteins. GO analysis was performed on DAVID's online website (https://david.ncifcrf.gov) and enrichment scores were calculated automatically. p < 0.05 was considered statistically significant.

KEGG Pathway Enrichment Analysis of Chelerythrine-Specific Responsive Proteins

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed on the 36 proteins identified as specific responders to chelerythrine, characterized by marked and sustained downregulation during prolonged exposure. The analysis revealed significant enrichment in metabolic pathways central to bacterial energy production and biosynthesis (Figure 5). Specifically, the proteins were enriched in pathways related to carbon metabolism, glycolysis/gluconeogenesis, and the tricarboxylic cycle (TCA cycle), highlighting disruptions in energy generation and metabolic flux. Additionally, pathways involved in the biosynthesis of amino acids, the pentose phosphate pathway, and arginine and proline metabolism were significantly enriched, indicating an impact on nitrogen metabolism and biosynthetic processes. Other enriched categories included microbial metabolism in diverse environments, biosynthesis of secondary metabolites, methane metabolism, and 2-oxocarboxylic acid metabolism. These results suggest that chelerythrine affects multiple interconnected pathways essential for bacterial survival, metabolism, and adaptability.

Figure 5.

KEGG pathway analysis on the chelerythrine-specific responsive proteins. KEGG analysis was performed on DAVID's online website (https://david.ncifcrf.gov) and enrichment scores were calculated automatically. p < 0.05 was considered statistically significant.

Discussion

By integrating MIC data with proteomic findings, this study provides mechanistic insights into how chelerythrine inhibits MRSA growth. The strong correlation between MIC values and metabolic disruption suggests that chelerythrine's antimicrobial action is primarily driven by interference with bacterial energy homeostasis, rather than direct membrane disruption or DNA damage. The MIC value of 19.54 mg/L for chelerythrine against MRSA was determined through serial dilution assays. This MIC value is comparable to or lower than those reported for other alkaloid-based compounds, such as berberine and palmatine, highlighting its potent antimicrobial efficacy.¹⁸ Notably, compared to traditional antibiotics such as tetracycline (MIC ∼32 mg/L against MRSA) and erythromycin (MIC ∼128 mg/L against MRSA),^19,20 chelerythrine exhibits a relatively strong inhibitory effect, supporting its potential as an antimicrobial agent. Unlike conventional antibiotics that primarily target bacterial cell walls or protein synthesis, chelerythrine appears to exert its bactericidal effect through profound metabolic disruption.

The proteomic analysis reveals that at MIC levels, chelerythrine significantly downregulates enzymes involved in glycolysis, the tricarboxylic acid (TCA) cycle, and arginine catabolism. These pathways are critical for bacterial energy production and biosynthesis, suggesting that chelerythrine-induced metabolic collapse may underlie its inhibitory effects at MIC levels. This aligns with previous studies that demonstrated metabolic disruption as a primary mode of action for natural antimicrobial compounds, such as morusin and luteolin, which similarly downregulate enzymes in energy-generating pathways.^21-23 Notably, the persistent downregulation of fructose-bisphosphate aldolase and succinate-CoA ligase under chelerythrine treatment suggests a potential depletion of ATP and NAD(P)H, leading to energy crisis and reduced bacterial survival.

The significant enrichment of arginine and proline metabolism pathways in both GO and KEGG analyses indicates an additional layer of metabolic disruption. The enzymes involved in arginine catabolism, particularly ornithine aminotransferase, play crucial roles in nitrogen metabolism and biosynthesis. Inhibition of these pathways may limit the availability of amino acid precursors for protein synthesis, compounding the bactericidal effect of chelerythrine. These findings are consistent with reports on amino acid metabolism as a critical vulnerability in bacterial stress responses, as demonstrated by the inhibition of arginine biosynthesis in MRSA by nitric oxide-releasing agents.²⁴ Furthermore, recent studies have highlighted that disrupting biofilm formation and quorum sensing can significantly enhance the antimicrobial effects of natural compounds, including chelerythrine, by interfering with the coordinated bacterial communication and surface adherence mechanisms critical for MRSA virulence.^25,26

Interestingly, the oscillatory expression patterns observed in Cluster 2 proteins, which exhibit transient upregulation, could reflect an adaptive response by MRSA to chelerythrine exposure. Such temporal dynamics are characteristic of bacterial stress responses, as seen with sub-MIC levels of antibiotics, where transient upregulation of metabolic and repair pathways temporarily offsets the inhibitory effects of antimicrobial agents.²⁷ However, the inability of MRSA to sustain these compensatory mechanisms, as evidenced by the sustained downregulation in Cluster 3, underscores the potency of chelerythrine in overwhelming bacterial resilience.

The localization of the downregulated proteins to the cytosol, as indicated by the GO analysis, highlights the critical role of metabolic pathways in the antimicrobial mechanism of chelerythrine. Disruption of cytosolic enzymes may impair metabolic flux, cellular homeostasis, and signal transduction, ultimately contributing to the bactericidal activity of this compound. This finding aligns with previous studies on antibiotics such as Daptomycin and Linezolid, which similarly target cytosolic metabolic enzymes in MRSA.^28,29

While chelerythrine itself may have limited therapeutic potential due to its toxicity, our findings on its mechanism of action could guide the development of derivatives or combination therapies with improved efficacy and safety profiles. The ability of chelerythrine to disrupt interconnected metabolic networks, including glycolysis, the TCA cycle, and nitrogen metabolism, suggests its potential for use in synergistic applications. Combining this compound with agents that target membrane integrity or oxidative stress pathways may enhance bactericidal effects by compounding metabolic and structural damage. For example, prior studies have demonstrated that combinations of metabolic inhibitors and membrane-targeting antibiotics, such as Polymyxins, can achieve synergistic efficacy against multidrug-resistant pathogens.^30-33 Future research could explore the development of chelerythrine derivatives with reduced toxicity or investigate its synergistic effects with existing antibiotics to overcome resistance mechanisms in MRSA and other multidrug-resistant pathogens.

Conclusion

This study provides a detailed investigation into the antimicrobial mechanism of chelerythrine against MRSA, revealing its ability to disrupt bacterial metabolism through targeted inhibition of energy production and biosynthetic pathways. By significantly affecting glycolysis, the TCA cycle, and nitrogen metabolism, chelerythrine induces an energy crisis that impairs bacterial growth and survival. These findings underscore the compound's potential as a novel therapeutic agent against multidrug-resistant pathogens. Furthermore, the study's proteomic analysis offers valuable insights into the molecular targets of chelerythrine, paving the way for the development of more effective derivatives or combination therapies.

Limitations

Future research should explore the synergistic potential of chelerythrine with other antibiotics and assess its efficacy in vivo to further validate its therapeutic application.

Supplemental Material

sj-tif-1-npx-10.1177_1934578X251330198 - Supplemental material for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus

Supplemental material, sj-tif-1-npx-10.1177_1934578X251330198 for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus by Jianhua Liao, Chunyan Meng, Chaodan Shao, Dongli Yu and Yuechun Wang in Natural Product Communications

Supplemental Material

sj-xlsx-2-npx-10.1177_1934578X251330198 - Supplemental material for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus

Supplemental material, sj-xlsx-2-npx-10.1177_1934578X251330198 for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus by Jianhua Liao, Chunyan Meng, Chaodan Shao, Dongli Yu and Yuechun Wang in Natural Product Communications

Supplemental Material

sj-xlsx-3-npx-10.1177_1934578X251330198 - Supplemental material for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus

Supplemental material, sj-xlsx-3-npx-10.1177_1934578X251330198 for Proteomic Insights into the Antimicrobial Mechanism of Chelerythrine Against Methicillin-Resistant Staphylococcus aureus by Jianhua Liao, Chunyan Meng, Chaodan Shao, Dongli Yu and Yuechun Wang in Natural Product Communications

Footnotes

Acknowledgment

We would like to express our gratitude to the anonymous peer reviewers for their constructive feedback and suggestions, which significantly improved the quality of this work.

Footnote

Use of AI Tools Declaration:

The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

Statement of Human and Animal Rights

This research did not involve human participants, human tissue, or any procedures requiring human subjects. Additionally, animal experiments were not conducted in this study.

Statement of Informed Consent

There are no human subjects in this article and informed consent is not applicable.

ORCID iD

Yuechun Wang

Ethical Considerations

No human or animal subjects were involved in this study.

Funding

This study was partly supported by a grant from the Zhejiang Traditional Chinese Medicine Administration (grant number 2023ZL221).

Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

All datasets generated for this study are included in the manuscript and/or the Supplementary Files. All of the MS proteomics data have been deposited to iProX () and can be accessed with the accession IPX0010506000.

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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