Lichen Secondary Metabolites Inhibit the Wnt/β-Catenin Pathway in Glioblastoma Cells and Improve the Anticancer Effects of Temozolomide.
Aleksandra Majchrzak-CelińskaRobert KleszczElżbieta Studzińska-SrokaAgnieszka ŁukaszykAnna SzoszkiewiczEwelina StelcerKarol JopekMarcin RucinskiJudyta Cielecka-PiontekVioletta Krajka-KuzniakPublished in: Cells (2022)
Lichens are a source of secondary metabolites with significant pharmacological potential. Data regarding their possible application in glioblastoma (GBM) treatment are, however, scarce. The study aimed at analyzing the mechanism of action of six lichen secondary metabolites: atranorin, caperatic acid, physodic acid, squamatic acid, salazinic acid, and lecanoric acid using two- and three-dimensional GBM cell line models. The parallel artificial membrane permeation assay was used to predict the blood-brain barrier penetration ability of the tested compounds. Their cytotoxicity was analyzed using the MTT test on A-172, T98G, and U-138 MG cells. Flow cytometry was applied to the analysis of oxidative stress, cell cycle distribution, and apoptosis, whereas qPCR and microarrays detected the induced transcriptomic changes. Our data confirm the ability of lichen secondary metabolites to cross the blood-brain barrier and exert cytotoxicity against GBM cells. Moreover, the compounds generated oxidative stress, interfered with the cell cycle, and induced apoptosis in T98G cells. They also inhibited the Wnt/β-catenin pathway, and this effect was even stronger in case of a co-treatment with temozolomide. Transcriptomic changes in cancer related genes induced by caperatic acid and temozolomide were the most pronounced. Lichen secondary metabolites, caperatic acid in particular, should be further analyzed as potential anti-GBM agents.
Keyphrases
- induced apoptosis
- oxidative stress
- endoplasmic reticulum stress
- cell cycle
- cell cycle arrest
- signaling pathway
- ms ms
- diabetic rats
- cell proliferation
- cell death
- stem cells
- flow cytometry
- risk assessment
- ischemia reperfusion injury
- single cell
- newly diagnosed
- deep learning
- climate change
- machine learning
- combination therapy
- artificial intelligence
- rna seq
- data analysis