| Literature DB >> 31366901 |
Luigi Minafra1, Nunziatina Porcino1, Valentina Bravatà2, Daniela Gaglio1,3, Marcella Bonanomi3, Erika Amore4, Francesco Paolo Cammarata1, Giorgio Russo1, Carmelo Militello1, Gaetano Savoca1, Margherita Baglio1, Boris Abbate5, Giuseppina Iacoviello5, Giovanna Evangelista6, Maria Carla Gilardi1,7, Maria Luisa Bondì4, Giusi Irma Forte1.
Abstract
In breast cancer (BC) care, radiotherapy is considered an efficient treatment, prescribed both for controlling localized tumors or as a therapeutic option in case of inoperable, incompletely resected or recurrent tumors. However, approximately 90% of BC-related deaths are due to the metastatic tumor progression. Then, it is strongly desirable to improve tumor radiosensitivity using molecules with synergistic action. The main aim of this study is to develop curcumin-loaded solid nanoparticles (Cur-SLN) in order to increase curcumin bioavailability and to evaluate their radiosensitizing ability in comparison to free curcumin (free-Cur), by using an in vitro approach on BC cell lines. In addition, transcriptomic and metabolomic profiles, induced by Cur-SLN treatments, highlighted networks involved in this radiosensitization ability. The non tumorigenic MCF10A and the tumorigenic MCF7 and MDA-MB-231 BC cell lines were used. Curcumin-loaded solid nanoparticles were prepared using ethanolic precipitation and the loading capacity was evaluated by UV spectrophotometer analysis. Cell survival after treatments was evaluated by clonogenic assay. Dose-response curves were generated testing three concentrations of free-Cur and Cur-SLN in combination with increasing doses of IR (2-9 Gy). IC50 value and Dose Modifying Factor (DMF) was measured to quantify the sensitivity to curcumin and to combined treatments. A multi-"omic" approach was used to explain the Cur-SLN radiosensitizer effect by microarray and metobolomic analysis. We have shown the efficacy of the Cur-SLN formulation as radiosensitizer on three BC cell lines. The DMFs values, calculated at the isoeffect of SF = 50%, showed that the Luminal A MCF7 resulted sensitive to the combined treatments using increasing concentration of vehicled curcumin Cur-SLN (DMF: 1,78 with 10 µM Cur-SLN.) Instead, triple negative MDA-MB-231 cells were more sensitive to free-Cur, although these cells also receive a radiosensitization effect by combination with Cur-SLN (DMF: 1.38 with 10 µM Cur-SLN). The Cur-SLN radiosensitizing function, evaluated by transcriptomic and metabolomic approach, revealed anti-oxidant and anti-tumor effects. Curcumin loaded- SLN can be suggested in future preclinical and clinical studies to test its concomitant use during radiotherapy treatments with the double implications of being a radiosensitizing molecule against cancer cells, with a protective role against IR side effects.Entities:
Mesh:
Substances:
Year: 2019 PMID: 31366901 PMCID: PMC6668411 DOI: 10.1038/s41598-019-47553-2
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Size, PDI, and ζ-potential of empty and curcumin loaded SLNs in bidistilled water.
| Systems | Z-average (nm) | PDI | ζ-potential (mV) ± SD |
|---|---|---|---|
| empty SLNs | 159.0 | 0.275 | +44.9 ± 7.5 |
| Curcumin SLNs | 302.5 | 0.544 | +41.4 ± 4.6 |
Table 1 shows that the curcumin presence inside the nanoparticle system caused an increase of the mean size (from 159 nm to 302 nm) and polidispersity index (PDI). The zeta potential values, instead, were similar in both the systems considered and they ensured an adequate repulsion of particles.
Dose Modifying factors (DMF) for increasing concentrations of free-Cur and Cur-SLN.
| MCF10A | MCF7 | MDA-MB-231 | ||
|---|---|---|---|---|
| DMF50_Photons/Photons + free-Cur | 2,5 µM | 1,29 | 0,99 | 0,93 |
| 5 µM | 1,44 | 0,92 | 1,08 | |
| 10 µM | 2,28 | 1,68 | 1,72 | |
| DMF50_Photons/Photons + Cur-SLN | 2,5 µM | 1,65 | 1,06 | 0,81 |
| 5 µM | 1,65 | 1,58 | 1,03 | |
| 10 µM | 2,76 | 1,78 | 1,31 | |
Table 2 displays the DMFs, arbitrarily calculated at the SF of 50%, by means of a polynomial fitting from the dose response curves.
Figure 1Dose-Response curves of MCF10A. Dose-Response curves of MCF10A cells subjected to increasing doses of IR with or without 2.5 µM of free-Cur and Cur-SLN.
Figure 3Dose-Response curves of MDA-MB-231. Dose-Response curves of MDA-MB-231 BC cells subjected to increasing doses of IR with or without 2.5 µM of free-Cur and Cur-SLN.
Figure 4Determination of intracellular ROS production. Oxidative stress quantification performed measuring intracellular ROS levels with the DCFH-DA molecular probe in the MCF10A, MDA-MB-231 and MCF7 cell lines 24 hrs after treatments with 2 Gy, 2 Gy + Cur-SLN, Cur-SLN. The significance level respect to the control sample was set to *P < 0.05.
DAVID pathway analysis of the top pathways and related genes, deregulated after Cur-SLN treatment in MCF10 cell line.
| Pathway analysis of GEP induced by SLNB-Cur in MCF10 non tumorigenic cell line | |||||
|---|---|---|---|---|---|
| Term | Genes count | % | P value | Genes | |
| 1 | Lysine degradation | 7 | 0.0071 | 0.0075125 | KMT2C, WHSC1L1, ASH1L, WHSC1, SETD2, NSD1, SUV39H2 |
| 2 | Transcriptional misregulation in cancer | 12 | 0.0122 | 0.0255476 | NFKBIZ, CCR7, RXRA, TP53, CDK9, FOXO1, MDM2, MLLT1, WHSC1, ZBTB16, JMJD1C, RUNX2 |
DAVID pathway analysis of the top-5 pathways and related genes, deregulated after Cur-SLN treatment in MDA-MB-231 BC cell line.
| Pathway analysis of GEP induced by SLNB-Cur in MDA-MB-231 BC cell line | |||||
|---|---|---|---|---|---|
| Term | Genes count | % | P value | Genes | |
| 1 | FoxO signaling pathway | 27 | 0.0094 | 0.0006 | ATG12, FOXO1, TGFB2, IGF1R, PRMT1, FBXO25, CAT, EGF, AGAP2, PIK3R1, EGFR, GABARAPL2, IL6, PRKAB2, TGFBR2, SKP2, HOMER2, ATM, CDK2, BCL2L11, TNFSF10, PLK4, MAPK13, MAPK14, FBXO32, GADD45B, GADD45A |
| 2 | Cell cycle | 24 | 0.0084 | 0.0021 | ANAPC1, E2F2, E2F5, RBL1, SKP2, TTK, PRKDC, CDC20, WEE1, CDK2, ATM, TGFB2, MAD2L1, BUB1, TFDP2, YWHAQ, BUB1B, ANAPC7, ABL1, GADD45B, GADD45A, CCNA2, BUB3, TFDP1 |
| 3 | TGF-beta signaling pathway | 18 | 0.0063 | 0.0030 | SMAD9, LTBP1, ROCK1, E2F5, FST, TGFBR2, RBL1, RPS6KB2, TGFB2, ID2, ID1, ZFYVE16, SMURF2, SMURF1, BAMBI, BMPR1A, BMP6, TFDP1 |
| 4 | TNF signaling pathway | 19 | 0.0066 | 0.0152 | TRAF1, ICAM1, IL6, CSF1, CREB1, CXCL2, EDN1, NFKBIA, CREB5, CX3CL1, MMP14, CXCL10, RPS6KA5, BAG4, ATF4, MAPK13, MAPK14, MAP2K6, PIK3R1 |
| 5 | Phosphatidylinositol signaling system | 17 | 0.0059 | 0.0299 | PRKCA, INPP1, PIK3C2A, PIK3C2B, SYNJ1, ITPKB, PI4K2B, PIP5K1A, DGKA, DGKE, PLCD4, INPP5E, MTMR8, INPP5D, PLCB1, INPP5B, PIK3R1 |
DAVID pathway analysis of the top-5 pathways and related genes, deregulated after Cur-SLN treatment in MCF7 BC cell line.
| Pathway analysis of GEP induced by SLNB-Cur in MCF7 BC cell line | |||||
|---|---|---|---|---|---|
| Term | Genes count | % | P value | Genes | |
| 1 | Cytokine-cytokine receptor interaction | 21 | 0.0145 | 0.0257 | TNFRSF6B, TNF, OSMR, TNFRSF25, CSF1, LIFR, CD70, CX3CL1, CCL28, IL11RA, TGFB2, LIF, AMH, IL17A, IL20RB, CCR3, IL2RG, LTB, LTA, IL3RA, BMPR1A |
| 2 | Apoptosis | 9 | 0.0062 | 0.0186 | TNF, BAX, PIK3CD, CASP8, TP53, IL3RA, PIK3R1, AKT2, PIK3R2 |
| 3 | Platelet activation | 14 | 0.0097 | 0.0246 | F2RL3, ADCY2, PIK3CD, ITGA2, APBB1IP, COL5A2, JMJD7-PLA2G4B, P2RX1, PRKACA, COL1A1, COL11A2, PIK3R1, PIK3R2, AKT2 |
| 4 | Tyrosine metabolism | 6 | 0.0041 | 0.0397 | TYR, PNMT, MAOA, AOX1, HPD, AOC3 |
| 5 | Glucagon signaling pathway | 11 | 0.0076 | 0.0428 | LDHB, CPT1B, ADCY2, PYGM, GCK, PPP3R1, PRKACA, ACACB, PPARGC1A, GCGR, AKT2 |
Figure 5Venn diagrams of unique and shared differentially expressed genes by breast cell lines after combined treatments with IR/2,5 µM Cur-SLN. (A) A 51-gene signature of shared deregulate genes after Cur-SLN treatment was selected among MCF10A, MCF7 and MDA-MB-231 breast cell lines. The top-GO Biological processes were also displayed. (B) A 263-gene signature of common deregulated genes was selected for MCF7 and MDA-MB-231 BC cell lines and the top-GO biological processes regulated by these genes, were also reported in the figure.
Figure 6Comparative analysis of MCF10A, MCF7 and MDA-MB-231 by Hierarchical Clustering and untargeted enrichment plots. (A) Comparative analysis of MCF10A, MCF7 and MDA-MB-231 by Hierarchical Clustering. (B) Untargeted enrichment plot displaying increased metabolites levels in MCF7 and MDA-MB-231 under 2 Gy and 2 Gy/Cur-SLN treatments, respect to MCF10A under the same condition. (C) Untargeted enrichment plot displaying decreased metabolites levels in MCF7 and MDA-MB-231 under 2 Gy and 2 Gy/Cur-SLN treatments, respect to MCF10A under the same condition.
Figure 7Hierarchical Clustering analysis and untargeted enrichment plots of MCF10A, MCF7 and MDA-MB-231 exposed to 2 Gy vs 2 Gy/Cur-SLN treatments. (A) Hierarchical Clustering analysis and untargeted enrichment plots of MCF10A, exposed to 2 Gy vs 2 Gy/Cur-SLN treatments. (B) Hierarchical Clustering analysis and untargeted enrichment plots of MCF7, exposed to 2 Gy vs 2 Gy/Cur-SLN treatments. (C) Hierarchical Clustering analysis and untargeted enrichment plots of MDA-MB-231, exposed to 2 Gy vs 2 Gy/Cur-SLN treatments.