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. 2021 Jul 22;22(15):7840.
doi: 10.3390/ijms22157840.

Metabolomic Identification of Anticancer Metabolites of Australian Propolis and Proteomic Elucidation of Its Synergistic Mechanisms with Doxorubicin in the MCF7 Cells

Muhammad A Alsherbiny  1   2 , Deep J Bhuyan  1 , Ibrahim Radwan  3 , Dennis Chang  1 , Chun-Guang Li  1
Affiliations

Metabolomic Identification of Anticancer Metabolites of Australian Propolis and Proteomic Elucidation of Its Synergistic Mechanisms with Doxorubicin in the MCF7 Cells

Muhammad A Alsherbiny et al. Int J Mol Sci. .

Abstract

The combination of natural products with standard chemotherapeutic agents offers a promising strategy to enhance the efficacy or reduce the side effects of standard chemotherapy. Doxorubicin (DOX), a standard drug for breast cancer, has several disadvantages, including severe side effects and the development of drug resistance. Recently, we reported the potential bioactive markers of Australian propolis extract (AP-1) and their broad spectrum of pharmacological activities. In the present study, we explored the synergistic interactions between AP-1 and DOX in the MCF7 breast adenocarcinoma cells using different synergy quantitation models. Biochemometric and metabolomics-driven analysis was performed to identify the potential anticancer metabolites in AP-1. The molecular mechanisms of synergy were studied by analysing the apoptotic profile via flow cytometry, apoptotic proteome array and measuring the oxidative status of the MCF7 cells treated with the most synergistic combination. Furthermore, label-free quantification proteomics analysis was performed to decipher the underlying synergistic mechanisms. Five prenylated stilbenes were identified as the key metabolites in the most active AP-1 fraction. Strong synergy was observed when AP-1 was combined with DOX in the ratio of 100:0.29 (w/w) as validated by different synergy quantitation models implemented. AP-1 significantly enhanced the inhibitory effect of DOX against MCF7 cell proliferation in a dose-dependent manner with significant inhibition of the reactive oxygen species (p < 0.0001) compared to DOX alone. AP-1 enabled the reversal of DOX-mediated necrosis to programmed cell death, which may be advantageous to decline DOX-related side effects. AP-1 also significantly enhanced the apoptotic effect of DOX after 24 h of treatment with significant upregulation of catalase, HTRA2/Omi, FADD together with DR5 and DR4 TRAIL-mediated apoptosis (p < 0.05), contributing to the antiproliferative activity of AP-1. Significant upregulation of pro-apoptotic p27, PON2 and catalase with downregulated anti-apoptotic XIAP, HSP60 and HIF-1α, and increased antioxidant proteins (catalase and PON2) may be associated with the improved apoptosis and oxidative status of the synergistic combination-treated MCF7 cells compared to the mono treatments. Shotgun proteomics identified 21 significantly dysregulated proteins in the synergistic combination-treated cells versus the mono treatments. These proteins were involved in the TP53/ATM-regulated non-homologous end-joining pathway and double-strand breaks repairs, recruiting the overexpressed BRCA1 and suppressed RIF1 encoded proteins. The overexpression of UPF2 was noticed in the synergistic combination treatment, which could assist in overcoming doxorubicin resistance-associated long non-coding RNA and metastasis of the MCF7 cells. In conclusion, we identified the significant synergy and highlighted the key molecular pathways in the interaction between AP-1 and DOX in the MCF7 cells together with the AP-1 anticancer metabolites. Further in vivo and clinical studies are warranted on this synergistic combination.

Keywords: MCF7; apoptosis; breast adenocarcinoma; breast cancer; doxorubicin; metabolomics; propolis; proteomics; synergy.

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Conflict of interest statement

As a medical research institute, NICM Health Research Institute receives grants and donations from foundations, universities, government agencies, individuals, and industry. Sponsors and donors also provide untied funding to advance the vision and mission of the institute. The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Biochemometric and LCMS metabolomic-driven identification of anticancer metabolites in AP-1 against the MCF7 breast adenocarcinoma cells. (A) Score plot of the UPLC-MS (m/z 50–1200) principal component analyses (PCA) of the significant metabolome of propolis fractions as described by vectors of principal component 1 and 2, (B) Score plot of the UPLC-MS (m/z 50–1200) OPLS-DA selected metabolites of propolis fractions as described by vectors 1 and 2. (C) Loading scatter S-plot of the UPLC-MS OPLS-DA analysis of significant AP-1 metabolites, comparing the most active fraction with less active ones, with a legend indicating its chemical class and feature size reflects its abundance in the crude extract. (D) Average concentration inhibiting 50% of the MCF7 cells (IC50) upon treatment with AP-1 and its fractions for 72 h (n = 3, ns = non-significant, **** = statistically significant compared to the propolis extract at p < 0.0001 via one-way ANOVA with Dunnet’s correction of multiple comparisons).
Figure 2
Figure 2
Putative LC-MS identified metabolites in the AP-1 extract with potential anticancer activity against the MCF7 cells.
Figure 3
Figure 3
Synergy and sensitivity quantitation of AP-1 in combination with DOX against the MCF7 cells in a checkerboard assay.
Figure 4
Figure 4
Pearson’s correlation r values among different synergy quantitation metrics.
Figure 5
Figure 5
Relative ROS production in MCF7 (A) and its percentage viability (B) compared to the negative control, upon treatment with Australian propolis (AP-1), doxorubicin (DOX), their synergistic combination (PDOX) and the positive control tert-Butyl hydroperoxide (TBHP). Values expressed as mean ± standard deviation (SD) (n = 3), One-way ANOVA was used for multiple comparisons, #; statistically significant relative to negative control (p < 0.0001), *; p < 0.05, ***; p < 0.001, ****; p < 0.0001.
Figure 6
Figure 6
(A) Flow cytometric assessment of apoptotic profiles of the MCF7 breast cancer cell line and the images are representative of three separate experiments, (B) Cell percentage analysis in different treatment groups in quadruplicates. The AP-1 (100 µg mL−1), DOX (0.29 µg mL−1) and their most synergistic combination (at half-dose; 50 µg mL−1 AP-1 + 0.15 µg mL−1 DOX) with the vehicle control were implemented using antibodies against Annexin-V CF-Blue and the reporter 7AAD after 24 h of treatment. Superscript letters indicate statistical significance derived from two-way ANOVA and Tukey’s multiple comparisons within the same cell group (bar colour) where different letters are statistically significant with p < 0.0001 (n = 4). Raw data are available in Supplementary Table S3.
Figure 7
Figure 7
Differently expressed apoptotic proteins in the MCF7 cell lysates after treatment with AP-1, DOX, and the synergistic combination (PDOX). (A) Mean apoptotic proteins expression heatmap of the proteome arrays processed data after treatment of MCF7 cells with the vehicle (0.5% DMSO), 0.29 µg mL−1 DOX, 100 µg mL−1 AP-1 and their synergistic combination with hierarchical clustering of the groups using the Euclidean distance measure and Ward clustering algorithm. (B) The MCF7 lysates were analysed by Proteome ProfilerTM human apoptotic arrays after 24 h of treatment. The significant features are marked with blue and red rectangles (other than the vehicle control), indicating the downregulation and upregulation of proteins, respectively. The yellow and green rectangles on the control array indicate the significant proteins identified by coefficient and VIP scores of the PLS-DA model, respectively. Protein coordinates are listed in Table S4. (C) The significantly dysregulated apoptotic proteins after DOX treatment as selected by volcano plot compared to the control with the fold change (FC) threshold (x) 1.3 and t-test threshold (y) 0.05. (D) The significantly dysregulated apoptotic proteins after AP-1 treatment as selected by volcano plot compared to the vehicle control with the FC threshold (x) 1.3 and t-test threshold (y) 0.05. (E) The significantly dysregulated apoptotic proteins after treatment with the synergetic combination (100 µg mL− 1 AP-1 and 0.29 µg mL−1 DOX) as selected by volcano plot compared to the vehicle control with the FC threshold (x) 1.3 and t-test threshold (y) 0.05. The fold changes and p values are log-transformed, and the further the FC values are from the (0,0), the more significant the feature is.
Figure 8
Figure 8
(A) Overview of quality control metrics of LC-MS/MS shotgun proteomic study of the MCF7 cells after treatment with AP-1, DOX and their synergistic combination (PDOX). (B) Venn diagrams of the overlapped identified proteins in the differently treated groups.
Figure 9
Figure 9
(A) Enriched pathways using g:Profiler, (B) STRING network of the differentially expressed proteins in the synergistic combination-treated MCF7 cells and (C) Volcano plot of 0.015 p-value and absolute 1.7 FC threshold among identified proteins in the synergistic combination-treated cells with selected proteins expression summary (PDOX = synergistic combination of AP-1 and DOX). BRCA1-A complex and BRCT domain associated proteins in red and green, respectively, in STRING network. WP; Wikipathways, REAC; Reactome.
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