Integrated metabolomics and network pharmacology to reveal the different reason of propolis and gum against bacterial infection

doi:10.21203/rs.3.rs-3940340/v1

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Integrated metabolomics and network pharmacology to reveal the different reason of propolis and gum against bacterial infection

https://doi.org/10.21203/rs.3.rs-3940340/v1

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Background Propolis is a substance derived from plant resin (tree gum), and its antibacterial activity is one of its most important biological activities. However, the key differential components between propolis and tree gum and their antibacterial mechanisms remain unclear. Therefore, this study aimed to reveal the key active differential metabolites and potential antibacterial mechanisms of these differential metabolites utilizing widely targeted metabolomics and network pharmacology.

Methods Widely targeted metabolomics was employed to identify metabolites in poplar propolis and poplar gum. The antibacterial activities of poplar propolis and poplar gum against methicillin-resistant Staphylococcus aureus(MRSA) were compared using the inhibition zone diameter (DIZ), minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC), and the data were analyzed using one-way analysis of variance (ANOVA). Additionally, network pharmacology was utilized to screen key active differential metabolites and their core targets. The potential antibacterial mechanisms of these key active differential metabolites were determined through GO enrichment and KEGG pathway analysis.

Results Using widely targeted metabolomics, we identified 1646 metabolites in two poplar propolis samples and one poplar gum sample, of which 942 were common differential metabolites that were enriched in nine KEGG enrichment pathways, including flavonoid biosynthesis. In vitro antibacterial experiments also revealed that the diameter of the zone of inhibition of MRSA by poplar gum was significantly different from that of poplar propolis (p < 0.05). Furthermore, the MIC for poplar propolis was 0.15625 mg/ml, the MBC was 0.3125 mg/ml, the MIC for poplar gum was 0.3125 mg/ml, and the MBC was 0.625 mg/ml. Poplar propolis exhibited stronger antibacterial activity than poplar gum. Subsequently, network pharmacology analysis identified 72 key active differential metabolites and 23 core targets associated with the treatment of bacterial infection. Finally, GO enrichment and KEGG pathway analyses revealed that the core targets were predominantly enriched in signaling pathways related to immune regulation and the inflammatory response.

Conclusions In summary, although propolis is derived from tree gum, its metabolites are significantly different from those of tree propolis. In addition, the key active differential metabolites of propolis and poplar propolis determined the differences in their antibacterial efficacy, and the potential antibacterial mechanisms of poplar propolis and poplar gum might include immune regulation and the inflammatory response. Moreover, the screening of key active differential metabolites might lead to the identification of reliable biomarkers for poplar propolis and poplar gum. This work provides valuable insights for future quality control of poplar propolis and treatment of associated bacterial infections.

Poplar propolis

Poplar gum

key active differential metabolites

Widely targeted metabolomics

Network pharmacology

bacterial infections

Antibacterial activity

Propolis is a resinous material that bees (Apis mellifera L.) use to gather from the buds and bark of appropriate plants and deposit in their hives. [1]. Propolis has a wide range of therapeutic and health-promoting activities, such as anti-pathogenic microorganism, antioxidant, anti-inflammatory, antitumor, immunomodulatory, hepatoprotective, and antidiabetic activities. It has been used in food, drinking, and folk medicine to prevent diseases and promote human health since ancient times [2].

The antibacterial activity action of propolis is one of its most important biological activities. Bacterial infection is a disease that threatens human health and even life. In clinical treatment, bacterial infection is often treated with various antibiotic drugs. Notably, with the widespread use of antibiotics, the issue of bacterial resistance has become increasingly prominent. Additionally, antibiotic use is generally associated with certain factors, increasing the importance and urgency of treating bacterial infections [3]. Therefore, the search for natural drugs with antibacterial activity has garnered increasing attention globally. Numerous studies have been conducted to demonstrate that propolis is a natural antibacterial product. However, propolis is a complex substance, and the chemical components of propolis are highly complex and variable depending on the plant, geographical origin, bee species, collection method, and harvesting season [4]. Among these factors, the origin of plants is the most important factor in determining the chemical composition of propolis. In temperate regions, poplar, birch, elm, alder, ragweed, eucalyptus, beech, oak, horse chestnut, cypress, and pine plants are the main plant sources of propolis [4]. According to the botanical origin of propolis, more than 800 components have been identified, mainly flavonoids and their derivatives [4-6]; glycosides (flavonoids and diterpenes); benzofurans; benzopyrans; chalcones; glycerol; and esters, lignans, phenylpropanoids, steroids, terpenes, terpenoids, alcohols, acids, and their derivatives. Among them, flavonoids and phenolic acids are crucial components of propolis and are responsible for its antibacterial activity. Nevertheless, the chemical composition of propolis and its plant sources (tree gum) are poorly understood, and the underlying mechanisms of its antibacterial activity are not yet fully understood. Therefore, a more comprehensive comparative analysis of the metabolites of propolis and tree gum is essential for obtaining a deeper understanding of the compositional differences and differences in antibacterial activity between propolis and tree gum.

In recent years, the development of various omics techniques, especially widely targeted metabolomics and network pharmacology, has provided methods for revealing additional metabolites in organisms. Metabolomics is defined as the qualitative and quantitative analysis of metabolites contained in samples to be tested [7-9], as a whole, under specific conditions [10]. Among metabolomic assay techniques, traditional targeted and untargeted metabolomics have progressed to widely targeted metabolomics [11, 12]. Widely targeted metabolomics refers to an approach that employs a platform that combines liquid chromatography and tandem mass spectrometry (LC‒MS/MS) to identify compounds; this approach is capable of quantitatively and qualitatively detecting more than 1000 known compounds and has a strong advantage in the detection of organic acids, amino acids, alkaloids, phenols and flavonoids in plants [13, 14]. Propolis comes from the metabolites of plants, so this technique may be perfectly suited for comprehensively determining the chemical composition of propolis and tree gum. Network pharmacology is an emerging research field that integrates the principles of network science and pharmacology. The aim of this field is to comprehend and predict the mechanisms of drug action, the multitarget effects of drugs, and the complex relationships between drugs and diseases by constructing and analyzing biological networks. Therefore, network pharmacology can be used to elucidate the distinct mechanisms of action of poplar propolis and poplar gum against bacterial infection.

In this study, we used a combination of widely targeted metabolomics and network pharmacology to reveal the key active differential metabolites of poplar propolis and poplar gum and their potential antibacterial mechanisms. These findings provide a theoretical basis for the study of propolis and tree gum.

Materials

Two poplar propolis samples, Xintai propolis (35.90878°N, 117.76800°E) (XP1) and Yanggu propolis (36.11447°N, 115.79131°E) (YP2), were collected from different apiaries of Shandong Province in North China during the autumn of 2022, and their major plant sources were poplar plants (Fig. 1 A). Shandong poplar gum (SG1) was obtained from the poplar buds. Formic acid, acetonitrile, and methanol were of chromatographic grade and obtained from Merck. Methicillin-resistant Staphylococcus aureus (MRSA) (ATCC 43300) was purchased from the Guangdong Microbial Strain Collection Center. Agar powder and resazurin were purchased from Sigma‒Aldrich, USA. Tryptone soy broth medium (TSB) was purchased from Qingdao Haibo Biotechnology Co., Ltd.

Preparation of the samples

All the samples were dried in a vacuum freezer dryer and then ground to powder using a grinder at 30 Hz for 1.5 min. Then, 100 mg of powder was dissolved in 1.2 mL of 70% methanol extract, vortexed every 30 min for 30 seconds for a total of 6 times, and stored in the refrigerator at 4°C overnight. All the extracted samples were centrifuged at 12000 rpm at 4°C for 10 min to obtain the supernatant, which was subsequently filtered through a microporous membrane (0.22 μm) before ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) analysis.

Chromatography mass spectrometry analysis

The detection conditions were consistent with those used in a previous study [15]. The instrument system for collecting data primarily consisted of ultra-performance liquid chromatography (UPLC, SHIMADZU Nexera X2, https://sciex.com.cn/) and tandem mass spectrometry. (MS/MS, Applied Biosystems 4500 QTRAP, https://sciex.com.cn/).

All the samples were injected into a UPLC-EIS-MS system. The UPLC column used was an Agilent SB-C18 (2.1 mm × 100 mm, 1.8 μm). The mobile phase was composed of ultrapure water containing 0.1% formic acid (referred to as A) and acetonitrile containing 0.1% formic acid (referred to as B). The gradient elution program utilized in this study was as follows: At 0.00 min, the mobile phase contained 5% B, which was linearly increased to 95% B within 9.00 min and maintained at 95% B for 1 min. Subsequently, the mobile phase decreased to 5% B within the time frame of 10.00-11.10 min, followed by equilibration with 5% B until 14 min was reached. The flow rate was 0.35 mL/min, while the column temperature was maintained at 40°C. Additionally, the injection volume used was 4 μL.

The experimental setup for mass spectrometry was as follows: the electrospray ionization (ESI) temperature was set to 500°C; the ion spray voltage (IS) was 5500 V, while the ion spray voltage was -4500 V; and the gas I (GSI), gas II (GSII), and curtain gas (CUR) pressures were adjusted to 50, 60, and 25 psi, respectively. The collision-activated dissociation (CAD) method was set to high mode. To perform instrument tuning and mass calibration, a triple quadrupole (QQQ) mode was used with a polypropylene glycol solution of 10 μmol/L, while the LIT mode used a polypropylene glycol solution of 100 μmol/L. QQQ scans were conducted in multiple reaction monitoring (MRM) mode, and the collision gas (nitrogen) was set to medium. The declustering potential (DP) and collision energy (CE) for each MRM ion pair were optimized. In each period, a specific set of MRM ion pairs was monitored based on the eluate metabolites.

Qualitative and quantitative analysis of metabolites

Qualitative analysis was conducted by utilizing secondary spectrum data, whereas the quantification of metabolites was achieved using triple quadrupole mass spectrometry in multiple reaction monitoring (MRM) mode. To ensure the specificity of the target substance ions, the precursor ions (also known as parent ions) were meticulously screened by the quadrupole, excluding any interference from ions belonging to other substances. These precursor ions were then fragmented into subions within the collision chamber and further filtered through QQQ, enabling the selection of the characteristic target ion. This thorough process eliminates any potential interference from nontarget ions, resulting in a more accurate and reproducible quantification. Following the acquisition of metabolite spectra from different samples, the peak areas of the mass spectra for all substances were integrated and normalized based on the mass spectra of the same metabolite in various samples.

Data analysis of metabolites

Three replicates were performed for each experiment. To process the MS data, Analyst 1.6.3 software was utilized. To analyze the identified metabolites in the samples comprehensively, both three-dimensional principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted. To identify differential metabolites between poplar propolis and poplar gum, supervised multiple regression orthogonal partial least-squares discriminant analysis (OPLS-DA) was carried out. The criteria for screening differential metabolites included a variable importance in projection (VIP) value ≥ 1, fold change ≥ 2 (upregulated) or ≤ 0.5 (downregulated), and P ≤ 0.05. R software (https://www.r-project.org) was used for cluster analysis to examine the accumulation patterns of metabolites across various samples. To obtain detailed pathway information, the differential metabolites between poplar propolis and poplar gum were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Determination of the antibacterial activity of poplar propolis and poplar gum

The method for determining the inhibition zone diameter (DIZ) was based on the filter paper diffusion method. In brief, 100 μL of a suspension of MRSA in the exponential growth phase (107 CFU/mL) was spread on a TSB agar plate. We treated sterile filter paper discs (6 mm) with 10 μL of XP1, YP2, or SG1 at a concentration of 100 mg/kg. The filter paper discs were subsequently placed on the surface of the TSB agar plate and cultured at 37°C for 24 hours. Subsequently, we used the resazurin microdilution method to determine the minimum inhibitory concentrations (MICs) of XP1, YP2, and SG1 against MRSA, which ranged from 0.039 to 1.25 mg/ml. The MIC was defined as the lowest concentration preventing the solution from changing from blue to pink. The Tryptic Soy Broth Agar dilution method was used to determine the minimum bactericidal concentrations (MBCs) of XP1, YP2, and SG1 against MRSA, defined as the lowest concentration on the agar plate where no bacterial colonies were observed after 24 hours of incubation. The data were analyzed using one-way analysis of variance (ANOVA), and independent experiments were repeated three times.

Identification of key active metabolites and their targets in poplar propolis and poplar gum

According to the criteria of oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.14, key differentially active metabolites in poplar propolis and poplar gum were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://old.tcmsp-e.com/tcmsp.php). Similarly, drugs satisfying the criteria “moderate” or “good” were selected as the key active differential metabolites according to the Encyclopedia of Traditional Chinese Medicine (ETCM) (http://www.tcmip.cn/ETCM/) database. Subsequently, we recorded the targets related to the key active differential metabolites through these two databases, and the names of the relevant targets were standardized using the UniProt database (https://www.uniprot.org/).

Screening of core targets

Searching for keywords related to "bacterial infection" in OMIM (https://omim.org/), GeneCards (https://www.genecards.org/), and DisGeNET (https://www.disgenet.org/), we identified and removed duplicate targets. Next, a Venn plot was constructed for common targets of disease and key active differential metabolites. The common targets were subsequently imported into the STRING database (https://cn.string-db.org/) to construct a protein–protein interaction (PPI) network. The results obtained from the STRING database were imported into Cytoscape 3.10.0 software, and core targets were established based on degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) values.

GO enrichment and KEGG pathway analysis

We imported the core targets into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. In the GO analysis, we visualized the top 10 pathways with p < 0.05 in the biological process (BP), cellular component (CC), and molecular function (MF) categories. Similarly, the top 20 pathways with p < 0.05 were included in the KEGG pathway analysis.

Data quality assessment

Quality control (QC) samples provide quality assurance for the data and are generated by mixing a certain amount of individual sample before the overall metabolite is assayed. Characteristic ions of metabolites were screened using QQQ, while the mass spectrometry data were processed using Analyst 1.6.3 and MultiQuant software. The MRM mode is used for the qualitative and quantitative analysis of metabolites in samples and has many advantages over previous mass spectrometry assays, such as high sensitivity, high specificity, good reproducibility and high-throughput automation [16]. As shown in Supplementary Fig. S1, the MRM multipeak plot illustrates the metabolites detected in the sample, in which each peak of a distinct color represents a detected metabolite. Supplementary Fig. S2 displays the complete correction diagrams for quantifying the metabolites, where the peak area indicates the relative abundance of the metabolite within the sample. As shown in Supplementary Fig. S3, the total ion flow curves for the metabolites detected via mass spectrometry of the QC samples overlapped well, with consistent retention times and peak intensities, indicating that the instrument had good signal stability when detecting the same sample at different times.

Widely targeted metabolomics analysis of poplar propolis and poplar gum

On the basis of the mass-to-charge ratios available in the MetWare database, a comprehensive analysis revealed that a total of 1646 metabolites were successfully identified in both poplar propolis and poplar gum, including 402 flavonoids (24.42%), 223 amino acids and derivatives (13.55%), 220 phenolic acids (13.37%), 172 lipids (10.45%), 115 organic acids (6.99%), 102 alkaloids (6.20%), 92 nucleotides and their derivatives (5.59%), 55 lignans and coumarins (3.34%), 48 terpenoids (2.92%), 34 quinones (2.07%), 9 tannins (0.55%), and 174 other compounds (10.57%) (Fig. 1B). A heatmap (Fig. 1C) was generated to visualize the overall metabolite profile. The clustering of the three replicates for each sample in the heatmap indicated that there was a high level of homogeneity and reliability among the replicates. Notably, there were noticeable disparities in metabolites between poplar propolis and poplar gum, as did variations in metabolites between the two types of poplar propolis.

Among the metabolites identified, 1634 metabolites were found in XP1, 1641 metabolites were found in YP2, and 1539 metabolites were found in SG1, suggesting that the two poplar propolis samples had more metabolites than did the poplar gum sample. In terms of chemical composition, the identified metabolites were the primary metabolic compounds found in plants and included sugars, amino acids, lipids, and various secondary metabolites, such as flavonoids, phenolic acids, terpenoids, alkaloids, quinones, tannins, and lignans. The number of identified metabolites exceeded the findings reported previously [4, 17], and flavonoids, amino acids and derivatives, phenolic acids, lipids, organic acids, and alkaloids were the main metabolites, accounting for 74.98% of all the metabolites identified. Notably, the number of flavonoids was the highest. By 2014, approximately 140 flavonoids had been reported in propolis [4], whereas 398 flavonoids were found in poplar propolis, and the results showed that flavonoids were the predominant substance in poplar propolis. In addition, only 48 terpenoids were identified in the present study, and a previous study reported nearly 160 terpenes using GC‒MS [17]. In addition, alkaloids (6.20%) and tannins (0.55%) were also identified; these compounds have rarely been mentioned in previous poplar propolis studies. The results from these investigations demonstrated that the analysis of poplar propolis and poplar gum using UHPLC-QqQ-MS is an exceptionally potent and efficient technique for the detection and characterization of numerous nonvolatile chemical constituents.

Multivariate analysis of identified metabolites in poplar propolis and poplar gum

PCA and HCA were further performed on the overall metabolite differences between the two poplar propolis samples and one poplar gum sample. Both three-dimensional PCA and HCA are multivariate statistical analysis methods. Three-dimensional PCA is designed to maximize the preservation of the originality of the data and simplify and downscale complex data. HCA allows for the highest possible homogeneity of substances in the same category and the highest possible heterogeneity between different categories. In the three-dimensional PCA, three principal components, PC1, PC2 and PC3, were extracted based on the initial eigenvalues of two poplar propolis products and one poplar gum metabolite. For each principal component, there was a clear separation between the poplar propolis and poplar gum samples. However, there was some separation between XP1 and YP2 only on PC1 (Fig. 2A). These results were consistent with those obtained via three-dimensional HCA (Fig. 2B). The differences in the metabolites of these two poplar propolis samples further demonstrated that the chemical composition of propolis varies depending on the geographical location and plant [18]. In general, considerable discrepancies in metabolites between poplar propolis and poplar gum were shown by both PCA and HCA. Furthermore, variations in metabolites were also observed between distinct types of poplar propolis.

Analysis of differential metabolites in poplar propolis and poplar gum

We utilized OPLS-DA models to compare the distinct metabolites present in poplar propolis and poplar gum. Additionally, we assessed the variation in metabolites between two different poplar propolis species. The OPLS-DA models were evaluated based on the prediction parameters R²X, R²Y, and Q². The model's stability and reliability can be determined by the proximity of these three parameters to 1. The vertical coordinate represents the R²Y and Q² values, and the horizontal coordinate represents the number of occurrences of the model's classification effect. According to this model, the R²X, R²Y, and Q²scores were greater than 0.9, indicating that the models were appropriate (Fig. 3A and 3B). To screen for differential metabolites more accurately, we chose the FC value, VIP value, and P value as the statistical analysis methods for screening differential metabolites, and the screening criteria were FC ≥ 2 or ≤ 0.5, VIP ≥ 1 and P ≤ 0.05. A total of 1224 differential metabolites were identified between poplar propolis and poplar gum. A total of 1040 differential metabolites were identified between SG1 and XP1 (859 upregulated and 181 downregulated), and 1126 differential metabolites were identified between SG1 and YP2 (995 upregulated and 131 downregulated). These findings, depicted in Figure 3D and 3E, suggest that many of the metabolites present in poplar propolis surpass the content found in poplar gum.

A Venn diagram was constructed to show the total number of differential metabolites between poplar propolis and poplar gum (Fig. 3G). A total of 942 common differential metabolites were identified, including 183 flavonoids, 149 phenolic acids, 148 amino acids and derivatives, 75 lipids, 64 nucleotides and derivatives, 67 organic acids, 62 alkaloids, 32 lignans and coumarins, 28 terpenoids, 14 quinones, 4 tannins, and 116 others (Table S1). Notably, 86 common differential metabolites were found only in two poplar propolis samples and not in poplar gum (Table S2). Similarly, only four metabolites, ethyl cinnamate, 9,10-DHOME, (12Z)-9,10-dihydroxyoctadec-12-enoic acid, 3-thiotetradecanoic acid and pinoresinol-4-O-glucoside, were present in poplar gum but not in poplar propolis.

We further identified the differential metabolites between XP1 and YP2. A total of 234 differential metabolites were detected (212 upregulated and 22 downregulated) (Fig. 3F). Figure 3C shows that the constructed OPLS-DA model was appropriate. These differential metabolites included 71 flavonoids, 43 amino acids and derivatives, 41 phenolic acids, 15 alkaloids, 14 organic acids, 10 lipids, 8 lignans and coumarins, 5 quinones, 5 nucleotides and derivatives, 3 tannins, 2 terpenoids and 17 others. The findings indicated that there was a significantly greater number of common metabolites between poplar propolis and poplar gum than between these two poplar propolis samples. Therefore, we next focused on the differential metabolites between poplar propolis and poplar gum.

K-means analysis was performed to represent the changes in the relative content of the differential metabolites in each group more graphically. The 1224 differential metabolites were divided into eight subclasses (Fig. 4). In subclass 1, subclass 2, subclass 4 and subclass 8 (Fig. 4A, 4B, 4D and 4H), the relative content of these metabolites in the two poplar propolis samples was greater than that in poplar gum, and flavonoids composed the main group of metabolites, such as quercetin, kaempferol (3,5,7,4'-tetrahydroxyflavone), luteolin (5,7,3',4'-tetrahydroxyflavone) and chalcone. In contrast, for 161 metabolites and 22 metabolites in Subclass 5 and Subclass 6 (Fig. 4E and 4F), the relative content of these metabolites in poplar propolis was lower than that in poplar gum. Among the metabolites in Subclass 5 and Subclass 6, flavonoids, including myricetin, glabrone, and icaritin, had the greatest abundance. In Subclass 3 and Subclass 7 (Fig. 4C and 4G), the relative content of metabolites in XP1 and YP2 was greater than that in the gum group. In addition, the different metabolites between poplar propolis and poplar gum were also flavonoids, phenolic acids, amino acids and derivatives, lipids, organic acids, and alkaloids.

KEGG annotation and enrichment analysis of differential metabolites

Understanding the metabolic profile of these distinct metabolites is essential for comprehending the biotransformation process from poplar gum to poplar propolis. To achieve this, we performed annotation and enrichment analysis of the distinct metabolites using the KEGG database [19]. A total of 414 differential metabolites were identified via the KEGG database, including 268 differential metabolites between SG1 and XP1 and 296 differential metabolites between SG1 and YP2; these metabolites were enriched and mapped to different pathways (Figs. S4 and S5). Furthermore, 97 (SG1 vs. XP1) and 96 (SG1 vs. YP2) pathways were mapped.

The number of differential metabolites annotated and enriched via metabolic pathway (204 and 231, respectively), biosynthesis of secondary metabolites (107 and 121, respectively), flavonoid biosynthesis (18 and 21, respectively), flavone and flavonol biosynthesis (11 and 17, respectively), biosynthesis of amino acids (29 and 34, respectively), phenylpropanoid biosynthesis (18 and 19, respectively), and biosynthesis of cofactors (33 and 36, respectively) indicated that these pathways were some of the main metabolic pathways for the differential metabolites.

KEGG pathway enrichment analysis was conducted using the characteristics of the differential metabolites, which are presented as differential abundance (DA) score plots (Figure 5A and 5B). The DA score reflects the upregulated and downregulated differential metabolites in the pathway (upregulated differential metabolites on the right side of the center axis and downregulated differential metabolites on the left side). The length of the line segment indicates the absolute value of the DA score, and the dots indicate the number of differential metabolites. The top 20 pathways according to P values are listed (P<0.05). In SG1 vs. XP1, nicotinate and nicotinamide metabolism were the most significantly enriched (P=0.001), and the other significantly enriched pathways included pentose and glucuronate interconversions (P=0.01) and amino sugar and nucleotide sugar metabolism (P=0.06). The metabolic pathway (P=0.10) exhibited the greatest abundance of metabolites. In the comparison between SG1 and YP2, metabolic pathway (P=0.002) demonstrated the highest level of significance, with this specific group exhibiting the greatest abundance of enriched metabolites. The other pathways included arginine biosynthesis (P=0.03) and nicotinate and nicotinamide metabolism (P=0.04).

Based on the number of differential metabolites annotated to metabolic pathways and the P value and combined with the results analyzed previously, the differences between poplar propolis and poplar gum might be the result of nine metabolic pathways, including the biosynthesis of flavonoids; flavones and flavonols; amino acids; nicotinate and nicotinamide; pentose and glucuronate interconversions; ascorbate and aldarate; phenylpropanoids; cofactors; and C5-branched dibasic acid. Poplar propolis are natural substances that are processed and stored in hives by honeybees. During the process of accessing poplar propolis, honeybees mix their secretions from the salivary and wax glands to contribute to metabolite transformation from poplar gum to poplar propolis. In addition, the microbial community in the hives and the internal and external environments might also affect metabolite transformation. The reasons for the metabolite transformation from poplar gum to poplar propolis must be further studied.

The antibacterial activity of propolis can be traced back to ancient Egypt [20]. With the widespread use of antibiotics, a series of problems have arisen. For example, excessive use of antibiotics may lead to bacterial resistance and adverse reactions, such as nausea, vomiting, and allergies. The action of antibiotics is generally direct at the site of bacterial infection, and prolonged use may inevitably result in some side effects [21]. Notably, antibiotics may also disrupt normal microbial communities, especially those of the intestinal microbiota. Therefore, to identify antibacterial substances as alternatives to antibiotics, in this study, we selected two poplar propolis samples and one poplar gum sample to assess their antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). We compared the antibacterial capabilities of XP1, YP2, and SG1 using the methods of DIZ, MIC and MBC. As shown in Table 1, the DIZ of XP1 and YP2 significantly differed from that of SG1 (P<0.05), with MIC values of 0.15625 mg/ml for XP1 and YP2, while poplar gum had an MIC of 0.3125 mg/ml. Similarly, the MBC for XP1 and YP2 was 0.3125 mg/ml, whereas that for SG1 was 0.625 mg/ml. Therefore, the antibacterial activity of poplar propolis is stronger than that of poplar gum.

Table 1 XP1, YP2 and SG1 results for the DIZ, MIC and MBC of MRSA.

No.	DIZ (mm)	MIC（mg/ml）	MBC（mg/ml）
XP1	13.68±0.39^a	0.15625	0.3125
YP2	13.47±1.30^a	0.15625	0.3125
SG1	8.42±0.52^b	0.3125	0.625
CTRL	6.00^c	0	0

Identification of key active differential metabolites of poplar propolis and poplar gum

We identified a total of 72 key active differential metabolites in the TCMSP and ETCM databases that met the screening criteria. These metabolites included 20 flavonoids, 19 phenolic acids, 5 alkaloids, 5 organic acids, 5 lignans and coumarins, 4 quinones, 4 amino acids and derivatives, 3 terpenoids, 2 lipids, and 5 others (Table S3). Among them, 66 differential metabolites corresponded to 559 targets. Flavonoids are known for their antioxidant, antitumor, antiaging, anti-inflammatory, immune-regulating, hypoglycemic, hypolipidemic, and tissue-regenerating properties [22]. Additionally, flavonoids were identified as the key active components in propolis, determining the differences in biological activity between poplar propolis and poplar gum [23]. For instance, luteolin (5,7,3',4'-tetrahydroxyflavone) has been shown to exhibit cytotoxic effects on both colon cancer cells (HCT116 cells) and triple-negative breast cancer cells (MDA-MB-231 cells), inhibiting their growth through apoptosis[24]; however, propolis has rarely been reported to play an important role in antitumor, anti-inflammatory, and antioxidant activities. Medicarpin has been found in Nepalese propolis, and although it did not have any effect on the cytokines IL-6, TNF-alpha, or IL-33, its role could not be ignored. Phenolic acids are also important components of propolis. For example, 4-methoxycinnamic acid, which was the metabolite identified in this study, was found in Brazilian propolis. This Brazilian propolis exhibited notable cytotoxic effects on cancer cells and significantly inhibited the growth of C. neoformans, methicillin-resistant Staphylococcus aureus, and P. aeruginosa [25]. Notably, vanillin and 4-hydroxy-3-methoxybenzaldehyde were found in both our current study and in previous Brazilian red propolis. Phenolic compounds are important components for the antioxidant activity of Brazilian red propolis [26]. p-Coumaricacid is also an important phenolic compound that may affect the immune response by stimulating the production of regulatory effectors, such as IL-10, in vivo [27]. The other key active differential metabolites identified in this study, Sesamin, Isofraxidin, pinoresinol, dalbergin, and Dehydrodiconiferyl alcohol, are lignans and coumarins [28]. These natural compounds are widely distributed in plants and hold significant research prospects, such as anticancer, antibacterial, and antiviral activities. Alkaloids, including N-feruloyltyramine; moupinamide, histamine, hyoscyamine, cadaverine, and aurantiamideacetate, constitute the main active ingredients in medicinal plants [29]. Additionally, terpenoids, quinones, lipids, organic acids, nucleotides and derivatives, and vitamins are also important components of propolis that exert biological activities. Several terpenoids and quinones exhibit antibacterial effects on propolis [20]. In conclusion, the isolation and identification of chemical components in propolis are highly important for its development.

Identification of core targets and construction of PPI networks

To further identify the core targets for treating bacterial infection, we screened 934 targets associated with bacterial infection through the OMIM, DisGeNET, and GeneCards databases. A Venn diagram illustrated a total of 98 common targets between the key active differential metabolites and bacterial infection (Figure 6A). To determine the distinct effects of poplar propolis and poplar gum on bacterial infection, we inputted these 98 common targets into the STRING database for protein‒protein interaction (PPI) analysis. The results of the PPI analysis were subsequently imported into Cytoscape 3.10.0 software to assess the significance of the targets. The common targets were filtered based on DC, BC, and CC. As shown in Figure 6B, the importance of the targets is displayed according to the circles from large to small and the colors from dark to light. Subsequently, 23 core targets were ultimately identified (Table 2). Notably, the interleukin family constituted the highest percentage of the core targets. Interleukins, crucial cellular immune factors, play a significant role in the inflammatory response and immunomodulation, making them essential in disease research and treatment [30]. Additionally, there were core targets associated with the Nf-κb signaling pathway, TLR signaling pathway, caspase-mediated apoptosis signaling pathway, and MAPK signaling pathway.

Table 2 The DC, BC and CC values for key active differential metabolites.

Core targets	Degree centrality (DC)	Closeness centrality (BC)	Closeness centrality (CC)
TNF	148.0	496.28595	0.80508476
ALB	148.0	571.94336	0.80508476
IL6	144.0	339.04156	0.7916667
AKT1	138.0	439.23337	0.77868855
INS	134.0	382.36197	0.766129
TP53	134.0	351.37943	0.75396824
IFNG	126.0	316.07388	0.7251908
IL10	118.0	171.54361	0.7037037
EGFR	118.0	180.82344	0.7089552
TLR4	116.0	120.42132	0.6985294
PTGS2	116.0	367.24893	0.71428573
CASP3	114.0	100.463234	0.7037037
MMP9	112.0	94.9405	0.6884058
JUN	110.0	131.19165	0.6985294
HSP90AA1	106.0	285.18048	0.6690141
IL4	102.0	78.57386	0.66433567
MAKP3	102.0	127.4424	0.66433567
ESR1	100.0	114.141685	0.66433567
IL2	98.0	140.50949	0.6597222
PPARG	98.0	88.09312	0.6551724
RELA	96.0	185.81624	0.64625853
ICAM1	94.0	31.805016	0.6418919
NFKBIA	94.0	31.366606	0.64625853

GO enrichment and KEGG pathway analysis

We imported these 23 core targets into the DAVID database to study the underlying mechanisms and signaling pathways associated with the differential metabolites of poplar propolis and poplar gum against bacterial infection. According to the p value from small to large, we selected the top 10 pathways in the BP, CC and MF categories (Figure 6C). In the BP category, the majority of the genes were enriched in the transcription of mRNA-related genes, particularly those involved in apoptosis and the regulation of NO generation. Based on the endosymbiotic theory, it was hypothesized that some bacterial DNA is highly similar to mitochondrial DNA and plays a crucial role in the apoptotic process. Studies have demonstrated that after bacterial infection, cells activate cellular autoimmunity through the mitochondrial protein ubiquitination pathway, promoting the release of proinflammatory factors [31]. Several studies have reported that propolis plays a significant role in reducing the release of proinflammatory factors, including IL-6, TNF-A, IL-1β, IL-8, and IL-17A [32]. In terms of wound repair, the use of nitric oxide-propelled nanomotors can increase the production of nitric oxide and reduce the release of inflammatory factors to treat wounds, and poplar propolis has good efficacy and has antibacterial and anti-inflammatory properties in vitro; thus, it could also be used to treat wounds [33]. In terms of CC, the core targets were involved in the cellular membrane, cytoplasm, extracellular space and nucleus. Concerning MF, the core targets were enriched mainly in the pathways associated with protein binding and chromatin DNA, which further suggested that these targets were responsible for regulating gene expression. In conclusion, analyzing BP, CC, and MF helps us to comprehensively understand the diverse effects of the differential metabolites of poplar propolis and poplar gum on bacterial infection and provides a certain theoretical basis for future research on the antibacterial efficacy of poplar propolis and poplar gum.

To gain further insight into the signaling pathways involved in the action of poplar propolis and poplar gum, we analyzed the core targets via the DAVID database for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. As shown in Figure 6D, the signaling pathways associated with the core pathogenic microorganisms were enriched in hepatitis B, Chagas disease, Yersinia infection, Salmonella infection, Salmonella infection, leishmaniasis, Kaposi sarcoma-associated herpesvirus infection, influenza A, human T-cell leukemia virus 1 infection and human cytomegalovirus infection. In addition, core targets were enriched in the PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, T-cell receptor signaling pathway, and Th17 cell differentiation pathway, suggesting that the differential metabolites of poplar propolis and poplar propolis play important roles in immune regulation and related inflammatory signaling pathways. Studies have reported that Brazilian propolis can aid patients with leishmaniasis by reducing the levels of inflammatory factors [34]. Bulgarian propolis has a killing effect on Salmonella, and combination with antibiotics might provide a new therapeutic strategy for treating Salmonella infections [35]. Although there are some differences in the chemical composition of propolis from different origins, the antibacterial activity of propolis is highly significant. In summary, propolis enhances antibacterial activity mainly by inhibiting relevant inflammatory signaling pathways and modulating the autoimmune system.

The metabolites in poplar propolis and poplar gum were first comprehensively analyzed by using UHPLC-QqQ-MS based on widely targeted metabolomic analysis. A total of 1646 metabolites were identified in poplar propolis and poplar gum, including 402 flavonoids, 223 amino acids and derivatives, 220 phenolic compounds, 172 lipids, 115 organic acids, 102 alkaloids, 92 nucleotides and derivatives, 55 lignans and coumarins, 48 terpenes, 34 quinones, 9 tannins and 174 others. We screened a total of 1224 differential metabolites between poplar propolis and poplar gum, of which 942 were common differential metabolites. KEGG pathway analysis revealed that these differential metabolites were enriched in nine metabolic pathways, including flavonoid biosynthesis. These results indicated that the metabolites of poplar propolis derived from poplar gum are significantly different from those derived from poplar gum. Next, we assessed the antibacterial activity of poplar propolis and poplar gum and found that poplar propolis was more potent against MRSA than was poplar gum. Subsequently, network pharmacology analysis revealed 72 key active differential metabolites, including 20 flavonoids, 19 phenolic acids, 5 alkaloids, 5 organic acids, 5 lignans and coumarins, 4 quinones, 4 amino acids and derivatives, 3 terpenoids, 2 lipids, and 5 others. These key active differential metabolites might determine the differences in antibacterial efficacy between poplar propolis and poplar gum. Through PPI analysis, we identified 23 core targets associated with bacterial infection among the differential metabolites, including TNF, the interleukin family, IFNG, EGFR, TLR4, CASP3, MMP9, and MAPK. GO enrichment and KEGG pathway analyses indicated that these core targets primarily function in gene expression and regulation, pathogenic microbial infection, the PI3K-Akt signaling pathway, the TNF signaling pathway, the IL-17 signaling pathway, the T-cell receptor signaling pathway, and Th17 cell differentiation pathways. Analysis of the key active differential metabolites may also provide reliable biomarkers for the identification of poplar propolis and poplar gum.

In conclusion, this study systematically revealed that there were significant differences in the metabolites of poplar propolis and poplar gum. Through network pharmacology, we identified key active differential metabolites and their targets and signaling pathways associated with bacterial infection. These findings establish a theoretical foundation for future assessments of the functionalities of poplar propolis and poplar gum, particularly in the context of treating related bacterial infections.

XP1:Xinta propolis; YP2:Yanggu propolis; SG1: Shandong poplar gum; MRSA: methicillin-resistant Staphylococcus aureus; DIZ: inhibition zone diameter; MIC: minimum inhibitory concentration; MBC: minimum bactericidal concentration; LC‒MS/MS: liquid chromatography and tandem mass spectrometry; TSB: Tryptone soy broth medium; UPLC–MS/MS: ultrahigh-performance liquid chromatography–tandem mass spectrometry; OPLS-DA: orthogonal partial least-squares discriminant analysis; VIP: variable importance in projection; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein–protein interaction; TCMSP: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform; ETCM: Encyclopedia of Traditional Chinese Medicine; DC: Degree Centrality; CC: Closeness Centrality; BC: Betweenness Centrality; GO: Gene Ontology; BP: Biological Process; CC: Cellular Component; MF: Molecular Function; QC: Quality Control; PCA: three-dimensional principal component analysis; HCA: hierarchical cluster analysis ; MRM: multiple reaction monitoring

Ethics approval and consent to participate

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Consent for publication

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Authors’ contributions

ZL: Methodology, writing – original draft, formal analysis, and data curation. ZL, and YG: Validation, Data curation. HX: Conceptualization, Methodology, Resources, Project administration, Funding acquisition, Writing – review & editing.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Funding

This work was supported by the grant from Shandong Province Modern Agricultural Technology System (SDAIT-24-05), Shandong Provincial Natural Science Foundation of China (ZR2021MC110) and Taishan scholars (NO. tsqn202211172).

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No competing interests reported.

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Integrated metabolomics and network pharmacology to reveal the different reason of propolis and gum against bacterial infection

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Abstract

Figures

Background

Materials and methods

Materials

Preparation of the samples

Chromatography mass spectrometry analysis

Qualitative and quantitative analysis of metabolites

Data analysis of metabolites

Determination of the antibacterial activity of poplar propolis and poplar gum

Identification of key active metabolites and their targets in poplar propolis and poplar gum

Screening of core targets

GO enrichment and KEGG pathway analysis

Results and discussion

Data quality assessment

Widely targeted metabolomics analysis of poplar propolis and poplar gum

Multivariate analysis of identified metabolites in poplar propolis and poplar gum

Analysis of differential metabolites in poplar propolis and poplar gum

KEGG annotation and enrichment analysis of differential metabolites

Identification of key active differential metabolites of poplar propolis and poplar gum

Identification of core targets and construction of PPI networks

GO enrichment and KEGG pathway analysis

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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