bims-glucam Biomed News
on Glutamine cancer metabolism
Issue of 2020‒10‒18
three papers selected by
Sreeparna Banerjee
Middle East Technical University


  1. Gastroenterol Hepatol. 2020 Oct 07. pii: S0210-5705(20)30249-1. [Epub ahead of print]
    Zhou G, Qin M, Zhang X, Yang J, Yu H.
      BACKGROUND: Topotecan is an anti-cancer chemotherapy drug with common side effects, including hepatotoxicity. In this study, we aim to investigate the mechanisms of topotecan-induced hepatocellular injury beyond conventional DNA damage.MATERIALS AND METHODS: Methyl Thiazolyl Tetrazolium (MTT) assay was used to detect the inhibitory effect of topotecan on cell proliferation. Western blot was used to detect protein expression. Flow cytometry assay was performed to determine apoptosis rate under topotecan treatment. ASCT2 overexpression was addressed using adenovirus vector. qRT-PCR and western blot assay were used to detect the expression of ASCT2. Glutamine uptake, intracellular glutathione (GSH) and reactive oxygen species (ROS) level were detected by glutamine detection kit, GSH detection kit and ROS detection kit respectively.
    RESULTS: MTT results showed that topotecan had an inhibitory effect on cell proliferation and induced apoptosis in both L02 and HepG2 cell lines. Topotecan inhibited the expression of glutamine transporter ASCT2 and the uptake of glutamine in both L02 and HepG2 cell lines. The uptake of glutamine and the GSH level was increased in both L02 and HepG2 cell lines after ASCT2 overexpression. The ROS level was inhibited by ASCT2 overexpression upon topotecan treatment in both L02 and HepG2 cell lines. Topotecan-induced hepatocellular apoptosis and proliferation inhibition were attenuated by ASCT2 overexpression in both L02 and HepG2 cell lines.
    CONCLUSION: Topotecan-induced hepatocytes death is dependent on ASCT2 down-regulation, which causes oxidative stress via inhibiting GSH production.
    Keywords:  ASCT2; Apoptosis; ERO; GSH; Hepatocellular injury; Lesión hepatocelular; ROS; Topotecan; Topotecán
    DOI:  https://doi.org/10.1016/j.gastrohep.2020.05.017
  2. Front Oncol. 2020 ;10 1631
    He Z, Wang C, Xue H, Zhao R, Li G.
      Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high- and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high- and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.
    Keywords:  glioblastoma; heterogeneity; metabolism; prognosis; signature
    DOI:  https://doi.org/10.3389/fonc.2020.01631
  3. Mutagenesis. 2020 Oct 12. pii: geaa026. [Epub ahead of print]
    Yan L, Zhao Z, Wang X, Lyu T, Li J, Qi Y, Wang X, Guo X.
      Glutamine (Gln) is a non-essential amino acid central for generating building blocks and cellular energy in tumours and rapidly proliferating non-transformed cells. However, the influence of Gln on regulating chromosomal stability of transformed and non-transformed cells remain poorly understand. We hypothesised that Gln is required for maintaining a homeostatic level of chromosomal stability. To this end, transformed cells HeLa and A375 and non-transformed cells NCM460 and HUVEC cells were intervened with varying concentrations of Gln (10, 1, 0.1 and 0.01 mM), with or without cisplatin (0.1 µg/ml), for 24 h. The cytokinesis-block micronucleus (MN) assay was used to determine chromosomal instability (CIN), the extent of which is reflected by the frequency of MN, nucleoplasmic bridge (NPB) and nuclear bud (NB). We demonstrated an unexpected decrease in the spontaneous rate of MN, but not NPB and NB, after Gln restriction in HeLa and A375 cells. Gln restriction reduced cisplatin-induced MN, but not NPB and NB, in HeLa and A375 cells. We further revealed that Gln restriction suppressed the proliferation of HeLa cells with high CIN induced by nocodazole, partially explaining why Gln restriction decreased the frequency of spontaneous and cisplatin-induced MN in transformed cells. In contrast, Gln restriction increased MN and NB, but not NPB, in NCM460 cells. In HUVEC cells, Gln restriction increased MN, NPB and NB. Meanwhile, Gln restriction sensitised NCM460 cells to cisplatin-induced genotoxicity. A similar but more pronounced pattern was observed in HUVEC cells. Collectively, these results suggest that the in vitro influences of Gln metabolism on CIN depend on cellular contexts: Transformed cells require high Gln to fine tune their CIN in an optimal rate to maximise genomic heterogeneity and fitness, whereas non-transformed cells need high Gln to prevent CIN.
    DOI:  https://doi.org/10.1093/mutage/geaa026