Mitochondrial Protein Density, Biomass, and Bioenergetics as Predictors for the Efficacy of Glioma Treatments
Sharapova G. Sabirova S. Gomzikova M. Brichkina A. Barlev N.A. Kalacheva N.V. Rizvanov A. Markov N. Simon H.-U.
July 2024Multidisciplinary Digital Publishing Institute (MDPI)
International Journal of Molecular Sciences
2024#25Issue 13
The metabolism of glioma cells exhibits significant heterogeneity and is partially responsible for treatment outcomes. Given this variability, we hypothesized that the effectiveness of treatments targeting various metabolic pathways depends on the bioenergetic profiles and mitochondrial status of glioma cells. To this end, we analyzed mitochondrial biomass, mitochondrial protein density, oxidative phosphorylation (OXPHOS), and glycolysis in a panel of eight glioma cell lines. Our findings revealed considerable variability: mitochondrial biomass varied by up to 3.2-fold, the density of mitochondrial proteins by up to 2.1-fold, and OXPHOS levels by up to 7.3-fold across the cell lines. Subsequently, we stratified glioma cell lines based on their mitochondrial status, OXPHOS, and bioenergetic fitness. Following this stratification, we utilized 16 compounds targeting key bioenergetic, mitochondrial, and related pathways to analyze the associations between induced changes in cell numbers, proliferation, and apoptosis with respect to their steady-state mitochondrial and bioenergetic metrics. Remarkably, a significant fraction of the treatments showed strong correlations with mitochondrial biomass and the density of mitochondrial proteins, suggesting that mitochondrial status may reflect glioma cell sensitivity to specific treatments. Overall, our results indicate that mitochondrial status and bioenergetics are linked to the efficacy of treatments targeting metabolic pathways in glioma.
bioenergetics , cancer treatment , drug response , glioblastoma , glioma , metabolic reprogramming , metabolism , mitochondria , mitochondria-targeting , OXPHOS
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Laboratory of Molecular Immunology, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russian Federation
OpenLab Gene and Cell Technology, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russian Federation
Laboratory of Intercellular Communication, Kazan Federal University, Kazan, 420111, Russian Federation
Institute of Systems Immunology, Center for Tumor Biology and Immunology, Philipps University of Marburg, Marburg, 35043, Germany
Gene Expression Program, Institute of Cytology RAS, Saint-Petersburg, 194064, Russian Federation
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
Division of Medical and Biological Sciences, Tatarstan Academy of Sciences, Kazan, 420111, Russian Federation
I.K. Akhunbaev Kyrgyz State Medical Academy, Bishkek, 720020, Kyrgyzstan
Institute of Pharmacology, University of Bern, Bern, 3010, Switzerland
Institute of Biochemistry, Brandenburg Medical School, Neuruppin, 16816, Germany
Laboratory of Molecular Immunology
OpenLab Gene and Cell Technology
Laboratory of Intercellular Communication
Institute of Systems Immunology
Gene Expression Program
Department of Biomedical Sciences
Division of Medical and Biological Sciences
I.K. Akhunbaev Kyrgyz State Medical Academy
Institute of Pharmacology
Institute of Biochemistry
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