Elucidation of GPR55-Associated Signaling behind THC and LPI Reducing Effects on Ki67-Immunoreactive Nuclei in Patient-Derived Glioblastoma Cells.
Marc Richard KolbeTim HohmannUrszula HohmannErik MarondeRalph GolbikJulian PrellJörg IllertChristian StraussFaramarz DehghaniPublished in: Cells (2023)
GPR55 is involved in many physiological and pathological processes. In cancer, GPR55 has been described to show accelerating and decelerating effects in tumor progression resulting from distinct intracellular signaling pathways. GPR55 becomes activated by LPI and various plant-derived, endogenous, and synthetic cannabinoids. Cannabinoids such as THC exerted antitumor effects by inhibiting tumor cell proliferation or inducing apoptosis. Besides its effects through CB 1 and CB 2 receptors, THC modulates cellular responses among others via GPR55. Previously, we reported a reduction in Ki67-immunoreactive nuclei of human glioblastoma cells after GPR55 activation in general by THC and in particular by LPI. In the present study, we investigated intracellular mechanisms leading to an altered number of Ki67 + nuclei after stimulation of GPR55 by LPI and THC. Pharmacological analyses revealed a strongly involved PLC-IP3 signaling and cell-type-specific differences in Gα-, Gβγ-, RhoA-ROCK, and calcineurin signaling. Furthermore, immunochemical visualization of the calcineurin-dependent transcription factor NFAT revealed an unchanged subcellular localization after THC or LPI treatment. The data underline the cell-type-specific diversity of GPR55-associated signaling pathways in coupling to intracellular G proteins. Furthermore, this diversity might determine the outcome and the individual responsiveness of tumor cells to GPR55 stimulation by cannabin oids.
Keyphrases
- fatty acid
- induced apoptosis
- signaling pathway
- cell cycle arrest
- cell proliferation
- transcription factor
- endoplasmic reticulum stress
- oxidative stress
- neoadjuvant chemotherapy
- cell death
- squamous cell carcinoma
- epithelial mesenchymal transition
- reactive oxygen species
- machine learning
- electronic health record
- high resolution
- big data
- immune response
- cell cycle
- toll like receptor
- squamous cell