Disrupting calcium homeostasis and glycometabolism in engineered lipid-based pharmaceuticals propel cancer immunogenic death

Abstract

Homeostasis and energy and substance metabolism reprogramming shape various tumor microenvironment to sustain cancer stemness, self-plasticity and treatment resistance. Aiming at them, a lipid-based pharmaceutical loaded with CaO₂ and glucose oxidase (GOx) (LipoCaO₂/GOx, LCG) has been obtained to disrupt calcium homeostasis and interfere with glycometabolism. The loaded GOx can decompose glucose into H₂O₂ and gluconic acid, thus competing with anaerobic glycolysis to hamper lactic acid (LA) secretion. The obtained gluconic acid further deprives CaO₂ to produce H₂O₂ and release Ca²⁺, disrupting Ca²⁺ homeostasis, which synergizes with GOx-mediated glycometabolism interference to deplete glutathione (GSH) and yield reactive oxygen species (ROS). Systematical experiments reveal that these sequential multifaceted events unlocked by Ca²⁺ homeostasis disruption and glycometabolism interference, ROS production and LA inhibition, successfully enhance cancer immunogenic deaths of breast cancer cells, hamper regulatory T cells (Tregs) infiltration and promote CD8⁺ T recruitment, which receives a considerably-inhibited outcome against breast cancer progression. Collectively, this calcium homeostasis disruption glycometabolism interference strategy effectively combines ion interference therapy with starvation therapy to eventually evoke an effective anti-tumor immune environment, which represents in the field of biomedical research.

Keywords: Calcium homeostasis disruption; Cancer plasticity; Engineered lipids; Glycometabolism interference; Immunogenic cell death; Lactic acid; Oxidative stress; Starvation therapy.

Graphical abstract

Scheme 1

Schematic diagram illustrating LCG synthesis and how the combination of calcium homeostasis and glycometabolism in LCG pharmaceuticals unlock multifaceted events hamper Tregs infiltration and function, encourage CD8⁺ T recruitment and brought about cancer immunogenic death to repress breast cancer.

Figure 1

Construction and characterization of LCG. (A) Schematic diagram of LCG synthesis, scale bar = 50 nm; (B) TEM images of CaO₂ nanoparticles, scale bar = 20 nm; (C) TEM images of LCG nanoparticles; (D, E) Particle size distribution (D) and zeta potential (E) of different nanoparticles. (F–H) XRD (F), XPS (G) and EDS (H) spectra of LCG; (I) UV absorption spectra of different nanoparticles; (J) Ca²⁺ release from LCG at different pH conditions. Data are expressed as mean ± SD, n = 3.

Figure 2

Multifaceted function tests in LCG including GOx enzymically-catalytic activity, H₂O₂ production and GSH depletion. (A) pH changes in GOX-, LG- or LCG-involved glucose solution with different glucose concentrations; (B) pH changes in GOX-, LG- or LCG-involved glucose solution at different periods. (C) Absorbance changes in H₂O₂ production by GOX, LG and LCG in glucose solution at different concentrations; and (D) Absorbance changes in H₂O₂ production by GOX, LG and LCG in glucose solution over time. (E) Absorbance changes in GSH content in solution after co-incubation of different materials with GSH (10 mmol/L); (F) Absorbance changes in GSH content in solution after co-incubation of different volumes of LCG with GSH (10 mmol/L). Data are expressed as mean ± SD (n = 3).

Figure 3

Cellular-level multifaceted events exploration and its role in promoting ROS production. (A) Fluorescence images of cells after different treatments (red fluorescence signal from LCG-DID, cells stained by DAPI and LysoTraker Green), scale bar = 5 μm; (B) FCM patterns of 4T1 cells after incubation with LCG-DID for different periods (0 and 4 h), both of which were used to assess the cellular phagocytosis of LCG-DID by 4T1 cells. (C) The concentration of Ca²⁺ in the culture medium following various cell treatments. (D–F) Intracellular contents of H₂O₂ (D), GSH (E) and LA (F) in 4T1 cells after co-incubation with different nanomaterials, scale bar = 20 μm; (G) CLSM images of 4T1 cells stained with DCFH-DA after co-incubation with different nanoparticles; and (H) FCM analysis of 4T1 cells stained with DCFH-DA after co-incubation with different nanoparticles, both of which could detect the intracellular ROS content after different treatments. Data are expressed as mean ± SD (n = 3); ∗P＜0.05, ∗∗P＜0.01, ∗∗∗P＜0.001, ∗∗∗∗P＜0.0001.

Figure 4

In vitro anti-tumor evaluations and cancer immunogenic death test. (A) Relative viability of 4T1 cells after incubations with different samples under varied concentrations for 24 h; (B) Relative viability of 4T1 cells after incubations with different samples under varied concentrations for different periods (n = 6); (C) CLSM images of PI-stained 4T1 cells in different experimental subgroups that experienced corresponding treatments for 4, 8 and 12 h, scale bar = 100 μm; (D) CLSM images of 4T1 cells co-stained with Annexin V-mCHerrry and SYTOX Green in different groups, scale bar = 20 μm; (E) FCM patterns of 4T1 cells stained with PI and Annexin V-FITC after co-incubation with different samples; (F) CLSM images of Fluo-4 AM-stained 4T1 cells after co-incubation with different nanoparticles for tracing intracellular Ca²⁺ levels, scale bar = 20 μm; and (G) corresponding MFI values. (H) Intracellular ATP content detection after co-incubation of 4T1 with different nanoparticles; (I–N) Immunofluorescence images (I–K) and corresponding MFI values (L–N) of CRT (I,L), HSP 70 (J, M) and HMGB1 (K, N), scale bar = 20 μm; (O) WB images of intracellular CRT and HMGB1 after different treatments. Data are expressed as mean ± SD (n = 3), ‘ns’ no significance, ∗∗P < 0.01, ∗∗∗P < 0.001 and ∗∗∗∗P < 0.0001.

Figure 5

Anti-tumor assay and mechanism exploration. (A) Schematic of imaging in small animals; (B) Fluorescence biodistribution imaging of LCG in mice under different time points post-injection of LCG-DID and free DID (Control); (C) Fluorescence images of vivo organ harvested from LCG-treated mice after 6 and 48 h post-injection of LCG-DID. (D) Schematic diagram of tumors treated with different nanomaterials; (E) Time-dependent changes in the average body weight of mice in different treatment groups; (F) Time-dependent changes in the average tumor volume of mice in different treatment groups; (G) Time-dependent changes in the average tumor body weight in different treatment groups; (H) Digital images of excised tumors from mice in different treatment groups at the end of experiment period; (I) Photos of 4T1 tumor-bearing mice before and after different treatments in different treatment groups; (J) Survival rate of mice treated with various formulations in different treatment groups; (K) Immunofluorescence images of tumor slices co-stained with FOXP3 and DAPI after different treatments for assaying Tregs infiltration, scale bar = 50 μm; (L) Immunofluorescence images of tumor slices co-stained with CD4, CD8 and DAPI after different treatments for assaying T-cell infiltration, scale bar = 50 μm; (M) ROS levels in tumor sections stained with dihydroethidium (DHE), scale bar = 50 μm; (N) Optical photos of tumor slices after different immunochemical staining in different treatment groups for realizing H&E, KI67 and TUNEL expression analysis, scale bar = 50 μm. Data are expressed as mean ± SD (n = 5); ∗P＜0.05 and ∗∗P＜0.01.

Figure 6

Induces ICD and activates immunity. (A–C) Fluorescence micrographs of 4T1 tumor tissue stained with CRT and HMGB1 (A), scale bar = 50 μm, along with the corresponding MFI (B,C); (D) Intratumoral ATP levels in tumor after different treatments (n = 4); (E–G) Secretion levels of IFN-γ (E), TNF-α (F), and IL-6 (G) in mouse serum based on Elisa kits (n = 4); (H,I) Representative FCM patterns (H) and quantitative analysis (I) of matured DCs (CD80⁺CD86⁺) in tumor tissues after various treatments; (J,K) Representative FCM patterns (J) and quantitative analysis (K) of CD4⁺ and CD8⁺ T cells in tumor tissues following various treatments (n = 3); (L, M) Representative FCM patterns (L) and quantitative analysis (M) of Tregs ((FOXP3⁺CD4⁺) in tumor tissues following various treatments (n = 3). Data are expressed as mean ± SD; ∗P＜0.05, ∗∗P＜0.01 and ∗∗∗P＜0.001.