Ferroptosis-inducing agents enhance TRAIL-induced apoptosis through upregulation of death receptor 5.
Young-Sun LeeDae-Hee LeeSo Yeon JeongSeong Hye ParkSang Cheul OhYong Seok ParkJian YuHaroon A ChoudryDavid L BartlettYong J LeePublished in: Journal of cellular biochemistry (2018)
Ferroptosis is considered genetically and biochemically distinct from other forms of cell death. In this study, we examined whether ferroptosis shares cell death pathways with other types of cell death. When human colon cancer HCT116, CX-1, and LS174T cells were treated with ferroptotic agents such as sorafenib (SRF), erastin, and artesunate, data from immunoblot assay showed that ferroptotic agents induced endoplasmic reticulum (ER) stress and the ER stress response-mediated expression of death receptor 5 (DR5), but not death receptor 4. An increase in the level of DR5, which is activated by binding to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and initiates apoptosis, was probably responsible for synergistic apoptosis when cells were treated with ferroptotic agent in combination with TRAIL. This collateral effect was suppressed in C/EBP (CCAAT-enhancer-binding protein)-homologous protein (CHOP)-deficient mouse embryonic fibroblasts or DR5 knockdown HCT116 cells, but not in p53-deficient HCT116 cells. The results from in vitro studies suggest the involvement of the p53-independent CHOP/DR5 axis in the synergistic apoptosis during the combinatorial treatment of ferroptotic agent and TRAIL. The synergistic apoptosis and regression of tumor growth were also observed in xenograft tumors when SRF and TRAIL were administered to tumor-bearing mice.
Keyphrases
- cell cycle arrest
- cell death
- binding protein
- induced apoptosis
- endoplasmic reticulum stress
- endoplasmic reticulum
- pi k akt
- poor prognosis
- signaling pathway
- oxidative stress
- endothelial cells
- cancer therapy
- editorial comment
- cell proliferation
- diffuse large b cell lymphoma
- rheumatoid arthritis
- insulin resistance
- transcription factor
- metabolic syndrome
- big data
- high throughput
- adipose tissue
- machine learning
- long non coding rna
- combination therapy
- artificial intelligence
- pluripotent stem cells
- replacement therapy
- protein protein