Citrinin Provoke DNA Damage and Cell-Cycle Arrest Related to Chk2 and FANCD2 Checkpoint Proteins in Hepatocellular and Adenocarcinoma Cell Lines.
Darija Stupin PolančecSonja HomarDaniela JakšićNevenka KopjarŠegvić Klarić MajaSanja DabelićPublished in: Toxins (2024)
Citrinin (CIT), a polyketide mycotoxin produced by Penicillium , Aspergillus , and Monascus species, is a contaminant that has been found in various food commodities and was also detected in house dust. Several studies showed that CIT can impair the kidney, liver, heart, immune, and reproductive systems in animals by mechanisms so far not completely elucidated. In this study, we investigated the CIT mode of action on two human tumor cell lines, HepG2 (hepatocellular carcinoma) and A549 (lung adenocarcinoma). Cytotoxic concentrations were determined using an MTT proliferation assay. The genotoxic effect of sub-IC 50 concentrations was investigated using the alkaline comet assay and the impact on the cell cycle using flow cytometry. Additionally, the CIT effect on the total amount and phosphorylation of two cell-cycle-checkpoint proteins, the serine/threonine kinase Chk2 and Fanconi anemia (FA) group D2 (FANCD2), was determined by the cell-based ELISA. The data were analyzed using GraphPad Prism statistical software. The CIT IC 50 for HepG2 was 107.3 µM, and for A549, it was >250 µM. The results showed that sensitivity to CIT is cell-type dependent and that CIT in sub-IC 50 and near IC 50 induces significant DNA damage and cell-cycle arrest in the G2/M phase, which is related to the increase in total and phosphorylated Chk2 and FANCD2 checkpoint proteins in HepG2 and A549 cells.
Keyphrases
- cell cycle
- cell cycle arrest
- dna damage
- cell death
- cell proliferation
- pi k akt
- flow cytometry
- protein kinase
- oxidative stress
- signaling pathway
- dna repair
- high throughput
- dna damage response
- induced apoptosis
- stem cells
- single cell
- chronic kidney disease
- squamous cell carcinoma
- endothelial cells
- radiation therapy
- cell therapy
- machine learning
- atomic force microscopy
- high resolution
- atrial fibrillation
- polycyclic aromatic hydrocarbons
- climate change
- locally advanced
- big data
- drinking water