Abstract Royal Jelly (RJ) is a natural substance produced by honeybees, serving not only as nutrition for bee brood and queens but also as a functional food due to its health-promoting properties. Despite its well-known broad-spectrum antibacterial activity, the precise molecular mechanism underlying its antibacterial action has remained elusive. In this study, we investigated the impact of RJ on the bacteria model MG1655 at its half-maximal inhibitory concentration, employing LC–MS/MS to analyze proteomic changes. The differentially expressed proteins were found to primarily contribute to the suppression of gene expression processes, specifically transcription and translation, disrupting nutrition and energy metabolism, and inducing oxidative stress. Notably, RJ treatment led to a marked inhibition of superoxide dismutase and catalase activities, resulting in heightened oxidative damage and lipid peroxidation. Furthermore, through a protein–protein interaction network analysis using the STRING database, we identified CRP and IHF as crucial host regulators responsive to RJ. These regulators were found to play a pivotal role in suppressing essential hub genes associated with energy production and antioxidant capabilities. Our findings significantly contribute to the understanding of RJ's antibacterial mechanism, highlighting its potential as a natural alternative to conventional antibiotics. The identification of CRP and IHF as central players highlights the intricate regulatory networks involved in RJ's action, offering new targets for developing innovative antimicrobial strategies. Keywords: Royal jelly, Antibacterial activity, Oxidative stress, Bacterial proteome Subject terms: Drug discovery, Microbiology, Molecular biology Introduction Royal jelly (RJ), a creamy substance mainly secreted by the hypopharyngeal glands and mandibular glands of young worker bees, is the most vital nourishment for both larval bees and queen bees. Accumulating evidence suggests that RJ is regarded as a functional food, supported by its documented safety profile and a variety of pharmacological activities such as antimicrobial, anti-inflammatory, immunomodulatory effects, and potentially extending lifespan^[34]1,[35]2. The primary constituents of RJ comprise (60–70% w/w) water, (9–18% w/w) proteins, (7–18% w/w) carbohydrates, and (3–8% w/w) lipids^[36]3. It is believed that various compounds found in RJ possess antibacterial properties. These include the major royal jelly proteins MRJP1, MRJP2 and MRJP4; as well as peptides like Jelleines and Royalisin; and hydroxylated fatty acids such as 10-HDA^[37]4. These compounds display various antibacterial activities against a range of microorganisms, including gram-positive bacteria, gram-negative bacteria, and fungi. Notable examples of these microorganisms include Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Staphylococcus aureus (S. aureus), Bacillus subtilis (B. subtilis), etc.^[38]4. In addition, optimized analogs of Jelleine-I, have shown enhanced antimicrobial activity against many multi-drug resistant (MDR) strains with minimal toxicity. These MDR strains include R P. aeruginosa, extended-spectrum β-Lactamase E. coli, and methicillin-resistant S. aureus^[39]5. This is of particular significance in light of the growing challenge in public health posed by the prevalence of antibiotic-resistant bacteria. Recent discoveries have unveiled a shared mechanism of cell death induced by all classes of antibiotics, wherein cells exhibit unfavorable responses to drug-induced stress^[40]6. When exposed to a lethal dose of drugs, cells initiate the production of detrimental hydroxyl radicals through a ubiquitous cell death pathway. This pathway involves alterations in vital cellular processes, such as iron metabolism, and the tricarboxylic acid (TCA) cycle, which governs central metabolism^[41]7,[42]8. Although evidence has shown that RJ exhibits microbial inhibition by disrupting or interfering with surface structures^[43]5,[44]9,[45]10, the precise molecular mechanisms underlying their action remain incompletely understood. In this study, we utilized the bacterial model E. coli K12 strain MG1655, a well-characterized and extensively studied model organism ideal for antibacterial research, to systematically explore the proteomic changes following exposure to RJ. Our results revealed a global inhibition of transcription and translation processes as a result of RJ treatment. Additionally, we observed perturbations in cellular carbohydrate, lipid, and protein metabolism, along with the induction of oxidative damage. To further elucidate the impact of RJ treatment, we constructed a Protein–Protein Interaction (PPI) network involving differentially expressed proteins (DEPs) using the Search Tool for the Retrieval of Interacting Genes (STRING) database. This network allowed to pinpoint hub proteins and key transcriptional factors (TFs) that respond to RJ. For the first time, the antibacterial mechanism of RJ was unveiled through a systematic analysis of the global changes in bacterial gene expression induced by RJ. This analysis highlighted the role of CRP and IHF as crucial bacterial regulators responsive to RJ. These findings enhance our understanding of RJ's antibacterial mechanism and offer valuable insights into the potential development of RJ as a source for novel natural antibiotic products. Materials and methods Bacteria strain and fresh royal jelly The genotype of the laboratory strain E. coli K12 MG1655 strain (NCBI Taxonomy ID: 511145) is as follows: F- lambda- ilvG- rfb-50 rph-1. The recipe for Luria–Bertani (LB) broth is as follows: Combine 10 g of tryptone, 5 g of yeast extract, 10 g of NaCl, and 1 L of distilled water. The freshly harvested RJ used in this study was collected from the queen cells approximately 72 h after larval grafting and then stored in a freezer at − 20 °C until use. RJ solutions were prepared using deionized (DI) water (Coolaber, Beijing, China). Bacteria growth Determination of half-maximal inhibitory concentration The overnight culture of MG1655 was diluted to 1:100 in fresh LB broth (Solarbio, Beijing, China) within a shaking incubator set at 37 °C and 200 rpm. When the optical density at 600 nm (OD[600]) reached to 0.1, the cell culture was evenly divided into six flasks, each containing varying concentrations of RJ ranging from 0 to 50 mg/mL. These flasks were reintroduced into the shaking incubator, and the OD[600] of the cultures was monitored hourly over a 12-h period. The growth curve was obtained based on three independent experiments. The half-maximal inhibitory concentration (IC50) of RJ was determined by the quantity of RJ required to inhibit the biomass by half during the stationary phase. Disc diffusion test The overnight culture of MG1655 was diluted to a 1:100 ratio in fresh LB broth in a shaking incubator (37 °C, 200 rpm). When the bacteria reached mid-exponential phase (OD[600] = 0.4), 200 µL of the cell culture was taken and spread onto an LB agar plate (Solarbio, Beijing, China). Then six sterile filter paper (Coolaber, Beijing, China) discs (6 mm in diameter) were punched onto the LB agar plate, followed by adding 5 µL of RJ solutions with the final RJ concentrations of 10 mg/mL, 20 mg/mL, 30 mg/mL, 40 mg/mL and 50 mg/mL, respectively. As a control, an equal volume of DI water was added to a sterile filter paper disc. The plate was then incubated at 37 °C overnight, and the diameters of the inhibition zones were measured. The diameters of inhibition zones were obtained based on three independent experiments. Visualization of MG1655 The overnight culture of MG1655 was diluted to 1:100 in fresh LB broth and cultivated in a shaking incubator (37 °C, 200 rpm). When the bacteria reached mid-exponential phase (OD[600] = 0.4), RJ was introduced into the cell culture at a final concentration of 20 mg/mL. In the control group, an equal volume of DI water was added. The culture was then incubated for 1 h in a shaker (37 °C, 200 rpm). The cells were spread on anti-off slides (Solarbio, Beijing, China) and air-dried. After fixation with 4% paraformaldehyde (Beyotime, Shanghai, China) at 4 °C for 15 min, the samples were washed three times with phosphate-buffered saline (PBS, Solarbio, Beijing, China). Subsequently, MG1655 cells were stained for 10 min in the dark with 10 μg/mL of 4′,6-diamidino-2-phenylindole (DAPI, Solarbio, Beijing, China). Following staining, the samples were washed three times with PBS, and a blocking agent containing anti-fluorescence quencher (Applygen, Beijing, China) was added, and then sealed with cover slips (Solarbio, Beijing, China). Samples were visualized using a Nikon Eclipse Ni microscope. Sample preparation for proteomic analysis Twelve individual colonies of MG1655 were picked from LB agar plate using sterile toothpicks and inoculated in LB broth overnight at 37 °C and 200 rpm. One sample with an OD[600] approximately half that of the others was excluded due to potential growth defects. The remaining cultures were diluted 1:100 in fresh LB broth and cultivated at 37 °C and 200 rpm. Upon reaching an OD[600] of 0.4, RJ was added to five out of the eleven flasks at a final concentration of 20 mg/mL. The other six flasks received an equal volume of DI water as a control. All flasks were incubated for an additional hour before bacterial samples were harvested from the cell culture by centrifugation at 6000 rpm for 10 min. The samples were then washed and lysed immediately for subsequent LC–MS/MS analysis. Sample preparation for RT-qPCR analysis, ELISA, and ROS determination The overnight culture of MG1655 was diluted 1:100 in fresh LB broth and cultivated in a shaking incubator (37 °C, 200 rpm). Once the OD[600] reached 0.4, the cell culture was exposed to 20 mg/mL of RJ in fresh LB both at 37 °C for 30 min and 1 h before being harvested for further analysis. A bacterial suspension treated with DI water served as the control. Each experimental group comprised three biological replicates. Sample preparation for LC–MS/MS analysis MG1655 cells were harvested from the cell culture through centrifugation at 6000 rpm for 10 min and subjected to six washes with 1 × PBS at pH 7.0. Subsequently, the cells were lysed on ice with lysis buffer (BestBio, Shanghai, China) comprising 8 M urea (Solarbio, Beijing, China), 2 M thiourea (Sigma Aldrich, USA), 4% 3-[(3-cholamidopropyl) dimethylammonio-1-propanesulfonate (CHAPS, Solarbio, Beijing, China), 20 mM DTT (Solarbio, Beijing, China). The resulting cell lysate was centrifuged at 15,000 g for 20 min at 4 °C to obtain the supernatant. Proteins were precipitated by adding three volumes of ice-cold acetone, followed by incubation for 30 min on ice and subsequent centrifugation at 15,000 g for 20 min at 4 °C. The precipitated proteins were resuspended in 5 M urea and further diluted with 40 mM NH[4]HCO[3] for the quantification using a Bradford assay kit (ThermoFisher Scientific). The protein samples were subjected to reduction with 10 mM DTT for 1 h and alkalized with 50 mM iodoacetamide (Solarbio, Beijing, China) in the dark for 1 h. The samples were subsequently digested using sequencing grade trypsin (Promega, USA) overnight at 37 °C. The digestion process was terminated by the addition of 1 µL formic acid (ThermoFisher Scientific) to halt trypsin activity and stabilize peptides. Purified peptide samples were obtained using SpeedVac (RVC2018, Germany) for subsequent LC–MS/MS analysis. LC–MS/MS method Liquid Chromatography-Tandem Mass Spectrometry (LC–MS/MS) analysis was conducted on a Q-Exactive Plus Orbitrap mass spectrometer coupled with EASY-nLC 1000 liquid chromatograph (Thermo Fisher Scientific). Initially, peptide samples were introduced into a trap column (2 cm in length, 5 μm C18 beads, Thermo Fisher Scientific) with mobile phase A (0.1% Formic acid) at a flow rate of 5 μL/min. Subsequently, the samples underwent separation in an analytical column (15 cm in length, 3 μm C18 beads, Thermo Fisher Scientific) with a 2 h gradient elution procedure: starting from 100% mobile phase A to 8% mobile phase B (0.1% formic acid in 80% acetonitrile) for 5 min, 8–20% phase B for 55 min, 20–30% phase B for 10 min, 30–100% phase B for 5 min, and finally, 100% phase B for 10 min. The eluted peptide samples were then introduced into the mass spectrometer equipped with an electrospray ionization (ESI) probe. Ion signals were collected in a data-dependent mode with specific settings: full scan (resolution: 70,000; scan range: 300–1800 m/z); MS/MS scan (resolution: 17,500; isolation: 2 m/z; normalized collision energy: 27); dynamic exclusion (exclusion of unassigned, 1; and > 8 charged precursors; peptide match: preferred; exclude isotopes on; dynamic exclusion time 10 s). The MS/MS data were retrieved using Xcalibur software (v2.2, Thermo Fisher Scientific, USA), and analyzed with PEAKS software (v7.5, Bioinformatics Solutions, Canada) against the MG1655 GenBank database (Taxonomy ID: 511145). For the search parameters, the precursor mass tolerance was set at 15.0 ppm, and the fragmentation tolerance was set at 0.05 Da. Carbamidomethylation (C)/ + 57.02 Da was chosen as a fixed modification, while oxidation (M)/ + 15.99 Da was selected as the variable modifications. A false discovery rate (FDR) ≤ 1.0% (− 10 log p ≥ 20.0) was used at both the protein and peptide levels. Additionally, proteins were included in the analysis only when identified with at least two unique peptides. For relative quantification, a label-free approach in the Q module of PEAKS software was used. Proteins were considered significant DEPs between groups if they had a fold change (|FC|) ≥ 1.5 and p-value < 0.05. Bioinformatic analysis To identify biological processes and pathways that are significantly affected by RJ treatment, Gene Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG)^[46]11 pathways enrichment was conducted using the online DAVID tool v6.8 ([47]https://david.ncifcrf.gov/). Significance was assessed using the Benjamini–Hochberg method to determine p-values, and adjusted for multiple t-test using Two-stage linear step-up procedure (FDR < 0.1). FDR < 0.1 was chosen to allow for a broader exploration and detection of potentially relevant findings. To visualize and analyze the interactions between DEPs, the STRING database v12.0 ([48]https://string-db.org) was used to construct PPI network using interaction sources, including Textmining, Experiments, Databases, Co-expression, Neighborhood, Gene Fusion, and Co-occurrence. The cut-off value of interaction score was set at 0.4 (the default value). Quantitative real-time PCR analysis The total RNA of MG1655 cells from the control group (DI water added) and treatment group (RJ added) was extracted with TRIzol reagent (Invitrogen, USA) following the manufacturer’s instructions. The concentration of the RNA samples was examined by NanoDrop 2000 spectrophotometer (Thermo Fisher, USA) and the quality of RNA was examined by 1% agarose gel electrophoresis, with acceptable purity ratios of A260/A280 between 1.8 and 2.0. The cDNA was synthesized from 1 μg RNA template using PrimeScrip RT reagent Kit (Takara, Japan). Then 2 μL 1:30 diluted cDNA was taken for RT-qPCR (TB Green Premix Ex Taq II) (Takara, Japan) on LineGene 9600 Plus (Bioer, China) and ran with the required thermal profile. All the samples were analyzed in triplicates and the relative quantification of gene expression was calculated by 2^−∆∆Ct methods with 16S rRNA as endogenous control. The primer sequences used for RT-qPCR are listed in Table [49]S1. Enzyme-linked immunosorbent assay (ELISA) Bacterial cells of control group (DI water added) and treatment group (RJ added) were subjected to triple washing in PBS at pH of 7.0, followed by lysis in 500 μL of cell lysis buffer (BestBio, Shanghai, China). The mixture was centrifuged at 12,000 g for 5 min, and the supernatant was collected. The enzymatic activities of superoxide dismutase (SOD), catalase (KatG) and malondialdehyde (MDA) were determined using ELISA kits (Shanghai Enzyme-linked Biotechnology Co., Shanghai, China). Briefly, 50 μL of standard solutions and lysed bacterial samples were added to the plate and incubated at 37 °C for 30 min. Subsequent to five washes, 100 μL of HRP-SA was added to each well and incubated at 37 °C again for 30 min. After five more washes, 50 μL of Chromogen Solution A and 50 μL of Chromogen Solution B were added to each well, and the wells were incubated for 10 min at 37 °C. Finally, termination solution was added to each well, and the optical density value at 450 nm was measured to construct the standard ELISA curve. All assays were performed at least 3 individual experiments, each comprising no less than three replicates. Determination of ROS levels The Reactive Oxygen Species (ROS) levels were determined using Reactive Oxygen Species Assay Kit (Applygen Technologies, Beijing, China). Briefly, 1 × 10^7/mL bacterial cells from each sample group were incubated in a 24-well plate with 10 µM 2′7′-dichlorofluorescein diacetate (DCFH-DA) or an equal volume of PBS (negative control) for 30 min in the dark at 37 °C for cellular incorporation. Following centrifugation at 1000 g and two washes with ice-cold PBS, cells were resuspended in 500 μL of ice-cold PBS. Fluorescence intensities were determined using a SpectraMax i3x fluorescence microplate reader (ThermoFisher, CA, USA), with excitation at 502 nm and emission at 530 nm. Fluorescence intensities of the treatment group (RJ) were normalized to the mean values of the control group (DI water) to quantify intracellular ROS levels. All assays were performed at least 3 individual experiments, each comprising no less than three replicates. Statistical analysis Results are presented as the mean ± standard error of the mean (SEM), based on at least three independent experiments. The differences between sample groups were evaluated by an unpaired two-tailed Student’s T-test, with significance levels set at p < 0.05 after applying the Holm-Sidak correction. Significance levels were denoted as follows: *p < 0.05, **p < 0.01, ***p < 0.001. The response curves of MG1655 to different concentrations of RJ treatment were simulated using the built-in nonlinear regression model in GraphPad Prism software v8.0 (Dotmatics, MA, USA). Results Anti-bacterial activity of RJ To explore the antibacterial efficacy of RJ, MG1655 cells were subjected to varying concentrations of RJ, and the “disc diffusion test” was conducted. As shown in Fig. [50]1A and Table [51]S2, RJ concentrations ranging from 0 to 50 mg/mL, resulting in a dose-dependent inhibition of MG1655 growth on LB agar plate. Quantification of the zone diameter based on three independent experiments revealed that the mean and standard deviation of the diameter of zones of inhibition under 20 mg/mL RJ to 50 mg/mL RJ ranging from 9.7 ± 0.06 to 25.8 ± 0.31 (Table [52]S2). The growth kinetics of MG1655, when challenged with diverse RJ concentrations, revealed an apparent IC50 of RJ against MG1655 at 20 mg/mL. This was evidenced by the optical density at OD[600] of MG1655 during the stationary phase, which was approximately half that of the control group (Ctrl) (Fig. [53]1B). Notably, this IC50 value aligns well with the findings from a previous investigation conducted by Ratanavalachai et al.^[54]12. Figure 1. [55]Figure 1 [56]Open in a new tab Inhibitory effects of RJ on MG1655. (A) Zones of inhibition for MG1655. Sterile discs, ranging from control (sterile discs with sterile H[2]O) to various concentrations (10 to 50 mg/mL) of RJ. The diameters of the inhibition zones were further determined based on three independent experiments (Table [57]S2). (B) Growth curves of MG1655 in LB broth treated with various concentrations of RJ in a shaking incubator at 37 °C, 200 revolutions per minute (rpm). The experiment was repeated three times and data was presented by Mean with SEM. (C) Fluorescence microscopy visualization of MG1655 cells with 20 mg/mL of RJ (RJ samples) or without RJ treatment (Ctrl samples). Nuclear labeling by DAPI staining (blue) and differential interference contrast (DIC) images were overlaid to produce the merged images. Scale bars = 10 μm. The experiment was repeated three times. (D) Changes in the length of MG1655 cells following treatment with 20 mg/mL RJ. The mean of 20 measurements in each group is plotted with error bars representing SEM. Statistical significance was determined by two-tailed unpaired Student's t-test. (E) Quantification of bacterial gene expression levels in response to RJ treatment using RT-qPCR. The reference gene 16S rRNA was used. The mean of three biological replicates is plotted with error bars representing SEM. Statistical significance was determined by two-tailed unpaired Student's t-test. ** and *** indicate statistical significance at p < 0.01 and p < 0.001, respectively. Primer sequences used were listed in Table [58]S1. The morphological alterations induced by 20 mg/mL of RJ on MG1655 were visualized using a fluorescence microscope (Fig. [59]1C). Statistical analysis of around 20 cells in both the control (Ctrl) and RJ-treated (RJ) groups revealed that RJ group cells were significantly longer (5.29 ± 0.98 μm) than Ctrl group cells (3.34 ± 0.41 μm; p < 0.001), as shown in Fig. [60]1D. Previous studies have investigated the inhibitory effects of RJ on bacterial growth^[61]4,[62]13. This study is the first to report that the antibacterial mechanisms of RJ are involved in cell elongation and divisionin E.coli. Bacterial stress-responsive genes, such as katG, osmC, tpX, sodA, and sodB, play crucial roles in bacterial survival and adaptability under stressful conditions^[63]14. KatG encodes catalase for detoxifying hydrogen peroxide^[64]15,[65]16; tpx encodes thiol peroxidase for reducing peroxides; sodA and sodB encode superoxide dismutases for eliminating superoxide radicals^[66]17; and osmC encodes an osmotically inducible protein aiding in osmotic stress resistance. These genes collectively enhance the bacterial defense system against various environmental stressors. To evaluate how MG1655 responds to stress induced by RJ, the expression of these genes was quantified using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). As shown in Fig. [67]1E, RJ treatment significantly inhibited the expression of all studied genes (p < 0.001). The fold change (FC = RJ/Ctrl) values ranged from 0.07 to 0.46. This result indicates that RJ can suppress the expression of oxidative stress response genes, leading to increased susceptibility to oxidative damage. Identification of DEPs between RJ treated and untreated cells To elucidate the global alterations in gene expression within MG1655 treated with RJ, LC–MS/MS-based proteomic analysis was performed. A total of 1511 proteins were identified in MG1655 samples without RJ treatment (C group), with 1078 (71%) proteins found in all samples (Fig. [68]2A). In RJ-treated samples (T group), a total of 1154 proteins were identified, with 774 (67%) proteins were detected in all samples (Fig. [69]2B). A total of 1057 proteins were common to both the C and T groups (Fig. [70]2C), based on the criterion that a protein was considered “present” if it was identified in at least four samples within the respective group. Figure 2. [71]Figure 2 [72]Open in a new tab Effects of RJ on the protein expression profile of MG1655 cultivated in LB medium. (A) Venn diagram illustrates the intersection of identified proteins across the six biological replicates in the control group, labeled as C1, C2, C3, C4, C5, and C6. (B) Venn diagram illustrates the overlap of identified proteins among the five biological replicates in the RJ treatment group (the final concentration of RJ is 20 mg/mL), labeled as T1, T2, T3, T4 and T5. (C) Venn diagram displays the proteins identified in the control and treatment groups. (D) Heatmap displays comparisons of calculated Z-scores for the DEPs (|FC|> 1.5, p < 0.05) between the control group and treatment group. Each row corresponds to a single protein, and each column represents an individual sample. Z-scores were computed on a row-by-row basis by subtracting the mean intensity and then dividing by the standard deviation. Increasing intensity in the positive range (red) represents abundances that are greater than the mean value, and decreasing intensity in the negative (blue) represents abundances that are lower than the mean value. In the T group, 200 proteins exhibited down-regulation with the absolute FC (|FC|) ≥ 1.5 (p ≤ 0.05), while nine proteins were found to be up-regulated (Fig. [73]2D and Table [74]S3). Quantifying these changes provides insights into the molecular mechanisms underlying the antibacterial effects of RJ and its broader impact on bacterial physiology. Functional analysis of DEPs To unravel the antimicrobial mechanisms of RJ, bioinformatic methods were employed to identify the enriched GOBP terms (Fig. [75]3A) and KEGG pathways (Fig. [76]3B) associated with the DEPs (Fig. [77]2D and Table [78]S3). The results of GOBP analysis demonstrated a significant enrichment of DEPs in various biological processes, including gene transcription and translation, carbohydrate metabolism, and oxidative stress. The prominent terms included “DNA-templated transcription, termination (p = 3.50E−03)”, “regulation of translation (p = 1.30E−04)”, “glycolytic process (p = 2.20E−08)”, and “response to oxidative stress (p = 2.15E−05)”, among others (depicted in Fig. [79]3A). Figure 3. [80]Figure 3 [81]Open in a new tab Functional analysis of DEPs (|FC|> 1.5, p < 0.05). (A) GOBP enrichment results. The numerical values on the bar chart denote the percentage of the corresponding protein quantity for each GO term. The Y-axis label indicates the GOBP term, and X-axis represents the statistical significance of the enrichment (-log10(p-value)). (B) KEGG pathway enrichment results. The Y-axis label indicates the pathway, while the X-axis label represents the rich factor. The size and color of each bubble reflect the quantity of DEPs enriched in the pathway and the significance of enrichment, respectively (Rich factor is calculated as: the ratio of the number of DEPs enriched in the functional term to the number of all genes in the background gene set). In alignment with the GOBP analysis findings, the KEGG pathway enrichment analysis disclosed significant enrichment in pathways such as “Ribosome”, “RNA polymerase”, “Carbon metabolism”, “Pentose phosphate pathway”, “Methane metabolism”, “Fatty acid biosynthesis”, “Citrate cycle” and “Tyrosine metabolism” (p-adj < 0.05, Fig. [82]3B). These terms revealed that RJ treatment affects the fundamental processes of gene expression, the key pathway for energy production, and highlighted the bacterial response to oxidative damage. RJ inhibits RNA polymerases and Ribosomal proteins Transcription and translation are the essential processes that facilitate the transfer of genetic information from DNA. The transcription process is intricately controlled and governed by DNA-dependent RNA polymerase (RNAP). Notably, all RNAP subunits (α, β, β’, and ω) encoded by rpoA, rpoB, rpoC, and rpoZ, respectively, were significantly suppressed by RJ, with FC ranging from 0.24 to 0.35 (Fig. [83]3B and Table [84]1). This RJ-induced suppression of these RNAP subunit-encoding genes was validated through RT-qPCR, in cells treated with 20 mg/mL RJ for 30 min and 1 h (Fig. [85]4A). Table 1. Quantification of RNAP subunits via label-free protein assessment based on LC–MS/MS. UniProt Accession Protein Name Gene Name FC (T/C)^a p-value^b [86]P0A8T7 RNA polymerase subunit β' rpoC 0.24 1.12E−07 [87]P0A7Z4 RNA polymerase subunit α rpoA 0.28 1.85E−06 [88]P0A8V2 RNA polymerase subunit β rpoB 0.33 3.35E−10 [89]P0A800 RNA polymerase subunit ω rpoZ 0.35 2.86E−07 [90]Open in a new tab ^aThe FC was determined by the ratio of the mean peak intensities of the protein within the T group to the mean peak intensities of the protein at the C group. ^bSignificance is determined using the Two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, with Q = 1%. More details were provided in Table [91]S3. Figure 4. [92]Figure 4 [93]Open in a new tab Inhibition of RNAP and ribosomal proteins by RJ. (A) Impact of RJ on the expression of rpoA, rpoB, rpoC, and rpoZ. MG1655 cells were exposed to RJ for 30 min or 1 h, and the expression of the RNAP coding genes were examined by RT-qPCR. 16S rRNA was utilized for normalization. The experiment was repeated three times and data was presented by Mean with SEM. Statistical significance was determined by two-tailed unpaired Student's t-test. Significance levels are denoted as follows: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001. Primer sequences used are listed in Table [94]S1. (B) GOBP enrichment analysis of ribosome-associated DEPs. Cytoscape software v3.9.1 ([95]https://cytoscape.org/) with ClueGO plugin (v2.5.9) was used for the visualization. Functional nodes and edges illustrate the connectivity of the GO terms in network, with a kappa score of 0.4 and p-value ≤ 0.05. The size of DEP nodes corresponding to the FC value, whereas the size of nodes representing GOBP terms indicate the associated p-value. The translation process is regulated by the interaction of Ribosomal proteins with the translation complex. In this study, RJ down-regulated 44 Ribosomal proteins, including 27 large subunits and 17 small subunits, constituting 81% of all Ribosomal proteins encoded in the MG1655 genome (Fig. [96]3A and Table [97]2). Functional enrichment analysis identified the functional clusters of the differentially expressed Ribosomal proteins. As demonstrated in Fig. [98]4B, five GO clusters were identified, including “response to antibiotic”, “maintenance of translational fidelity”, “regulation of translation”, “negative regulation of translation” and “negative regulation of cytoplasmic translation” (Fig. [99]4B). Table 2. Quantification of Ribosomal subunit proteins via label-free protein assessment based on LC–MS/MS. UniProt Accession Protein Name Gene Name FC (T/C)^a p-value^b [100]P0AG59 Ribosomal subunit protein S14 rpsN 0.12 5.95E−09 [101]P0A7T3 Ribosomal subunit protein S16 rpsP 0.2 1.42E−07 [102]P0A7U3 Ribosomal subunit protein S19 rpsS 0.2 3.67E−06 [103]P0A7T7 Ribosomal subunit protein S18 rpsR 0.22 1.47E−08 [104]P0AG63 Ribosomal subunit protein S17 rpsQ 0.24 6.62E−08 [105]P0A7V0 Ribosomal subunit protein S2 rpsB 0.27 2.94E−07 [106]P0AG67 Ribosomal subunit protein S1 rpsA 0.29 5.32E−06 [107]P0ADZ4 Ribosomal subunit protein S15 rpsO 0.3 2.00E−07 [108]P02358 Ribosomal subunit protein S6 rpsF 0.3 4.98E−08 [109]P0A7V8 Ribosomal subunit protein S4 rpsD 0.31 2.71E−08 [110]P02359 Ribosomal subunit protein S7 rpsG 0.33 3.70E−06 [111]P0A7R5 Ribosomal subunit protein S10 rpsJ 0.34 9.60E−05 [112]P0A7W1 Ribosomal subunit protein S5 rpsE 0.35 2.98E−06 [113]P0A7S3 Ribosomal subunit protein S12 rpsL 0.37 8.49E−06 [114]P0A7W7 Ribosomal subunit protein S8 rpsH 0.38 4.79E−04 [115]P0A7S9 Ribosomal subunit protein S13 rpsM 0.39 2.95E−06 [116]P0A7X3 Ribosomal subunit protein S9 rpsI 0.52 1.97E−04 [117]P0A7P5 Ribosomal subunit protein L34 rpmH 0.02 2.96E−04 [118]P0AG48 Ribosomal subunit protein L21 rplU 0.24 2.29E−08 [119]P0A7M9 Ribosomal subunit protein L31 rpmE 0.25 1.87E−07 [120]P62399 Ribosomal subunit protein L5 rplE 0.25 4.15E−08 [121]P60438 Ribosomal subunit protein L3 rplC 0.29 4.88E−09 [122]P0A7M6 Ribosomal subunit protein L29 rpmC 0.3 1.35E−05 [123]P0A7L0 Ribosomal subunit protein L1 rplA 0.32 6.28E−08 [124]P0AG55 Ribosomal subunit protein L6 rplF 0.33 1.98E−08 [125]P0A7J7 Ribosomal subunit protein L11 rplK 0.34 4.95E−08 [126]P60422 Ribosomal subunit protein L2 rplB 0.34 1.61E−04 [127]P0ADZ0 Ribosomal subunit protein L23 rplW 0.34 7.39E−06 [128]P02413 Ribosomal subunit protein L15 rplO 0.36 2.56E−05 [129]P0A7R1 Ribosomal subunit protein L9 rplI 0.36 6.17E−07 [130]P0A7K6 Ribosomal subunit protein L19 rplS 0.37 6.73E−06 [131]P0A7L3 Ribosomal subunit protein L20 rplT 0.37 1.11E−05 [132]P0ADY7 Ribosomal subunit protein L16 rplP 0.39 5.67E−07 [133]P0A7J3 Ribosomal subunit protein L10 rplJ 0.41 2.26E−07 [134]P0ADY3 Ribosomal subunit protein L14 rplN 0.41 6.17E−07 [135]P0AG44 Ribosomal subunit protein L17 rplQ 0.44 3.59E−07 [136]P0C018 Ribosomal subunit protein L18 rplR 0.44 1.34E−04 [137]P60723 Ribosomal subunit protein L4 rplD 0.44 3.68E−07 [138]P0AA10 Ribosomal subunit protein L13 rplM 0.46 6.53E−06 [139]P0A7L8 Ribosomal subunit protein L27 rpmA 0.46 1.58E−04 [140]P0A7N4 Ribosomal subunit protein L32 rpmF 0.46 3.01E−04 [141]P0A7K2 Ribosomal subunit protein L12 rplL 0.47 4.67E−07 [142]P61175 Ribosomal subunit protein L22 rplV 0.47 6.64E−06 [143]P68919 Ribosomal subunit protein L25 rplY 0.47 4.31E−06 [144]Open in a new tab ^aThe FC was determined by the ratio of the mean peak intensities of the protein within the T group to the mean peak intensities of the protein at the C group. ^bSignificance was determined using the Two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, with Q = 1%. More details were provided in Table [145]S3. The suppression of RNAP subunits and ribosomal proteins has a cascading effect on bacterial cellular functions. By disrupting transcription, mRNA synthesis is compromised, which in turn impairs translation and reduces the production of functional proteins. This reduction in protein synthesis disrupts metabolic processes, weakens cellular integrity, and diminishes the bacterial response to environmental stresses. RJ induces oxidative stress The results of GOBP enrichment analysis (Fig. [146]3A and Table [147]3) and alterations in the expression of marker genes associated with oxidative stress (Fig. [148]1E) showed that the activities of key enzymes responsible for countering superoxide anion (O[2]^·−), specifically SOD, and the primary scavenging enzyme for hydrogen peroxide (H[2]O[2]), KatG, exhibited significant reduction following exposure to 20 mg/mL RJ for both 30 min and 1 h (Fig. [149]5A,B). In line with the results from RT-qPCR analysis of gene expression (Fig. [150]1E) and enzymatic activities (Fig. [151]5A,B), the overall degree of oxidative stress, was demonstrated by a notable increase in ROS formation when cells were exposed to RJ for 30 min and 1 h (increased by 93.1% and 182.9%, respectively) (Fig. [152]5C). Furthermore, the concentration of MDA, a final product of polyunsaturated fatty acids peroxidation, that widely recognized as a marker of oxidative stress and antioxidant status^[153]18, exhibited an approximate 20% elevation following treatment with RJ for 30 min and 1 h (Fig. [154]5D). The elevation in MDA levels indicates a significant increase in lipid peroxidation, a process in which reactive oxygen species (ROS) attack lipids in cell membranes. Given the diverse nature of cellular damage caused by oxidative stress, it is anticipated that the elevated ROS and MDA levels will impact multiple cellular components and functions. Table 3. Quantification of oxidative stress-related proteins via label-free protein assessment based on LC–MS/MS. UniProt Accession Protein Name Gene Name FC (T/C)^a p-value^b [155]P37903 Universal stress protein F uspF 0.02 1.04E−05 [156]P13029 Catalase katG 0.05 3.25E−06 [157]P0C0L2 Osmotically inducible peroxiredoxin osmC 0.07 1.17E−06 [158]P0ADU5 BOF family protein YgiW ygiw 0.12 2.48E−07 [159]P0AC62 Reduced glutaredoxin 3 grxC 0.15 5.02E−05 [160]P08200 Isocitrate dehydrogenase icd 0.19 3.37E−04 [161]P0A862 Lipid hydroperoxide peroxidase tpx 0.20 4.67E−06 [162]P0AGD3 Superoxide dismutase (Fe) sodB 0.22 2.62E−05 [163]P0AA25 Reduced thioredoxin 1 trxA 0.36 2.05E−04 [164]P0A9P0 Lipoamide dehydrogenase lpd 0.40 6.94E−06 [165]P0A8G6 NAD(P)H dehydrogenase (quinone) wrbA 0.50 1.52E−03 [166]P00448 Superoxide dismutase (Mn) sodA 0.66 4.65E−02 [167]Open in a new tab ^aThe FC was determined by the ratio of the mean peak intensities of the protein within the T group to the mean peak intensities of the protein at the C group. ^bSignificance is determined using the Two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, with Q = 1%. More details were provided in Table [168]S3. Figure 5. [169]Figure 5 [170]Open in a new tab RJ induces oxidative stress in MG1655 cells. (A) Enzymatic activities of SOD were assessed by ELISA following 30 min and 1 h of RJ treatment. The Y-axis represents enzyme activity (U/g) of SOD. (B) Enzymatic activities of KatG were assessed by ELISA following 30 min and 1 h of RJ treatment. The Y-axis represents enzyme activity (U/g) of KatG. (C) Levels of ROS determined after 30 min and 1 h of RJ treatment. The Y-axis represents data normalized to the control group (Ctrl). (D) Levels of MDA determined after 30 min and 1 h of RJ treatment. The Y-axis represents the concentration (nmol/L) of MDA. The experiments were repeated three times and data were presented by Mean with SEM. Statistical significance was determined by two-tailed unpaired Student's t-test. Significance levels are denoted as follows: *, **, and *** indicate statistical significance at p < 0.05, p < 0.01, and p < 0.001, respectively. Construction of the PPI network and discovery of hub proteins and TFs To elucidate the principal regulators responsible for the antibacterial mechanism induced by RJ, we constructed a PPI network of DEPs with |FC|> 5 (p < 0.05), based on the STRING online database. Various interaction sources, including text mining, experiments, Databases, Co-expression, Neighborhood, Homology, Gene Fusion and Co-occurrence, were used for network construction (minimum required interaction score was 0.4). The resulting network, visualized through Cytoscape software v3.9.1 ([171]https://cytoscape.org/) with the CentiScape plugin v2.2, comprising of 48 nodes and 162 edges (Fig. [172]6A, Table [173]S4). Figure 6. [174]Figure 6 [175]Open in a new tab The PPI network and key regulators. (A) PPI network of the DEPs (FC > 5, P < 0.05) was constructed using STRING tools. Transcription factors (TFs) are represented by ellipses, while the remaining proteins are indicated by circles. The thickness of edges indicates the strength of the interaction. Node color, ranging from red to yellow, indicates the decreasing order of degree. Enrichment results of GOBP are indicated by dashed circles. The positive and negative regulation of TFs on their target genes are indicated by lines with green and red color, respectively. (B) Alterations in the expression of crp, ihfA, and ihfB in MG1655 cells after 1 h treatment with various concentrations of RJ. The reference gene used was 16S rRNA. The experiment was repeated three times and data was presented by Mean with SEM. *** denotes statistical significance of p < 0.001. Primer sequences used were listed in Table [176]S1. Functional enrichment analysis of the DEPs within the PPI network identified GOBPs, including tricarboxylic acid cycle”, “pentose phosphate pathway”, “response to oxidative stress”, and “cellular oxidant detoxification” (Fig. [177]6A, Table [178]S5). These observations were consistent with the results presented in Fig. [179]3A. Amone the PPI network genes, twelve hub proteins were identified based on their high correlation with other nodes, each having more than 10 links (node degree K ≥ 10)^[180]19. To find the regulators governing the expression of these hub proteins, a TF investigation was conducted using gene-transcriptional regulation annotated in EcoCyC database ([181]https://ecocyc.org/). Notably, hub proteins GltA, Mdh, AceA, SucC and GlpK were positively regulated by the cAMP receptor protein (CRP). Additionally, the integration host factor (IHF), composed of two highly homologous subunits IHFA and IHFB, a positive regulator of the hub protein GltA and AceA, while concurrently exerting negative regulation on the hub protein SucC and Icd (Fig. [182]6A, Table [183]S4). Label-free quantification results revealed significant down-regulation of the TFs CRP, IhfA and IhfB by RJ, with FCs of 5.94 ± 0.17, 6.73 ± 0.15, and 13.72 ± 0.07, respectively (p < 0.001, Table [184]S4). The inhibitory effects of RJ on TF expression were further confirmed through RT-qPCR using the total RNA from MG1655 cells treated with RJ concentrations ranging from 5 to 40 mg/mL for 1 h (Fig. [185]6B). For crp, the FCs in expression ranged from 0.10 to 0.68 (p < 0.001); for ihfA, the FCs ranged from 0.24 to 0.64 (p < 0.001), for ihfB, the FCs ranged from 0.21 to 0.39 (p < 0.001) (Fig. [186]6B). The downregulation of the global regulators CRP and IHF can alter multiple downstream genes, impacting various cellular pathways and processes. This will be further discussed in the discussion section. Discussion The imperative need for developing innovative natural antimicrobial drugs is emphasized by the challenges posed by AMR^[187]20,[188]21. RJ has been well known for its potential health benefits by exhibiting a broad spectrum of antibacterial activities^[189]3,[190]4. Notably, it has garnered attention for its ability to combat antibiotic resistance in AMR P. aeruginosa^[191]22, positioning it as a promising candidate for novel natural antibiotics. Transcription and translation represent the fundamental processes responsible for transferring the genetic information stored in DNA. In bacteria, transcription is facilitated by the RNAP core enzyme, composed of α[2]ββ’ω subunits. Given the vital role of transcription and the high conservation of RNAP in the bacterial realm, the discovery of antibacterial agents targeting on RNAP has been a focus since the mid-twentieth century^[192]23. A plethora of compounds, whether synthetic or isolated from microorganisms, have been identified for their ability to modulate transcription by inducing structural conformational changes in the RNAP core enzyme through specific site binding. Notable examples include Rifampicin, Streptolydigin, Fidaxomicin and Lipiarmycin^[193]24. Furthermore, certain compounds act on TFs associated with RNAP, thereby interfering with their binding to RNAP. Instances involve Bicyclomycin and the SB series^[194]24. A significant hurdle in the clinical development of many tested compounds is the frequent emergence of bacterial resistance due to mutations in their binding sites^[195]25. Currently, only Rifampicin and Fidaxomicin/Lipiarmycin have received approval for market use. In contrast to the aforementioned drugs, which primarily target transcription processes by inducing structural changes in RNAP or its associated factors, our results demonstrate that RJ inhibits E. coli growth and survival by suppressing all the subunits of RNAP core enzymes (Fig. [196]4A and Table [197]1). This distinctive mechanism of action suggests that RJ could offer an alternative approach to combat bacterial resistance, making it a promising candidate for natural antibacterial strategies. Further research and clinical studies are necessary to fully understand the potential of RJ as an effective and sustainable solution against bacterial resistance. In addition to its ability to inhibit transcription, RJ also exerts an influence on the translational process mediated by ribosomal proteins, specifically targeting the ribosome (Fig. [198]4B). Antibiotics, which are broadly classified as agents that hinder protein synthesis, can be categorized into two primary subclasses based on their binding sites on ribosome: the 50S inhibitors and 30S inhibitors^[199]6. In this study, we found that the treatment of 20 mg/mL RJ resulted in the down-regulation of 44 ribosomal proteins, encompassing 80% of 30S subunits and 77% of 50S subunits (Table [200]2). These findings shed light on the central role of RJ in regulating protein synthesis and maintaining translation fidelity (Fig. [201]4B). Intriguingly, RJ suppressed ribosomal proteins linked to "response to antibiotic", including RplF, RplV, RplD, RpsE, RpsQ, RpsD and RpsL (Fig. [202]4B), suggesting potential benefits in combating resistance and enhancing treatment efficacy. ROS which includes not only O[2]^·− but also other oxygen-derived molecules like H[2]O[2] and hydroxyl radicals (^•OH), form during metabolic processes and exposure to stressors like low temperatures, chemicals, and UV light^[203]26. Improper ROS regulation, due to their high reactivity, can cause oxidative stress, resulting in cellular damage and dysfunction, including DNA mutations, protein misfolding, and lipid peroxidation, etc^[204]27. Bacteria employ an antioxidant defense system that includes enzymes like SOD and catalase, to mitigate the harmful effects of ROS. In the case of E. coli, it possesses two cytoplasmic SOD enzymes (MnSOD encoded by sodA, and FeSOD encoded by sodB), and two catalases known as HPI and HPII (encoded by katG and katE, respectively). Antibiotics lethality is often accompanied by ROS generation^[205]8,[206]28. In our current investigation, it has been uncovered that the natural product RJ significantly elevates levels of ROS and MDA, established markers of bacterial oxidation (Fig. [207]5C,D)^[208]29. This increase in oxidative stress correlates with dysfunction of key antioxidant enzymes, such as SodA, SodB, and KatG, as illustrated in Figs. [209]1E, [210]5A,B. However, the expression level of katE did not show significant changes between the RJ-treated group and the control group (as indicated by RT-qPCR results and proteomic analysis, data not shown), suggesting that this gene is not strongly associated with the antibacterial effects of RJ. Our results suggest that RJ-induced ROS accumulation in E. coli was resulted from the inhibition of the antioxidant enzymatic system, ultimately leading to cellular damage. The most remarkable discovery in this study is the identification of host TFs responsive to RJ treatment (Fig. [211]6A,B). CRP, a central regulator on top of the hierarchical regulatory framework, plays a vital role in global stress responses such as oxidative stress, low pH, and osmotic pressure^[212]30. CRP alterations or amino acid substitutions impact hundreds of genes^[213]31. Particularly, it has been verified that E. coli crp mutant exhibited strong resistance to oxidative stress^[214]32. In this study, RJ strongly inhibited the expression of crp even with low-dose treatment (Fig. [215]6B). As a result, the control exerted by CRP over the TCA cycle and cellular responses to oxidative stress was substantial (Fig. [216]6A; Tables [217]S4, [218]S5). The TCA cycle serves as the ultimate converging route for the oxidative breakdown of fuel molecules (amino acids, fatty acids, and carbohydrates) in E. coli. Following RJ treatment, the key enzymes in the TCA cycle under the control of CRP exhibited down-regulation, including mdh, sucB, sucC, sucD, and gltA (Table [219]S3). As an effect of RJ treatment, the loss of the certain enzymes of TCA cycle resulted from CRP down-regulation, may lead to reduced pool of NADH and decreased production of superoxides, as indicated by Kohanski et al.^[220]8. In addition, pentose phosphate pathway (PPP), a route to stabilize NADPH levels that used as cofactor to reduce ROS through antioxidant systems^[221]33, was indirectly regulated by CRP through the downstream TF gene dksA and the downstream hub proteins TalA and TktB (Table [222]S4, Fig. [223]6A). Our results indicated that RJ treatment disrupts energy production and electron carrier production from the TCA cycle by repressing CRP, while reduced CRP expression may serve as a bacterial strategy to counteract RJ-induced oxidative damage. IHF is a global regulator operating in genetic recombination and exerts control over transcriptional and translational processes in gram-negative bacteria^[224]34. IHF-DNA binding is influenced by environmental factors like ion concentration^[225]35, though the precise role of environmental conditions in regulating its function remains unclear. In the present study, we report IHF's negative response to diverse RJ concentrations (Fig. [226]6B, Table [227]S3), disrupting bacterial growth by affecting multiple cellular processes, especially the TCA cycle (Fig. [228]6A). Recent research has unveiled IHF's role in regulating drug tolerance at low IHF levels, particularly its ability to repress the transcription of isocitrate dehydrogenase (Icd), a key component of the TCA cycle, while simultaneously activating the expression of isocitrate lyase (AceA), the first enzymes in the glyoxylate bypass^[229]36. We identified a number of TCA cycle genes and glyoxylate bypass genes controlled by IHF were significantly repressed by RJ, including citrate synthase (GltA), Icd, 2-oxoglutarate dehydrogenase E2 subunit (SucB), succinyl-CoA synthetase subunit α and β (SucD and SucC), malate dehydrogenase (Mdh), AceA and malate synthase A (AceB) (Table [230]S3). Together with the recent results on the role of CRP, DksA and IHF play in bacterial antibiotic tolerance and persister formation^[231]36–[232]38, this study elucidated the important roles of CRP, DksA and IHF in response to RJ treatment, and emphasize the intricate connections among the biological process modules that governed by the key TFs. The observed changes in Crp, DksA and IHF in RJ-treated samples indicate their critical importance for bacterial survival under the stress imposed by RJ. These findings not only enhance our understanding of RJ's antibacterial mechanism but also emphasize its potential as a natural alternative to conventional antibiotics. The identification of host factors CRP and IHF as crucial drug targets provides a foundation for further research and clinical studies, which are necessary to fully explore RJ's efficacy and sustainability as a solution against bacterial resistance. Limitations and future research Despite the insights gained, this study has limitations. First, it focused solely on E. coli K-12 MG1655, which may not fully represent bacterial responses to RJ across species. Further research with diverse bacterial strains is needed. Second, the LC–MS/MS method used is powerful but may not detect low-abundance proteins and crucial post-translational modifications in RJ's antibacterial mechanism. Third, examining RJ at multiple concentrations could reveal concentration-dependent effects. Additionally, in vitro conditions do not fully replicate in vivo environments, where host immune responses could affect RJ's activity, indicating that the transitioning to in vivo or clinical studies is crucial for understanding RJ's practical use. Conclusion This study elucidates the multifaceted impact of Royal jelly (RJ) on E. coli K-12, MG1655 strain. RJ not only hinders bacterial proliferation and induces morphological changes, but also exerts a significant influence on stress response genes, leading to heightened oxidative damage. The investigation into the molecular basis of RJ's antibacterial effectiveness revealed the role of RJ as global inhibitor of bacterial transcription, translation, and antioxidant activities, which was linked to the modulation of host regulators CRP and IHF. These findings advance our understanding of RJ's antibacterial mechanism and offer critical insight into the potential development of RJ as a source for novel natural antibacterial products. Supplementary Information [233]Supplementary Information 1.^ (10.6KB, xlsx) [234]Supplementary Information 2.^ (10KB, xlsx) [235]Supplementary Information 3.^ (63.2KB, xlsx) [236]Supplementary Information 4.^ (20.9KB, xlsx) [237]Supplementary Information 5.^ (10.5KB, xlsx) Acknowledgements