Glioblastoma Sensitization to Therapeutic Effects by Glutamine Deprivation Depends on Cellular Phenotype and Metabolism

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Abstract

Glutamine plays an important role in tumor metabolism. It is known that the core region of the solid tumors is deprived of glutamine which affects tumor growth and spread. Here we investigated the effect of glutamine deprivation on cellular metabolism and sensitivity of human glioblastoma cells U87MG and T98G to drugs of various origin: alkylating cytostatic agent temozolomide; cytokine TRAIL DR5-B – agonist of the DR5 receptor; and GMX1778 – a targeted inhibitor of the enzyme nicotinamide phosphoribosyltransferase (NAMPT), limiting NAD biosynthesis. Bioinformatics analysis of the cell transcriptomes showed that U87MG cells have a more differentiated phenotype than T98G, and also differ in the expression profile of genes associated with glutamine metabolism. Upon glutamine deprivation, the growth rate of U87MG and T98G cells decreased. Analysis of cellular metabolism by FLIM microscopy of NADH as well as assessment of the lactate content in the medium showed that glutamine deprivation shifted the metabolic status of U87MG cells towards glycolysis. This was accompanied by an increase in the expression of the stemness marker CD133, which collectively may indicate the de-differentiation of these cells. At the same time, we observed an increase in both the expression of DR5 receptor and the sensitivity of U87MG cells to DR5-B. On the contrary, glutamine deprivation of T98G cells induced a metabolic shift towards oxidative phosphorylation, a decrease in DR5 expression and resistance to DR5-B. The effects of NAMPT inhibition also differed across two cell lines and were opposite to those of DR5-B: upon glutamine deprivation, U87MG cells acquired resistance, while T98G cells were sensitized to GMX1778. Thus, phenotypic and metabolic differences between two human glioblastoma cell lines resulted in divergent metabolic changes and contrasting responses to different targeted drugs during glutamine deprivation. These data should be considered when developing treatment strategies for glioblastoma via drug deprivation of amino acids, as well as when exploring novel therapeutic targets.

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About the authors

A. A. Isakova

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences; Lomonosov Moscow State University

Email: anneyagolovich@gmail.com
Russian Federation, 117997, Moscow; 119991, Moscow

I. N. Druzhkova

Privolzhsky Research Medical University

Email: anneyagolovich@gmail.com
Russian Federation, 603081, Nizhny Novgorod

A. M. Mozherov

Privolzhsky Research Medical University

Email: anneyagolovich@gmail.com
Russian Federation, 603081, Nizhny Novgorod

D. V. Mazur

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences

Email: anneyagolovich@gmail.com
Russian Federation, 117997, Moscow

N. V. Antipova

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences

Email: anneyagolovich@gmail.com
Russian Federation, 117997, Moscow

K. S. Krasnov

Institute of Theoretical and Experimental Biophysics of the Russian Academy of Sciences

Email: anneyagolovich@gmail.com
Russian Federation, 142290, Pushchino, Moscow Region

R. S. Fadeev

Institute of Theoretical and Experimental Biophysics of the Russian Academy of Sciences

Email: anneyagolovich@gmail.com
Russian Federation, 142290, Pushchino, Moscow Region

M. E. Gasparian

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences

Email: anneyagolovich@gmail.com
Russian Federation, 117997, Moscow

A. V. Yagolovich

Lomonosov Moscow State University

Author for correspondence.
Email: anneyagolovich@gmail.com
Russian Federation, 119991, Moscow

References

  1. Bergström, J., Fürst, P., Norée, L. O., and Vinnars, E. (1974) Intracellular free amino acid concentration in human muscle tissue, J. Appl. Physiol., 36, 693-697, https://doi.org/10.1152/jappl.1974.36.6.693.
  2. Yang, L., Venneti, S., and Nagrath, D. (2017) Glutaminolysis: a hallmark of cancer metabolism, Annu. Rev. Biomed. Engin., 19, 163-194, https://doi.org/10.1146/annurev-bioeng-071516-044546.
  3. DeBerardinis, R. J., Mancuso, A., Daikhin, E., Nissim, I., Yudkoff, M., et al. (2007) Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis, Proc. Natl. Acad. Sci. USA, 104, 19345-19350, https://doi.org/10.1073/pnas.0709747104.
  4. Shanware, N. P., Bray, K., Eng, C. H., Wang, F., Follettie, M., et al. (2014) Glutamine deprivation stimulates mTOR-JNK-dependent chemokine secretion, Nat. Commun., 5, 4900, https://doi.org/10.1038/ncomms5900.
  5. Yuneva, M., Zamboni, N., Oefner, P., Sachidanandam, R., and Lazebnik, Y. (2007) Deficiency in glutamine but not glucose induces MYC-dependent apoptosis in human cells, J. Cell Biol., 178, 93-105, https://doi.org/10.1083/jcb.200703099.
  6. Pan, M., Reid, M. A., Lowman, X. H., Kulkarni, R. P., Tran, T. Q., et al. (2016) Regional glutamine deficiency in tumours promotes dedifferentiation through inhibition of histone demethylation, Nat. Cell Biol., 18, 1090-1101, https://doi.org/10.1038/ncb3410.
  7. Márquez, J., Alonso, F. J., Matés, J. M., Segura, J. A., Martín-Rufián, M., et al. (2017) Glutamine addiction in gliomas, Neurochem. Res., 42, 1735-1746, https://doi.org/10.1007/s11064-017-2212-1.
  8. Yin, H., Liu, Y., Dong, Q., Wang, H., Yan, Y., et al. (2024) The mechanism of extracellular CypB promotes glioblastoma adaptation to glutamine deprivation microenvironment, Cancer Lett., 216862, https://doi.org/10.1016/ j.canlet.2024.216862.
  9. Jia, J. L., Alshamsan, B., and Ng, T. L. (2023) Temozolomide chronotherapy in glioma: a systematic review, Curr. Oncol., 30, 1893-1902, https://doi.org/10.3390/curroncol30020147.
  10. Kuijlen, J. M., Mooij, J. J. A., Platteel, I., Hoving, E. W., Van Der Graaf, W. T., et al. (2006) TRAIL-receptor expression is an independent prognostic factor for survival in patients with a primary glioblastoma multiforme, J. Neuro Oncol., 78, 161-171, https://doi.org/10.1007/s11060-005-9081-1.
  11. Thang, M., Mellows, C., Mercer-Smith, A., Nguyen, P., and Hingtgen, S. (2023) Current approaches in enhancing TRAIL therapies in glioblastoma, Neuro Oncol. Adv., 5, vdad047, https://doi.org/10.1093/noajnl/vdad047.
  12. Galli, U., Colombo, G., Travelli, C., Tron, G. C., Genazzani, A. A., and Grolla, A. A. (2020) Recent advances in NAMPT inhibitors: a novel immunotherapic strategy, Front. Pharmacol., 11, 656, https://doi.org/10.3389/fphar. 2020.00656.
  13. Fung, M. K. L., and Chan, G. C.-F. (2017) Drug-induced amino acid deprivation as strategy for cancer therapy, J. Hematol. Oncol., 10, 144, https://doi.org/10.1186/s13045-017-0509-9.
  14. Jin, J., Byun, J.-K., Choi, Y.-K., and Park, K.-G. (2023) Targeting glutamine metabolism as a therapeutic strategy for cancer, Exp. Mol. Med., 55, 706-715, https://doi.org/10.1038/s12276-023-00971-9.
  15. Jezierzański, M., Nafalska, N., Stopyra, M., Furgoł, T., Miciak, M., et al. (2024) Temozolomide (TMZ) in the treatment of glioblastoma multiforme – a literature review and clinical outcomes, Curr. Oncol., 31, 3994-4002, https://doi.org/10.3390/curroncol31070296.
  16. Gasparian, M. E., Chernyak, B. V., Dolgikh, D. A., Yagolovich, A. V., Popova, E. N., et al. (2009) Generation of new TRAIL mutants DR5-A and DR5-B with improved selectivity to death receptor 5, Apoptosis, 14, 778-787, https://doi.org/10.1007/s10495-009-0349-3.
  17. Watson, M., Roulston, A., Bélec, L., Billot, X., Marcellus, R., Bédard, D., Bernier, C., Branchaud, S., et al. (2009) The small molecule GMX1778 is a potent inhibitor of NAD+ biosynthesis: strategy for enhanced therapy in nicotinic acid phosphoribosyltransferase 1-deficient tumors, Mol. Cell. Biol., 29, 5872-5888, https://doi.org/10.1128/MCB.00112-09.
  18. Love, M. I., Huber, W., and Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15, 550, https://doi.org/10.1186/s13059-014-0550-8.
  19. Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., and Mesirov, J. P. (2011) Molecular signatures database (MSigDB) 3.0, Bioinformatics, 27, 1739-1740, https://doi.org/10.1093/bioinformatics/btr260.
  20. Fang, Z., Liu, X., and Peltz, G. (2023) GSEApy: a comprehensive package for performing gene set enrichment analysis in Python, Bioinformatics, 39, btac757, https://doi.org/10.1093/bioinformatics/btac757.
  21. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., et al. (2000) Gene Ontology: tool for the unification of biology, Nat. Genet., 25, 25-29, https://doi.org/10.1038/75556.
  22. Kanehisa, M. (2000) KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28, 27-30, https:// doi.org/10.1093/nar/28.1.27.
  23. Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., et al. (2022) The reactome pathway knowledgebase 2022, Nucleic Acids Res., 50, D687-D692, https://doi.org/10.1093/nar/gkab1028.
  24. Martens, M., Ammar, A., Riutta, A., Waagmeester, A., Slenter, D. N., et al. (2021) WikiPathways: connecting communities, Nucleic Acids Res., 49, D613-D621, https://doi.org/10.1093/nar/gkaa1024.
  25. Schaefer, C. F., Anthony, K., Krupa, S., Buchoff, J., Day, M., et al. (2009) PID: the pathway interaction database, Nucleic Acids Res., 37, D674-D679, https://doi.org/10.1093/nar/gkn653.
  26. Xie, X., Lu, J., Kulbokas, E. J., Golub, T. R., Mootha, V., et al. (2005) Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals, Nature, 434, 338-345, https://doi.org/10.1038/nature03441.
  27. Benjamini, Y., and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B (Methodological), 57, 289-300, https://doi.org/10.1111/j.2517-6161. 1995.tb02031.x.
  28. Yagolovich, A. V., Artykov, A. A., Dolgikh, D. A., Kirpichnikov, M. P., and Gasparian, M. E. (2019) A new efficient method for production of recombinant antitumor cytokine TRAIL and its receptor-selective variant DR5-B, Biochemistry (Moscow), 84, 627-636, https://doi.org/10.1134/S0006297919060051.
  29. Suárez-Álvarez, B., Rodriguez, R. M., Calvanese, V., Blanco-Gelaz, M. A., Suhr, S. T., et al. (2010) Epigenetic mechanisms regulate MHC and antigen processing molecules in human embryonic and induced pluripotent stem cells, PLoS One, 5, e10192, https://doi.org/10.1371/journal.pone.0010192.
  30. Vagaska, B., New, S. E. P., Alvarez-Gonzalez, C., D’Acquisto, F., Gomez, S. G., et al. (2016) MHC-class-II are expressed in a subpopulation of human neural stem cells in vitro in an IFNγ-independent fashion and during development, Sci. Rep., 6, 24251, https://doi.org/10.1038/srep24251.
  31. Liu, R., Zeng, L.-W., Li, H.-F., Shi, J.-G., Zhong, B., et al. (2023) PD-1 signaling negatively regulates the common cytokine receptor γ chain via MARCH5-mediated ubiquitination and degradation to suppress anti-tumor immunity, Cell Res., 33, 923-939, https://doi.org/10.1038/s41422-023-00890-4.
  32. Yoo, H. C., Yu, Y. C., Sung, Y., and Han, J. M. (2020) Glutamine reliance in cell metabolism, Exp. Mol. Med., 52, 1496-1516, https://doi.org/10.1038/s12276-020-00504-8.
  33. Yang, S., Hwang, S., Kim, M., Seo, S. B., Lee, J.-H., et al. (2018) Mitochondrial glutamine metabolism via GOT2 supports pancreatic cancer growth through senescence inhibition, Cell Death Dis., 9, 55, https://doi.org/10.1038/s41419-017-0089-1.
  34. Li, Y., Bie, J., Song, C., Liu, M., and Luo, J. (2021) PYCR, a key enzyme in proline metabolism, functions in tumorigenesis, Amino Acids, 53, 1841-1850, https://doi.org/10.1007/s00726-021-03047-y.
  35. Lodder-Gadaczek, J., Becker, I., Gieselmann, V., Wang-Eckhardt, L., and Eckhardt, M. (2011) N-acetylaspartylglutamate synthetase II synthesizes N-acetylaspartylglutamylglutamate, J. Biol. Chem., 286, 16693-16706, https:// doi.org/10.1074/jbc.M111.230136.
  36. Dranoff, G., Elion, G. B., Friedman, H. S., Campbell, G. L., and Bigner, D. D. (1985) Influence of glutamine on the growth of human glioma and medulloblastoma in culture, Cancer Res., 45, 4077-4081.
  37. Lengauer, F., Geisslinger, F., Gabriel, A., Von Schwarzenberg, K., Vollmar, A. M., et al. (2023) A metabolic shift toward glycolysis enables cancer cells to maintain survival upon concomitant glutamine deprivation and V-ATPase inhibition, Front. Nutr., 10, 1124678, https://doi.org/10.3389/fnut.2023.1124678.
  38. Blacker, T. S., and Duchen, M. R. (2016) Investigating mitochondrial redox state using NADH and NADPH autofluorescence, Free Radic. Biol. Med., 100, 53-65, https://doi.org/10.1016/j.freeradbiomed.2016.08.010.
  39. Kolenc, O. I., and Quinn, K. P. (2019) Evaluating cell metabolism through autofluorescence imaging of NAD(P)H and FAD, Antioxid. Redox Signal., 30, 875-889, https://doi.org/10.1089/ars.2017.7451.
  40. Skala, M. C., Riching, K. M., Bird, D. K., Gendron-Fitzpatrick, A., Eickhoff, J., Eliceiri, K. W., Keely, P. J., and Ramanujam, N. (2007) In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia, J. Biomed. Optics, 12, 024014, https:// doi.org/10.1117/1.2717503.
  41. Song, A., Zhao, N., Hilpert, D. C., Perry, C., Baur, J. A., et al. (2024) Visualizing subcellular changes in the NAD(H) pool size versus redox state using fluorescence lifetime imaging microscopy of NADH, Commun. Biol., 7, 428, https://doi.org/10.1038/s42003-024-06123-7.
  42. Kim, J. H., Lee, J., Im, S. S., Kim, B., Kim, E.-Y., et al. (2024) Glutamine-mediated epigenetic regulation of cFLIP underlies resistance to TRAIL in pancreatic cancer, Exp. Mol. Med., 56, 1013-1026, https://doi.org/10.1038/ s12276-024-01231-0.
  43. Li, P., Zhou, C., Xu, L., and Xiao, H. (2013) Hypoxia enhances stemness of cancer stem cells in glioblastoma: an in vitro study, Int. J. Med. Sci., 10, 399-407, https://doi.org/10.7150/ijms.5407.
  44. Larionova, T. D., Bastola, S., Aksinina, T. E., Anufrieva, K. S., Wang, J., et al. (2022) Alternative RNA splicing modulates ribosomal composition and determines the spatial phenotype of glioblastoma cells, Nat. Cell Biol., 24, 1541-1557, https://doi.org/10.1038/s41556-022-00994-w.
  45. Najafi, M., Farhood, B., and Mortezaee, K. (2019) Cancer stem cells (CSCs) in cancer progression and therapy, J. Cell. Physiol., 234, 8381-8395, https://doi.org/10.1002/jcp.27740.
  46. Li, B., Cao, Y., Meng, G., Qian, L., Xu, T., et al. (2019) Targeting glutaminase 1 attenuates stemness properties in hepatocellular carcinoma by increasing reactive oxygen species and suppressing Wnt/beta-catenin pathway, EBioMedicine, 39, 239-254, https://doi.org/10.1016/j.ebiom.2018.11.063.
  47. Li, D., Fu, Z., Chen, R., Zhao, X., Zhou, Y., et al. (2015) Inhibition of glutamine metabolism counteracts pancreatic cancer stem cell features and sensitizes cells to radiotherapy, Oncotarget, 6, 31151-31163, https://doi.org/10.18632/oncotarget.5150.
  48. Prasad, P., Ghosh, S., and Roy, S. S. (2021) Glutamine deficiency promotes stemness and chemoresistance in tumor cells through DRP1-induced mitochondrial fragmentation, Cell. Mol. Life Sci., 78, 4821-4845, https:// doi.org/10.1007/s00018-021-03818-6.
  49. Tardito, S., Oudin, A., Ahmed, S. U., Fack, F., Keunen, O., et al. (2015) Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma, Nat. Cell Biol., 17, 1556-1568, https://doi.org/10.1038/ncb3272.
  50. Wang, L., Han, Y., Gu, Z., Han, M., Hu, C., et al. (2023) Boosting the therapy of glutamine-addiction glioblastoma by combining glutamine metabolism therapy with photo-enhanced chemodynamic therapy, Biomater. Sci., 11, 6252-6266, https://doi.org/10.1039/D3BM00897E.
  51. Wang, Q., Wu, M., Li, H., Rao, X., Ao, L., et al. (2022) Therapeutic targeting of glutamate dehydrogenase 1 that links metabolic reprogramming and Snail-mediated epithelial-mesenchymal transition in drug-resistant lung cancer, Pharmacol. Res., 185, 106490, https://doi.org/10.1016/j.phrs.2022.106490.
  52. Ezoe, S., Matsumura, I., Satoh, Y., Tanaka, H., and Kanakura, Y. (2004) Cell cycle regulation in hematopoietic stem/progenitor cells, Cell Cycle (Georgetown, Tex.), 3, 314-318.
  53. Wei, Y., Chen, Q., Huang, S., Liu, Y., Li, Y., et al. (2022) The interaction between DNMT1 and high-mannose CD133 maintains the slow-cycling state and tumorigenic potential of glioma stem cell, Adv. Sci., 9, 2202216, https:// doi.org/10.1002/advs.202202216.
  54. Ishii, N., Maier, D., Merlo, A., Tada, M., Sawamura, Y., et al. (1999) Frequent co‐alterations of TP53, p16/CDKN2A, p14ARF, PTEN tumor suppressor genes in human glioma cell lines, Brain Pathol., 9, 469-479, https://doi.org/ 10.1111/j.1750-3639.1999.tb00536.x.
  55. Wanet, A., Arnould, T., Najimi, M., and Renard, P. (2015) Connecting mitochondria, metabolism, and stem cell fate, Stem Cells Dev., 24, 1957-1971, https://doi.org/10.1089/scd.2015.0117.
  56. Pattappa, G., Heywood, H. K., De Bruijn, J. D., and Lee, D. A. (2011) The metabolism of human mesenchymal stem cells during proliferation and differentiation, J. Cell. Physiol., 226, 2562-2570, https://doi.org/10.1002/jcp.22605.
  57. Rodimova, S. A., Meleshina, A. V., Kalabusheva, E. P., Dashinimaev, E. B., Reunov, D. G., et al. (2019) Metabolic activity and intracellular pH in induced pluripotent stem cells differentiating in dermal and epidermal directions, Methods Appl. Fluores., 7, 044002, https://doi.org/10.1088/2050-6120/ab3b3d.
  58. Rodimova, S., Mozherov, A., Elagin, V., Karabut, M., Shchechkin, I., et al. (2023) FLIM imaging revealed spontaneous osteogenic differentiation of stem cells on gradient pore size tissue-engineered constructs, Stem Cell Res. Ther., 14, 81, https://doi.org/10.1186/s13287-023-03307-6.
  59. Morelli, M., Lessi, F., Barachini, S., Liotti, R., Montemurro, N., et al. (2022) Metabolic-imaging of human glioblastoma live tumors: a new precision-medicine approach to predict tumor treatment response early, Front. Oncol., 12, 969812, https://doi.org/10.3389/fonc.2022.969812.
  60. Yuzhakova, D. V., Sachkova, D. A., Shirmanova, M. V., Mozherov, A. M., Izosimova, A. V., et al. (2023) Measurement of patient-derived glioblastoma cell response to temozolomide using fluorescence lifetime imaging of NAD(P)H, Pharmaceuticals, 16, 796, https://doi.org/10.3390/ph16060796.
  61. Kim, J. H., Lee, K. J., Seo, Y., Kwon, J.-H., Yoon, J. P., et al. (2018) Effects of metformin on colorectal cancer stem cells depend on alterations in glutamine metabolism, Sci. Rep., 8, 409, https://doi.org/10.1038/s41598-017-18762-4.
  62. Valter, K., Chen, L., Kruspig, B., Maximchik, P., Cui, H., et al. (2017) Contrasting effects of glutamine deprivation on apoptosis induced by conventionally used anticancer drugs, Biochim. Biophys. Acta Mol. Cell Res., 1864, 498-506, https://doi.org/10.1016/j.bbamcr.2016.12.016.
  63. Siegmund, D., Lang, I., and Wajant, H. (2017) Cell death-independent activities of the death receptors CD 95, TRAILR 1, and TRAILR 2, FEBS J., 284, 1131-1159, https://doi.org/10.1111/febs.13968.
  64. Galluzzi, L., Vitale, I., Aaronson, S. A., Abrams, J. M., Adam, D., et al. (2018) Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018, Cell Death Differ., 25, 486-541, https:// doi.org/10.1038/s41418-017-0012-4.
  65. Dilshara, M. G., Jeong, J.-W., Prasad Tharanga Jayasooriya, R. G., Neelaka Molagoda, I. M., Lee, S., et al. (2017) Glutamine deprivation sensitizes human breast cancer MDA-MB-231 cells to TRIAL-mediated apoptosis, Biochem. Biophys. Res. Commun., 485, 440-445, https://doi.org/10.1016/j.bbrc.2017.02.059.
  66. Fumarola, C., Zerbini, A., and Guidotti, G. G. (2001) Glutamine deprivation-mediated cell shrinkage induces ligand-independent CD95 receptor signaling and apoptosis, Cell Death Differ., 8, 1004-1013, https://doi.org/10.1038/sj.cdd.4400902.
  67. Mauro-Lizcano, M., and López-Rivas, A. (2018) Glutamine metabolism regulates FLIP expression and sensitivity to TRAIL in triple-negative breast cancer cells, Cell Death Dis., 9, 205, https://doi.org/10.1038/s41419-018-0263-0.
  68. Wang, B., Pei, J., Xu, S., Liu, J., and Yu, J. (2024) A glutamine tug-of-war between cancer and immune cells: recent advances in unraveling the ongoing battle, J. Exp. Clin. Cancer Res., 43, 74, https://doi.org/10.1186/s13046-024-02994-0.
  69. Lucena-Cacace, A., Otero-Albiol, D., Jiménez-García, M. P., Peinado-Serrano, J., and Carnero, A. (2017) NAMPT overexpression induces cancer stemness and defines a novel tumor signature for glioma prognosis, Oncotarget, 8, 99514-99530, https://doi.org/10.18632/oncotarget.20577.
  70. Hasan Bou Issa, L., Fléchon, L., Laine, W., Ouelkdite, A., Gaggero, S., et al. (2024) MYC dependency in GLS1 and NAMPT is a therapeutic vulnerability in multiple myeloma, iScience, 27, 109417, https://doi.org/10.1016/ j.isci.2024.109417.
  71. Thongon, N., Zucal, C., D’Agostino, V. G., Tebaldi, T., Ravera, S., et al. (2018) Cancer cell metabolic plasticity allows resistance to NAMPT inhibition but invariably induces dependence on LDHA, Cancer Metab., 6, 1, https://doi.org/10.1186/s40170-018-0174-7.

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2. Fig. 1. Comparative analysis of GSEA gene sets in U87MG cells relative to T98G cells. GSEA results: a - for GO:BP collections; b - for GO:CC and GO:MF. The diameter of the circle is proportional to the number of genes with different expression levels compared to the total number of genes in the set. NES is the normalised enrichment score. FDR ≤ 0.05

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3. Fig. 2. Comparative analysis of GSEA gene sets in U87MG versus T98G cells. GSEA results: a - for KEGG, PID, Reactome, WP collections; b - TFT Legacy. The diameter of the circle is proportional to the number of genes with different expression levels compared to the total number of genes in the set. NES is the normalised enrichment score. FDR ≤ 0.05. c, Fold change in expression of genes associated with glutamine metabolism from the Reactome Glutamate and Glutamine Metabolism database in U87MG cell culture relative to T98G (logFC). * FDR ≤ 0.05, ** FDR ≤ 0.01

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4. Fig. 3. Effect of glutamine deprivation on U87MG and T98G cell lines. a - Growth rate of U87MG and T98G cells in the absence or presence of glutamine. Change of expression levels at the mRNA level determined by real-time PCR: b - CD133, c - p21Waf1 and p27KIP1. **** р < 0,005

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5. Fig. 4. Investigation of the metabolic status of U87MG and T98G cells upon glutamine deprivation. a - Microscopic pseudo-coloured FLIM images of the contribution ratio of free form NADH, α1, scale bar = 50 μm for all images. b - Quantification of NADH, α1, by FLIM. Bar charts represent the mean ± standard error of the mean. * Statistically significant deviation from the ‘glutamine+’ group, p < 0.005. c - Estimation of lactate content in the culture medium by the colorimetric method at a wavelength of 570 nm. Bar charts represent the mean value of the optical density of the solution ± standard deviation, * p < 0.001

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6. Fig. 5. Examination of DR5 and cFLIP expression in U87MG and T98G cell lines. a - cFLIP expression; b - DR5 expression at the mRNA level, **** p < 0.005. c - DR5 expression on the cell surface, * p < 0.05. MFI - mean fluorescence intensity

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7. Fig. 6. Cytotoxic activity of temozolomide, TRAIL DR5-B and NAMPT inhibitor GMX1778 on glioblastoma cell lines U87MG and T98G under standard conditions and glutamine deprivation, MTT assay. Significance compared to control: * p < 0.05; ** p < 0.005

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8. Fig. 7. Schematic representation of the opposite effects observed in human glioblastoma cell lines U87MG and T98G upon glutamine deprivation

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