ISSN 0006-2979, Biochemistry (Moscow), 2024, Vol. 89, No. 10, pp. 1744-1758 © Pleiades Publishing, Ltd., 2024.
1744
Glioblastoma Sensitization to Therapeutic Effects
by Glutamine Deprivation Depends
on Cellular Phenotype and Metabolism
Alina A. Isakova
1,2
, Irina N. Druzhkova
3
, Artem M. Mozherov
3
, Diana V. Mazur
1
,
Nadezhda V. Antipova
1
, Kirill S. Krasnov
4
, Roman S. Fadeev
4
,
Marine E. Gasparian
1
, and Anne V. Yagolovich
2,a
*
1
Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences,
117997 Moscow, Russia
2
Lomonosov Moscow State University, 119991 Moscow, Russia
3
Privolzhsky Research Medical University, 603081 Nizhny Novgorod, Russia
4
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences,
142290 Pushchino, Moscow Region, Russia
a
e-mail: anneyagolovich@gmail.com
Received June 3, 2024
Revised August 23, 2024
Accepted September 5, 2024
Abstract— Glutamine plays an important role in tumor metabolism. It is known that the core region of 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 the genes associated
with glutamine metabolism. Upon glutamine deprivation, growth rate of the U87MG and T98G cells decreased.
Analysis of cellular metabolism by FLIM microscopy of NADH as well as assessment of lactate content in the
medium showed that glutamine deprivation shifted metabolic status of the U87MG cells towards glycolysis.
This was accompanied by the increase in expression of the stemness marker CD133, which collectively could
indicate de-differentiation of these cells. At the same time, we observed increase in both expression of the DR5
receptor and sensitivity of the U87MG cells to DR5-B. On the contrary, glutamine deprivation of T98G cells in-
duced metabolic shift towards oxidative phosphorylation, decrease in the DR5 expression and resistance toDR5-B.
Theeffects of NAMPT inhibition also differed between the two cell lines and were opposite to the effects of DR5-B:
upon glutamine deprivation, U87MG cells acquired resistance, while T98G cells were sensitized to GMX1778. Thus,
phenotypic and metabolic differences between the two human glioblastoma cell lines caused 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-mediated deprivation of amino
acids, as well as when exploring novel therapeutic targets.
DOI: 10.1134/S0006297924100079
Keywords: glutamine deprivation, glioblastoma, cell differentiation, CD133, TRAIL, DR5, NAD(P)H, FLIM
microscopy, NAMPT, GMX1778
Abbreviations: CD133,prominin-1; DR5,death receptor5; FLIM,fluorescence-lifetime imaging microscopy; NADH,reduced
form of nicotinamide adenine dinucleotide; NAMPT,nicotinamide phosphoribosyltransferase; OXPHOS,oxidative phosphor-
ylation; TRAIL,Tumor necrosis factor-related apoptosis-inducing ligand.
* To whom correspondence should be addressed.
GLIOBLASTOMA SENSITIZATION BY GLUTAMINE DEPRIVATION 1745
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
INTRODUCTION
Tumor cells are characterized by intense meta-
bolic processes that require increased consumption of
nutrients. Glutamine is one the most abundant non-
essential amino acids in the body [1]. High glutamine
consumption by tumor cells is an adaptive metabolic
mechanism that promotes proliferation under condi-
tions of hypoxia and nutrient deficiency. During gluta-
minolysis, glutamine is converted into glutamate and
α-ketoglutarate. Glutamine is a carbon and nitrogen
donor for the synthesis of reduced glutathione (GSH),
thereby regulating redox status of tumor cells [2]. Glu-
tamine has been shown to be an anaplerotic precursor
for tricarboxylic acid cycle; in addition, the product
of glutaminolysis is NAD(P)H, which ensures other
anabolic processes [3]. Lack of glutamine can affect
tumor growth: for example, glutamine deprivation in
osteosarcoma cells caused increase in secretion of the
pro-inflammatory chemokine interleukin-8 (IL-8) [4];
also, glutamine deficiency promoted c-MYC-mediated
apoptosis [5]. Glutamine metabolism can be repro-
grammed by the oncogenes c-MYC, KRAS, PI3K/AKT/
mTOR, and tumor suppressor p53 [2].
In solid tumors, there is spatial heterogeneity in
distribution of oxygen and nutrients, which results
in local hypoxia. Similarly, there is local deficiency
of glutamine in tumors, which could be one of the
factors of resistance to chemotherapy [6]. Among the
solid tumors, glioblastoma is particularly dependent on
glutamine metabolism, since homeostasis of the neu-
rotransmitter glutamate, which originates from gluta-
mine, is required for normal brain function. In glio-
blastoma, local glutamine deprivation was also shown,
which was more pronounced deep in the tumor [7, 8].
Drug therapy for glioblastoma is currently limited pri-
marily to temozolomide, an alkylating-type cytostatic
chemotherapy drug. However, given its insufficient ef-
ficacy, a search for new promising molecular targets is
necessary [9]. The death receptor DR5 is known to be
a prognostic marker for the patients with glioblastoma
[10], and use of the DR5 agonists in the treatment of
glioblastoma has also been investigated [11]. Another
promising target is NAMPT, an enzyme that catalyz-
es synthesis of nicotinamide mononucleotide, which
is the rate-limiting step in NAD biosynthesis. NAMPT
inhibitors have potential for treatment of solid tumors,
including glioblastoma [12].
A number of strategies for drug-induced gluta-
mine deprivation in combination with other antican-
cer drugs are being developed to enhance therapeutic
efficacy [13, 14]. The work aimed at investigating the
effect of glutamine deprivation on metabolism of the
human glioblastoma cells and their sensitivity to ther-
apeutic effects of the drugs of different nature: con-
ventional chemotherapeutic drug temozolomide [15],
DR5 receptor agonist– modified cytokine TRAIL DR5-B
[16], as well as small molecule targeted inhibitor of
NAMPT – GMX1778 [17].
MATERIALS AND METHODS
Cell culturing. U87MG and T98G glioblastoma
cell lines were obtained from the ATCC (USA) and cul-
tured in a DMEM nutrient medium (PanEco, Russia,
Cat. No. C420p) containing 4.5 g/l glucose and 110 mg/ml
sodium pyruvate, with addition of 10% fetal calf se-
rum (HyClone, USA, Cat. No. K052/SV30 160.03), 2 mM
L-glutamine (PanEco, Russia, Cat. No. F032), penicillin
(100 μg/ml) and streptomycin (100 μg/ml) (PanEco,
Russia, Cat. No. A073p) at 37°C and 5% CO
2
. To simu-
late glutamine deprivation, cells were incubated in a
L-glutamine-free medium for at least 72 h.
Transcriptome sequencing. RNA sequencing was
performed at Genoanalitika, on a HiSeq1500 device (Il-
lumina, USA) generating at least 40 million short reads
with length of 150 nucleotides. Using the STAR software
(version 2.7.9a), the initial reads were mapped to the
GRCh38 genome, and number of the reads mapped to
individual genes (Ensembl annotation, version 99) with
no more than three mismatches was counted. RNA se-
quencing for each group was performed in duplicate.
Differential gene expression analysis. Differen-
tial gene expression in the U87MG cell culture rela-
tive to T98G was evaluated using the DEseq2 package
version 1.28.1 [18] for the R programming language
version 4.2.2. Gene Sets Enrichment Analysis (GSEA)
was performed using the Molecular Signature DataBase
MsigDB [19] and the GSEApy library for the Python
programming language version 3.11.7 version 1.0.6
[20]. For analysis, we used a ranked list of genes
sorted by decreasing fold change in expression, ob-
tained by calculating differential gene expression
(GSEA preranked). To study in detail changes in the
cell transcriptional activity, GSEA was performed using
gene sets from the curated GeneOntology databases
(subcollections GO: Biological Processes GO:BP, GO:
Cellular Components GO:CC, and GO: Molecular Func-
tions GO:MF) [21], Kyoto Encyclopedia of Genes and
Genomes (KEGG) [22], Reactome [23], WikiPathways
(WP) [24], Protein Interaction Database (PID) [25],
Transcription Factor Targets Legacy (TFT Legacy) [26].
To assess changes in the transcriptional activity of the
genes associated with glutamine metabolism, a list of
genes was obtained from the Reactome Glutamate and
Glutamine Metabolism database gene set, for which
fold change in expression (logFC) values were used.
To assess significance of gene expression changes us-
ing DESeq2, the Wald test with correction for Benja-
mini–Hochberg multiple testing (FDR) was used [27].
All data are presented for FDR ≤ 0.05.
ISAKOVA et al.1746
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
Table 1. Primers for RT-qPCR
No. Name
Sequence (5′->3′)
Forward Reverse
1 p21
Waf1
(CDKN1A-F110) AGTCAGTTCCTTGTGGAGCC CATTAGCGCATCACAGTCGC
2 p27
KIP1
(CDKN1B-F115) TGTTTCAGACGGTTCCCCAAA CCATTCCATGAAGTCAGCGAT
3 CD133 ACTCCCATAAAGCTGGACCC TCAATTTTGGATTCATATGCCTT
4 DR5 TGGAACAACGGGGACAGAACG GCAGCGCAAGCAGAAAAGGAG
5 cFLIP GGCTCCCCCTGCATCACATC CCGCAGTACACAGGCTCCAGA
6 18S GGCCCTGTAATTGGAATGAGTC CCAAGATCCAACTACGAGCTT
Quantitative reverse transcription PCR (RT-qPCR).
Total RNA was isolated using an ExtractRNA reagent
(Evrogen, Russia). RNA concentration was deter-
mined using a Nanodrop One C spectrophotometer
(Thermo Fisher Scientific, USA). Total RNA was used
as a template for cDNA synthesis using a MMLV RT kit
(Evrogen) according to the manufacturer’s instructions.
Amplification was carried out for 5 min at 10°C, and
then for 25 min at 37°C and 42°C, respectively, and at
70°C for 10 min to inactivate the enzyme. Real-time
PCR was performed with a LightCycler 96 (Roche) us-
ing a qPCRmix-HS SYBR reagent (Evrogen) according
to the manufacturer’s instructions according to the fol-
lowing program: 95°C for 150 s, 45 cycles of 95°C for
20 s, 60°C for 20 s and 72°C for 20 s. Data acquisition
was performed using the LightCycler Software (ver-
sion4.1). Absence of PCR byproducts was determined
from melting curves. For each primer pair, identical
PCR melting peaks of each sample were observed in
triplicate across all samples. The obtained Ct (cycle
threshold) values for each sample did not exceed 35.
Ribosomal 18S RNA gene was used as an internal con-
trol. Relative gene expression levels were calculated
with the 2
–ΔΔCT
method. Visualization and statistical
data processing were performed using the GraphPad
Prism 9.3.1 (GraphPad Software, USA). Cell samples
were compared with each other using one-way ANOVA.
Differences were considered significant at p < 0.05.
Primers for detecting expression of the p21
Waf1
, p27
KIP1
,
CD133 (prominin-1), DR5, and cFLIP genes are listed
in Table 1.
Fluorescent-lifetime imaging microscopy (FLIM).
FLIM of the metabolic cofactor NADH (reduced form
of nicotinamide adenine dinucleotide) was performed
using a laser scanning microscope LSM880 (Carl Zeiss,
Germany). A femtosecond Τi:Sa laser (Spectra Physics,
USA) with a pulse repetition rate of 80MHz and dura-
tion of 120 fs was used as an excitation source. Detec-
tion of fluorescence lifetimes was performed using a
TCSPC FLIM module (Becker &Hickl GmbH, Germany),
based on time-correlated single-photon counting.
Toobtain images, a 40×/1.3 oil immersion objective was
used. NAD(P)H fluorescence was excited in a two-pho-
ton mode at a wavelength of 750 nm, and the signal
was recorded in the range of 450-490 nm. Power of the
exciting radiation was7 mW. Photon acquisition time
was 60 s. Number of photons per pixel was no less
than 5000. During the experiment, the cells were in an
incubator at 37°C, 5% CO
2
. FLIM data were processed
using the SPCImage software (Becker &Hickl GmbH).
Least squares approximation was used to obtain pa-
rameters of the decay curves at each pixel. NAD(P)H
flu orescence decay curves were fitted by a bi-expo-
nential model. Approximation accuracy was assessed
using the χ2 parameter. For all data, χ2 ranged from
0.8 to 1.2. Short and long decay components (τ1 and τ2,
respectively), relative amplitudes of these components
(α1 and α2), as well as average fluorescence lifetime
(τm = α1τ1 + α2τ2) were estimated. Autofluorescence
analysis was performed for each cell individually in
the cytoplasmic region. For each group, at least 5 im-
ages were obtained with a total number of cells of
at least 30.
Assessment of cell glycolytic activity from lac-
tate content in the culture medium. U87MG and
T98G cells were seeded in a 6-well plate at 5×10
5
cells
per well and cultured in a medium with or without
glutamine for 20 h. Then the culture medium was col-
lected and proteins were removed using a deprotein-
ization kit (ab204708, Abcam, USA). Samples were an-
alyzed using a colorimetric lactate assay kit (MAK064,
Sigma-Aldrich, USA). Amount of lactate was assessed
from optical density of the solution at a wavelength
of 570 nm using a SYNERGYmx microplate reader
(BioTek, USA).
Flow cytometry. To analyze surface expression of
DR5 receptor, U87MG and T98G cells were seeded in
6-well plates at 2×10
5
cells per well and cultured at 37°C,
5% CO
2
with or without glutamine for at least 72 h.
Cells were detached with a Versene solution, washed
GLIOBLASTOMA SENSITIZATION BY GLUTAMINE DEPRIVATION 1747
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
with an ice-cold PBS, resuspended in a FACS buffer
(1% BSA in PBS), and incubated for 1 h at 4°C with
5 μg/ml of monoclonal antibodies against DR5 (clone
DR5-01-1, GeneTex, USA). Cells were then washed twice
and incubated for 1 h at 4°C with 20 μg/ml of a sec-
ondary antibody Dylight 488 (GeneTex), washed, and
resuspended in FACS buffer containing propidium io-
dide. DR5 receptor expression was determined using
aCytoFlex flow cytometer (Beckman Coulter, USA) us-
ing mouse IgG1 as an isotype control.
Production of a recombinant DR5-specific TRAIL
variant DR5-B. Recombinant DR5-B protein was ex-
pressed in E. coli SHuffle B cells and purified from the
soluble cellular fraction by nickel affinity and ion ex-
change chromatography as described previously [28].
Cytotoxicity assay. U87MG or T98G cells were
seeded into 96-well plates at 1×10
4
cells per well.
After 24 h, temozolomide (Macklin, China), DR5-B or
GMX1778 were added to the cells at the indicated con-
centrations and incubated for 72 h. Temozolomide and
GMX1778 were pre-dissolved in DMSO at concentration
of 1 M and 100 mM, respectively, and added to the cells
so that final DMSO content in the well did not exceed
0.5% and did not exhibit a cytotoxic effect. Then 0.05%
MTT was added to the cells, incubated for 4 h, and the
formed crystals were dissolved in DMSO (100 μl per
well). Absorbance was measured at 570 nm using an
iMark plate spectrophotometer (Bio-Rad, USA).
Statistical analysis. The obtained data were nor-
mally distributed and expressed as a mean or a mean
±standard deviation. Normality of the distribution was
assessed using the Shapiro–Wilk test. No significant
outliers were observed. Statistical analysis of all re-
sults except differential gene expression was carried
out using Student’s t-test. The experiments were per-
formed in triplicate. The results were processed using
GraphPad Prism8.0.1 (USA). Differences were consid-
ered significant at p < 0.05.
RESULTS
Human glioblastoma cells U87MG have more
differentiated phenotype compared to T98G. In this
work, human glioblastoma cell lines U87MG and T98G
were studied. To compare characteristics and differ-
ences of the two cell lines, transcriptome sequencing
was performed under standard culture conditions.
Analysis of differential gene expression between the
U87MG and T98G cell lines by functional affiliation of
the gene sets (GSEA, Gene Sets Enrichment Analysis)
from the GO:BP database showed increased activity
of the genes associated with major histocompatibil-
ity complex class II in the U87MG cells relative to
the T98G cells (Fig. 1a). Similar results were obtained
when analyzing the genes from the GO:CC and GO:MF
subcollections (Fig.1b). This suggests that the U87MG
cells have a more differentiated phenotype compared
to the T98G cells [29, 30]. Analysis using the sets of
genes from the KEGG, WP, Reactome, and PID collec-
tion (Fig.2a) showed increased activity of the genes in-
volved in signal transduction processes in the U87MG
cells due to generation of the secondary signaling mol-
ecules, as well as in the PD-1 signaling pathway, which
could be one of the factors of cellular resistance to
TRAIL [31]. In addition, the GSEA analysis was per-
formed using transcription factor (TF) target gene sets
(TFT-Legacy collection), which could indirectly indicate
activity of the genes under control of the specific TFs
(Fig. 2b). Transcriptional activity of the genes under
control of transcription factors EN, RSRFC4 (MEF2A),
EVI1 (MECOM), FOXJ2, CDPCR3 (CUTL1, CUX1), RORA2
(RORA), NKX25 (NKX2-5), PAX4, HFH1 (FOXQ1), and
FOXD3 was increased in the U87MG cells relative to
the T98G cells. Activity of these transcription factors
is important for regulation of cell differentiation, indi-
cating a more differentiated phenotype of the U87MG
cells compared to the T98G cells.
U87MG and T98G cells differ in the level of tran-
scriptional activity of the genes associated with
glutamine metabolism. Analysis of the changes in
the transcriptional activity of the genes associated
with glutamine metabolism from the GSEA MSigDB
Reactome Glutamate and Glutamine Metabolism da-
tabases showed statistically significant increase in the
expression of glutamate dehydrogenase1 (GLUD1), in-
volved in glutamate catabolism [32], but not its paralog
GLUD2, in the U87MG cells relative to the T98G cells
(Fig. 2c). At the same time, expression of a number
of other genes in the U87MG cells was significantly
lower than in the T98G cells: mitochondrial aspartate
aminotransferase (GOT2), which is associated with re-
duced glutamine metabolism and cellular senescence
[33]; cytoplasmic (PYCR3) and mitochondrial (PYCR1)
pyrroline-5-carboxylate reductases, catalyzing biosyn-
thesis of proline from glutamate as a precursor [34];
glutamine synthetase (GLUL), catalyzing endogenous
synthesis of glutamine from glutamate [32]; and N-ace-
tyl-aspartyl-glutamate synthaseA (NAAGSA, RIMKLA),
metabolizing glutamate [35]. There were no significant
changes in the expression of other genes involved in
glutamine metabolism (Fig. 2c).
Glutamine deprivation reduces growth rate of
the U87MG and T98G cells and increases expres-
sion of the stem cell marker CD133 and the cyclin-
dependent kinase inhibitors p21
Waf1
and p27
KIP1
in
the U87MG cells. Glutamine-free cell culturing led to
the decrease in the growth rate of U87MG and T98G
cells to varying degrees (Fig. 3a), which is consistent
with the previously published data [36, 37]. Since
U87MG and T98G cells differ in their initial differen-
tiation state, we assessed the change in expression
ISAKOVA et al.1748
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
Fig. 1. Comparative analysis of GSEA gene sets in the U87MG cells relative to T98G cells. GSEA results for GO:BP(a), GO:CC
and GO:MF(b) collections. Diameter of the circle is proportional to the ratio of the number of differentially expressed genes
versus total number of genes in the set. NES,normalized enrichment score. FDR≤0.05.
of theglioma stem cell marker CD133 upon glutamine
deprivation using RT-qPCR. The level of CD133 expres-
sion increased in the U87MG cells, which could indi-
cate the process of de-differentiation of these cells.
In the T98G cells, the CD133 marker was not detect-
able regardless of the presence of glutamine (Fig. 3b).
A similar effect was observed for the expression of
inhibitors of cyclin-dependent kinases p21
Waf1
and
p27
KIP1
: significant increase in the expression was ob-
served in the U87MG cells, but not in the T98G cells
(Fig. 3c).
Glutamine deprivation induces contrasting met-
abolic effects in U87MG and T98G cells as revealed
by FLIM. Changes in the metabolism of U87MG and
T98G cells upon glutamine deprivation were studied
using metabolic imaging based on two-photon fluo-
rescence-lifetime imaging microscopy (FLIM) of NADH
autofluorescence. The phosphorylated form NADPH,
which is characterized by the longest fluorescence
lifetime of ~4.4 ns [38], was not detected in the an-
alyzed cell cultures. When cultured with glutamine,
percentage contributions of free (α1) and bound NADH
forms for the U87MG and T98G cells did not differ
initially (Table 2). Analysis of the NADH autofluores-
cence parameters showed typical lifetime values of the
free (τ1) and protein-bound forms (τ2) [39, 40]. Upon
glutamine deprivation in the U87MG cells, decrease in
the average fluorescence lifetime(τm), lifetime of the
free form(τ1), and increase in the contribution of the
free form (α1) were observed, which together could
indicate shift of the cell metabolism towards glycol-
ysis. At the same time, lifetime of the protein-bound
form (τ2) remained unchanged. In the T98G cells, on
the contrary, increase in the parameters τm and τ1
was observed, and the glycolysis-associated contribu-
tion of the free form α1 significantly decreased, which
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BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
Fig. 2. Comparative analysis of GSEA gene sets in the U87MG cells relative to T98G cells. a)GSEA results for KEGG, PID,
Reactome, WP; b)TFT Legacy collections. Diameter of the circle is proportional to the ratio of the number of differentially
expressed genes versus total number of genes in the set. NES,normalized enrichment score. FDR≤0.05. c)Fold change (FC)
in the expression of genes associated with glutamine metabolism from the Reactome Glutamate and Glutamine Metabolism
database in the U87MG cells relative to T98G cells (logFC). *FDR≤0.05; **FDR≤ 0.01.
Fig. 3. Effects of glutamine deprivation on U87MG and T98G cell lines. a)Growth rate of U87MG and T98G cells in the absence
or presence of glutamine. b)Changes in the expression levels of CD133. c)p21
Waf1
and p27
KIP1
at the mRNA level determined
by RT-qPCR. ****p<0.005.
ISAKOVA et al.1750
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
Table 2. Parameters of NADH autofluorescence in U87MG and T98G cells in the absence or presence of glutamine
(mean ±SD)
Cell line Glutamine τm, ns τ1, ns τ2, ns α1, %
U87MG
+ 0.84±0.07 0.42±0.03 2.47±0.19 79.31±1.74
–
0.74±0.07*
p<0.001
0.37±0.04
p<0.001
2.32±0.18
p<0.001
81.16±1.60*
p<0.001
T98G
+ 0.86±0.08 0.46±0.04 2.46±0.21 79.84±1.95
–
0.99±0.10*
p<0.001
0.51±0.05*
p<0.001
2.73±0.24
p<0.001
77.56±2.07*
p<0.001
Note. τ1 and τ2 are fluorescence lifetimes of the free and protein-bound NADH, respectively, τm is average fluorescence lifetime
of NADH, α1 is percentage contribution of the free form of NADH.
*Significant difference from the control, p<0.001.
Fig. 4. Study of the metabolic status of U87MG and T98G cells upon glutamine deprivation. a)Microscopic pseudo-colored FLIM
images of the free form NADH contribution ratio, α1; scale bar: 50μm for all images. b)Quantification of NADH, α1, by FLIM.
Bar graphs represent a mean ±SEM. *Statistically significant deviation from the “glutamine+” group, p<0.005. c)Evaluation
of lactate content in the culture medium by colorimetric method at a wavelength of 570nm. Bar graphs represent a mean
optical density of the solution ±SEM, *p<0.001.
allows to conclude that there is a shift in metabo-
lism towards oxidative phosphorylation [41] (Fig.4,a
and b, Table 2).
Glutamine deprivation differentially modulates
lactate levels in the culture medium of U87MG and
T98G cells. Significant increase in the lactate produc-
tion was observed in the U87MG cells cultured in glu-
tamine-free medium: calorimetrically assessed optical
density was 1.99 ± 0.04 versus 1.42 ± 0.07 (p < 0.01).
Incontrast, for the T98G cells, decrease in the optical
density of the solution was observed from 1.55 ± 0.05
to 0.96 ± 0.07, p < 0.01 (Fig.4c). The obtained data con-
firm the changes observed by FLIM.
Glutamine deficiency modulates expression of
the cytokine TRAIL signaling pathway components.
It was shown in the recent work that glutamine depri-
vation in pancreatic cancer cells resulted in the de-
creased expression of antiapoptotic protein cFLIP, a
caspase-8 homologue without protease activity, thereby
increasing sensitivity of the cells to cytotoxic effects of
the cytokine TRAIL; however, surface expression of the
TRAIL target receptor DR5 was unchanged [42]. How-
ever, in our experiments with glioblastoma cells, the
opposite effects were observed: upon glutamine depri-
vation, the cFLIP expression increased in the U87MG
cells, but did not change in the T98G cells (Fig. 5a).
In the U87MG cell, increase in the DR5 receptor ex-
pression was also observed both at the mRNA level,
determined by RT-qPCR (Fig. 5b), and on the cell sur-
face, as determined by flow cytometry (Fig. 5c), where-
as the opposite trend was observed for the T98G cells
(Fig. 5, b and c). This suggests that not only the cells
of different tumor types, but also different cell lines
of the same tumor type could respond differently to
glutamine deprivation.
Glutamine deprivation in U87MG and T98G cells
leads to opposite effects upon therapeutic treat-
ments. Despite the proposed relationship between
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Fig. 5. DR5 and cFLIP expression in U87MG and T98G cell lines. Expression of (a) cFLIP and (b) DR5 at the mRNA level,
****p<0.005. c)Expression of DR5 on the cell surface, *p<0.05.
Fig. 6. Cytotoxicity of temozolomide (a), TRAIL DR5-B(b), and NAMPT inhibitor GMX1778(c) for glioblastoma cell lines U87MG
and T98G under standard conditions and upon glutamine deprivation, MTT test. * Significance compared to the control,
p<0.05; **p<0.005.
ISAKOVA et al.1752
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
glutamine deprivation and tumor chemoresistance,
glutamine deprivation did not affect sensitivity of
both cell lines to temozolomide (Fig.6a). However, cell
sensitivity to the targeted DR5-specific cytokine TRAIL
DR5-B differed significantly: glutamine deprivation
sensitized the DR5-B-resistant U87MG cells, whereas
the initially DR5-B-sensitive T98G cells, on the contrary,
became more resistant to the DR5-mediated cell death
(Fig. 6b). Such heterogeneous response of the U87MG
and T98G cells to DR5-B correlates with the opposite
changes in the expression of DR5 and cFLIP in these
cells (Fig.5). NAMPT inhibitor GMX1778 also induced
opposite effects: in the absence of glutamine, resis-
tance of the U87MG cells increased, while the T98G
cells were sensitized to GMX1778 (Fig. 6c).
DISCUSSION
Metabolism of amino acids, particularly of gluta-
mine, plays an important role in tumor development.
In this regard, studying of the effects of glutamine
deprivation on tumor cells is important both for iden-
tifying biochemical processes occurring in tumors and
for the development of new potential therapies through
the drug-induced deprivation of amino acids. In this
work, we investigated the effect of glutamine depri-
vation on two human glioblastoma cell lines, U87MG
and T98G, which differ in a number of characteristics.
Transcriptome analysis revealed more differentiated
phenotype of the U87MG cell line compared to the
T98G cell line. Also, the data on differential activity of
the genes associated with glutamine metabolism from
the GSEA MSigDB Reactome Glutamate and Glutamine
Metabolism database in U87MG cells relative to T98G
(increased expression of GLUD1 and decreased expres-
sion of GOT2, PYCR3, PYCR1, GLUL, RIMKLA) pointed
to possible differences in susceptibility of the cells to
glutamine deprivation, as was observed in the subse-
quent experiments.
In particular, upon glutamine-free culturing, in-
creased expression of the stem cell marker CD133
was observed in the U87MG cells, in contrast to the
T98G cells. This could indicate de-differentiation of
the U87MG cells, which is supported by a number of
studies. For example, in the study by Pan et al., re-
gional glutamine deficiency in the tumor core led to
histone hypermethylation and caused de-differentia-
tion of tumor cells [6]. In addition, de-differentiation of
differentiated glioblastoma cells as a result of hypoxia
in the tumor core was shown, which was accompa-
nied by the increased expression of stemness markers
and acidification of the core region [43, 44]. Overall,
the process of de-differentiation of tumor cells under
the influence of various triggers with activation of
stemness genes and acquisition of properties similar to
those of cancer stem cells has been described in the lit-
erature [45].
There are conflicting data regarding the effects
of glutamine on tumor cell stemness. For example,
glutamine deprivation or inhibition of glutaminase 1
(GLS1), which converts glutamine to glutamate, re-
duced expression of the stemness-related genes in
hepatocellular carcinoma cells [46]. Similarly, gluta-
mine deprivation inhibited tumor cell self-renewal and
reduced expression of the stemness-related genes in
the pancreatic cancer stem cells [47]. In contrast, in
another study, glutamine deficiency in the ovarian and
colon cancer cells caused metabolic reprogramming,
leading to increased glycolysis, decreased proliferation,
and increased population of tumor stem cells [48].
We observed similar conflicting effects in the U87MG
and T98G cell lines.
Decrease in proliferation rate upon glutamine
deprivation in our experiments was observed in both
lines, but was more pronounced for the T98G cells
than for the U87MG cells. Glioblastoma cell growth
rate could be differentially affected by glutamine: lack
of exogenous glutamine inhibits proliferation of the
glioma cell lines D-54 MG, U-118 MG, and U-251 MG,
but not U-373MG, D-245MG, and D-259MG [36]. Later
it was shown that the glioma stem cells are able to
grow independently of exogenous glutamine, which is
associated with the increased expression of glutamine
synthetase (GLUL) [49]. However, recent work has
shown that the tumors from the U87MG cell line, on
the contrary, are glutamine dependent [50], correlating
with the decreased level of GLUL expression and in-
creased level of the GLUD1 expression (Fig. 2c), which
also determines glutamine dependence [51]. This could
be due to the absence of stem cell population, sup-
porting our findings that the U87MG cells are highly
differentiated. Unfortunately, the CD133 expression
was not observed in the T98G cells, which, however,
does not contradict their lower degree of differentia-
tion, since the degree of stemness and differentiation
is determined by the expression of various markers,
among which CD133 is not the most specific [45].
It is known that inhibitors of the cyclin-dependent
kinases p21
Waf1
and p27
KIP1
prevent proliferation and
thereby maintain dormant state of stem cells and their
ability to self-renew [52]. Therefore, increase in the
p21
Waf1
and p27
KIP1
expression in U87MG cells along
with the increase in CD133 correlates with the liter-
ature data reporting that activation of p21
Waf1
and
p27
KIP1
maintained dormant state of the CD133
+
stem
cells [53]. This also supports our hypothesis about
de-differentiation of the U87MG cells upon glutamine
deprivation. However, in the T98G cells, in contrast to
the U87MG cells, increase in the expression of p21
Waf1
and p27
KIP1
was not statistically significant. This
could be due to mutation in the p53 gene [54], since
GLIOBLASTOMA SENSITIZATION BY GLUTAMINE DEPRIVATION 1753
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
proliferation of the T98G cells was inhibited through
alternative mechanisms.
The differences revealed between the U87MG and
T98G cell lines cultured in the absence of glutamine
were accompanied by opposing metabolic processes.
The NADH autofluorescence studies using FLIM-based
metabolic imaging showed shift in the metabolic ac-
tivity toward glycolysis in the U87MG cells, which was
also confirmed by the increased lactate production.
This correlates with the previously published data:
for example, it was shown in the recent study that
glutamine deprivation in the colorectal and pancre-
atic cancer cells leads to lysosomal acidification and
induction of pro-survival autophagy. The study also
demonstrated metabolic shift towards glycolysis under
glutamine deprivation using the Seahorse™ glycolysis
stress test and the CYRIS® flox multisensor cell analy-
sis platform [37]. Increased glycolysis has also been
shown as an adaptive response to glutamine deficien-
cy in the glioblastoma cells [8]. Since the switch in
metabolic activity towards oxidative phosphorylation
has been previously shown during differentiation of
stem [55,56] and induced pluripotent cells [57, 58], the
observed opposite shift towards glycolysis upon glu-
tamine deprivation in the U87MG cells independently
confirms the reverse process, i.e., de-differentiation
of these cells.
Metabolic changes during glutamine deprivation
in the T98G cells, unlike in the U87MG cells, were
associated with the shift towards OXPHOS (oxidative
phosphorylation), manifested both by NADH autoflu-
orescence and by the decrease in lactate production.
Previously, we and other researchers have shown that
these changes correlate with the cell stress response.
Inparticular, shift towards OXPHOS is a typical response
to chemotherapy of various tumor cells including glio-
mas [59, 60]. Perhaps the opposite shifts towards gly-
colysis or OXPHOS observed upon glutamine depriva-
tion in the U87MG and T98G lines, respectively, are due
to metabolic plasticity of the tumor cells, which could
determine sensitivity of the tumor cells to therapy.
For example, high levels of OXPHOS were observed in
the metformin-sensitive colorectal cancer line HT29,
while the metformin-resistant SW620 cells had low
levels of OXPHOS, but became sensitive to metformin
upon glutamine deprivation [61]. Also, opposite effects
of glutamine on sensitivity of the neuroblastoma cells
to chemotherapy have been reported: glutamine depri-
vation suppressed the etoposide-induced apoptosis,
but stimulated the cisplatin-induced apoptosis [62].
Based on this information, sensitivity of the
U87MG and T98G cell lines to therapeutic effects of
drugs of various nature was investigated in the next
stage of our study: the conventional drug temozolo-
mide, the DR5-specific cytokine TRAIL DR5-B, and the
targeted inhibitor of nicotinamide phosphoribosyl-
transferase (NAMPT). Glutamine deprivation had no
effect on sensitivity of either U87MG or T98G cells to
temozolomide, but it significantly altered sensitivity
of both cell lines to two other drugs. U87MG cells are
initially resistant to the cytokine TRAIL DR5-B com-
pared to the T98G cells, which correlates with their
high degree of differentiation, since it is known that as
the cells differentiate, surface expression of the TRAIL
death receptors decreases [63,64]. Consistent with this,
glutamine deprivation in the U87MG cells resulted in
the increased expression of DR5 and increased the
DR5-mediated cell death, although the sensitizing ef-
fect was not pronounced, apparently due to increase in
cFLIP, or due to mutation in the gene encoding PTEN
phosphatase [54]. This correlates with the increased
sensitivity of breast cancer [65] and pancreatic can-
cer cells to the TRAIL-mediated apoptosis upon glu-
tamine deprivation [42]. Similarly, acute glutamine
deprivation induced tumor cell apoptosis triggered by
the CD95-mediated caspase cascade [66]. However, it
should be noted that the T98G cell line, which was ini-
tially highly sensitive to the DR5-mediated cell death,
on the contrary, became more resistant to DR5-B in
the absence of glutamine, which was accompanied by
the decrease in DR5 expression. Interestingly, similar
results were previously obtained for the breast cancer
Fig. 7. Schematic representation of the opposite effects observed in the human glioblastoma cell lines U87MG and T98G upon
glutamine deprivation.
ISAKOVA et al.1754
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
cell lines: glutamine deprivation in the triple-negative
breast cancer cells with basal (mesenchymal) pheno-
type increased their sensitivity to TRAIL, whereas
the breast cancer cell lines with luminal phenotype
were refractory to TRAIL sensitization upon glutamine
deprivation [67]. Since the TRAIL signaling pathway
plays an important role in the immunosurveillance
of tumor cells, our findings support the known fact
that glutamine metabolism could modulate antitumor
immune response [68].
NAMPT is also a promising target in glioblasto-
ma: in particular, its expression correlates with the
degree of glioblastoma stemness and prognosis for
patients [69]. Since glutamine is one of the sources of
NAD(P)H [3], and NAMPT is the rate-limiting step in
NAD biosynthesis, catalyzing formation of its interme-
diate product nicotinamide mononucleotide, these met-
abolic pathways are obviously interconnected. Recent
data suggest that simultaneous drug-induced inhibition
of glutaminolysis and NAD synthesis could become a
successful strategy for cancer therapy [70]. As with the
TRAIL DR5-B, contrasting effects were also observed
in the U87MG and T98G cells when cultured in the
absence of glutamine: the T98G cells were sensitized
to the NAMPT inhibitor GMX1778, whereas the U87MG
cells acquired resistance. This result correlates with
the published data that the tumor cell resistance to
NAMPT inhibitor is associated with the shift toward
glycolysis [71], which we observed in the U87MG cells.
Importantly, changes in the sensitivity of U87MG and
T98G cells to GMX1778 upon glutamine deprivation
were opposite to the changes in sensitivity to the
DR5-mediated cell death, but mechanisms of this phe-
nomenon remain to be elucidated.
Schematic representation of the effects observed
in the U87MG and T98G cell lines upon glutamine
deprivation are shown in Fig. 7.
CONCLUSION
Phenotypic and metabolic heterogeneity of human
glioblastoma cells was demonstrated using two cell
lines, U87MG and T98G. Upon glutamine deprivation,
differences in the initial degree of differentiation and
metabolic plasticity of these cell lines resulted in op-
posite metabolic changes and contrasting responses to
the targeted drugs of various nature. Our data could
potentially facilitate effective selection of the patients
who could respond to the targeted therapy based
on phenotypic and metabolic status of the tumor.
In addition, the obtained results could help to iden-
tify mechanisms of tumor resistance associated with
glutamine metabolism and facilitate development of
the drug-induced glutamine deprivation regimens to
enhance effectiveness of antitumor therapy.
Acknowledgments. The authors are grateful to
D. N. Kazyulkin, A. V. Kurkin for providing GMX1778
and to V. V. Tatarskiy for providing primers, as well as
to B. V. Chernyak and M. V. Shirmanova for valuable
comments.
Contributions. A.V.Y. conceptualization, project
administration, funding acquisition; A.A.I., D.V.M.,
K.S.K., I.N.D., and A.M.M. investigation, data curation,
visualization; N.V.A., I.N.D., R.S.F., and M.E.G. methodol-
ogy, validation; A.V.Y. and K.S.K. writing (original draft);
I.N.D. and M.E.G. writing (review and editing).
Funding. The study was financially supported by
the Russian Science Foundation, grant no.24-24-00222,
https://rscf.ru/project/24-24-00222/ (in Russian).
Ethics declarations. This work does not contain
any studies involving human and animal subjects.
Theauthors of this work declare that they have nocon-
flicts of interest.
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) Glutami-
nolysis: 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., Nis-
sim,I., Yudkoff,M., Wehrli, S., and Thompson, C. B.
(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., Follet-
tie,M., Myers,J., Fantin, V.R., and Abraham, R.T. (2014)
Glutamine deprivation stimulates mTOR-JNK-depen-
dent chemokine secretion, Nat. Commun., 5, 4900,
https://doi.org/10.1038/ncomms5900.
5. Yuneva, M., Zamboni, N., Oefner, P., Sachidanan-
dam,R., and Lazebnik,Y. (2007) Deficiency in gluta-
mine but not glucose induces MYC-dependent apop-
tosis 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., Liu, X., Yang, Y., Hernandez-Davies,
J.E., Rosales, K.K., Li,H., Hugo,W., Song, C., Xu, X.,
Schones, D. E., Ann, D. K., Gradinaru, V., Lo, R. S.,
Locasale, J. W., and Kong, M. (2016) Regional gluta-
mine deficiency in tumours promotes dedifferenti-
ation through inhibition of histone demethylation,
Nat. Cell Biol., 18, 1090-1101, https://doi.org/10.1038/
ncb3410.
GLIOBLASTOMA SENSITIZATION BY GLUTAMINE DEPRIVATION 1755
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
7. Márquez, J., Alonso, F. J., Matés, J. M., Segura, J. A.,
Martín-Rufián,M., and Campos-Sandoval, J.A. (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., Wang,X.,
Wan,X., Yuan,G., and Pan,Y. (2024) The mechanism
of extracellular CypB promotes glioblastoma adap-
tation to glutamine deprivation microenvironment,
Cancer Lett., 597, 216862, https://doi.org/10.1016/
j.canlet.2024.216862.
9. Jia, J.L., Alshamsan,B., and Ng, T.L. (2023) Temozolo-
mide 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.A., Span, M.M., Hollema,H., and
den Dunnen, W. F. A. (2006) TRAIL-receptor expres-
sion 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 en-
hancing TRAIL therapies in glioblastoma, Neuro On-
col. Adv., 5, vdad047, https://doi.org/10.1093/noajnl/
vdad047.
12. Galli,U., Colombo,G., Travelli,C., Tron, G.C., Genaz-
zani, A.A., and Grolla, A.A. (2020) Recent advances
in NAMPT inhibitors: a novel immunotherapic strate-
gy, 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 thera-
py, 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., Kabut, J., and Gisterek-Grocholska, I.
(2024) Temozolomide (TMZ) in the treatment of glio-
blastoma multiforme – a literature review and clini-
cal 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., Sycheva, A. M.,
Moshkovskii, S. A., and Kirpichnikov, M. P. (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., Marcel-
lus,R., Bédard,D., Bernier,C., Branchaud,S., Chan,H.,
Dairi, K., Gilbert, K., Goulet, D., Gratton, M.-O.,
Isakau, H., Jang, A., Khadir, A., Koch,E., Lavoie, M.,
Lawless, M., Nguyen, M., Paquette, D., Turcotte, É.,
Berger, A., Mitchell, M., Shore, G. C., and Beaupar-
lant,P. (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) Moderat-
ed 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., Thor-
valdsdóttir, H., Tamayo, P., and Mesirov, J. P. (2011)
Molecular signatures database (MSigDB) 3.0, Bio-
informatics, 27, 1739-1740, https://doi.org/10.1093/
bioinformatics/btr260.
20. Fang,Z., Liu, X., and Peltz, G. (2023) GSEApy: a com-
prehensive package for performing gene set enrich-
ment analysis in Python, Bioinformatics, 39, btac757,
https://doi.org/10.1093/bioinformatics/btac757.
21. Ashburner,M., Ball, C.A., Blake, J.A., Botstein,D., But-
ler,H., Cherry, J.M., Davis, A.P., Dolinski,K., Dwight,
S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel- Tarver,L.,
Kasarskis,A., Lewis,S., Matese, J.C., Richardson, J.E.,
Ringwald, M., Rubin, G. M., and Sherlock, G. (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., Roth-
fels, K., Senff-Ribeiro, A., Griss, J., Sevilla, C., Mat-
thews,L., Gong,C., Deng,C., Varusai,T., Ragueneau,E.,
Haider, Y., May, B., Shamovsky, V., Weiser, J., Brun-
son,T., Sanati,N., Beckman,L., Shao,X., Fabregat,A.,
Sidiropoulos,K., Murillo,J., Viteri,G., Cook,J., Shors-
er,S., Bader,G., Demir,E., Sander,C., Haw,R., Wu,G.,
Stein, L., Hermjakob, H., and D’Eustachio, P. (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., Hanspers, K., Miller, R. A., Digles, D.,
Lopes, E. N., Ehrhart, F., Dupuis, L. J., Winckers,
L.A., Coort, S.L., Willighagen, E.L., Evelo, C.T., Pico,
A.R., and Kutmon,M. (2021) WikiPathways: connect-
ing 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., Hannay,T., and Buetow, K.H. (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.,
Lindblad-Toh, K., Lander, E. S., and Kellis,M. (2005)
Systematic discovery of regulatory motifs in human
promoters and 3′ UTRs by comparison of several
ISAKOVA et al.1756
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
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 power-
ful 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., Kir-
pichnikov, M.P., and Gasparian, M.E. (2019) A new
efficient method for production of recombinant anti-
tumor cytokine TRAIL and its receptor-selective vari-
ant 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., Ortega, F., Otero, J.,
Cibelli, J. B., Moore, H., Fraga, M. F., and López-
Larrea,C. (2010) Epigenetic mechanisms regulate MHC
and antigen processing molecules in human embry-
onic 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., Bulstrode, N. W., Mad-
rigal, A., and Ferretti, P. (2016) MHC-class-II are ex-
pressed 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., Shu,
H.-B., and Li,S. (2023) PD-1 signaling negatively reg-
ulates 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) Glu-
tamine 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., and
Jeong, S. M. (2018) Mitochondrial glutamine metab-
olism 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-acety-
laspartylglutamate synthetase II synthesizes N-acetyl-
aspartylglutamylglutamate, 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., and Bartel, K.
(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 fluo-
rescence 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.,
Wallace, D.C., and Schaefer, P. M. (2024) Visualizing
subcellular changes in the NAD(H) pool size ver-
sus 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., Min,
H.-J., Heo,J., Chang, E.-J., Choi, K.-C., Shin, D.-M., and
Son,J. (2024) Glutamine-mediated epigenetic regula-
tion of cFLIP underlies resistance to TRAIL in pan-
creatic 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 en-
hances stemness of cancer stem cells in glioblastoma:
an invitro 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., Shender, V.O., Andreev, D.E., Kovalen-
ko, T.F., Arapidi, G.P., Shnaider, P.V., Kazakova, A.N.,
Latyshev, Y.A., Tatarskiy, V.V., Shtil, A.A., Moreau,P.,
Giraud,F., Li,C., Wang,Y., Rubtsova, M.P., Dontsova,
O. A., Condro, M., Ellingson, B. M., Shakhparonov,
M.I., Kornblum, H.I., Nakano,I., and Pavlyukov, M.S.
(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) Can-
cer stem cells (CSCs) in cancer progression and ther-
apy, J. Cell. Physiol., 234, 8381-8395, https://doi.org/
10.1002/jcp.27740.
46. Li,B., Cao,Y., Meng,G., Qian,L., Xu,T., Yan,C., Luo,O.,
Wang,S., Wei,J., Ding,Y., and Yu,D. (2019) Targeting
glutaminase 1 attenuates stemness properties in he-
patocellular carcinoma by increasing reactive oxygen
species and suppressing Wnt/beta-catenin pathway,
GLIOBLASTOMA SENSITIZATION BY GLUTAMINE DEPRIVATION 1757
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
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., Zeng, B.,
Yu, M., Zhou, Q., Lin, Q., Gao, W., Ye, H., Zhou, J.,
Li, Z., Liu, Y., and Chen, R. (2015) Inhibition of glu-
tamine 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 mitochondri-
al 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.,
Zheng,L., Miletic,H., Sakariassen, P.Ø., Weinstock,A.,
Wagner, A., Lindsay, S. L., Hock, A. K., Barnett, S. C.,
Ruppin,E., Mørkve, S.H., Lund-Johansen,M., Chalm-
ers, A. J., Bjerkvig, R., Niclou, S. P., and Gottlieb, E.
(2015) Glutamine synthetase activity fuels nucleo-
tide 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., and Li, Z.
(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., Wang, H.,
Yao, L., Wang, X., Hong, X., Wang, J., Aa, J., Sun,M.,
Wang,G., Liu,J., and Zhou,F. (2022) Therapeutic tar-
geting of glutamate dehydrogenase 1 that links met-
abolic reprogramming and Snail-mediated epithe-
lial-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 hemato-
poietic stem/progenitor cells, Cell Cycle (Georgetown,
Tex.), 3, 314-318.
53. Wei, Y., Chen, Q., Huang, S., Liu, Y., Li, Y., Xing, Y.,
Shi, D., Xu, W., Liu, W., Ji, Z., Wu, B., Chen, X., and
Jiang,J. (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.,
Diserens, A.‐C., and van Meir, E. G. (1999) Fre-
quent co‐alterations of TP53, p16/CDKN2A, p14
ARF
,
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., Torgomyan, H.G.,
Vorotelyak, E.A., and Zagaynova, E.V. (2019) Metabol-
ic 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., Kozlov, D., Krylov, D., Gavrina, A.,
Kaplin, V., Epifanov, E., Minaev, N., Bardakova, K.,
Solovieva, A., Timashev, P., Zagaynova, E., and
Kuznetsova,D. (2023) FLIM imaging revealed spon-
taneous 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., Mon-
temurro, N., Perrini, P., Santonocito, O. S., Gambac-
ciani,C., Snuderl,M., Pieri,F., Aquila,F., Farnesi,A.,
Naccarato, A. G., iacava, P., Cardarelli, F., Ferri, G.,
Mulholland, P., Ottaviani, D., Paiar, F., Liberti, G.,
Pasqualetti,F., Menicagli,M., Aretini, P., Signore,G.,
Franceschi, S., and Mazzanti, C. M. (2022) Metabol-
ic-imaging of human glioblastoma live tumors: a new
precision-medicine approach to predict tumor treat-
ment 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., Zolotova, A.S., and
Yashin, K. S. (2023) Measurement of patient-derived
glioblastoma cell response to temozolomide using flu-
orescence lifetime imaging of NAD(P)H, Pharmaceuti-
cals, 16, 796, https://doi.org/10.3390/ph16060796.
61. Kim, J. H., Lee, K. J., Seo, Y., Kwon, J.-H., Yoon, J. P.,
Kang, J. Y., Lee, H. J., Park, S. J., Hong, S. P., Cheon,
J.H., Kim, W.H., and Il Kim,T. (2018) Effects of met-
formin on colorectal cancer stem cells depend on al-
terations 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.,
Zhivotovsky, B., and Gogvadze, V. (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.
ISAKOVA et al.1758
BIOCHEMISTRY (Moscow) Vol. 89 No. 10 2024
64. Galluzzi, L., Vitale, I., Aaronson, S. A., Abrams, J. M.,
Adam, D., Agostinis, P., Alnemri, E. S., Altucci, L.,
Amelio, I., Andrews, D. W., Annicchiarico-Petruzzel-
li, M., Antonov, A. V., Arama, E., Baehrecke, E. H.,
Barlev, N. A., Bazan, N. G., Bernassola, F., Bertrand,
M. J. M., Bianchi,K., Blagosklonny, M.V., Blomgren,K.,
Borner, C., Boya, P., Brenner, C., Campanella, M.,
Candi, E., Carmona- Gutierrez, D., Cecconi, F., Chan,
F. K.-M., Chandel, N. S., Cheng, E. H., Chipuk, J. E.,
Cidlowski, J. A., Ciechanover, A., Cohen, G. M.,
Conrad,M., etal. (2018) Molecular mechanisms of cell
death: recommendations of the Nomenclature Com-
mittee 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.,
Park, S. R., Choi, Y. H., and Kim, G.-Y. (2017) Gluta-
mine deprivation sensitizes human breast cancer
MDA-MB-231 cells to TRIAL-mediated apoptosis, Bio-
chem. 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 in-
duces 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) Glu-
tamine 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 glu-
tamine 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., Ouelk-
dite, A., Gaggero, S., Cozzani, A., Tilmont, R., Chau-
vet,P., Gower,N., Sklavenitis-Pistofidis,R., Brinster,C.,
Thuru, X., Touil, Y., Quesnel, B., Mitra, S., Ghobrial,
I.M., Kluza,J., and Manier,S. (2024) MYC dependen-
cy in GLS1 and NAMPT is a therapeutic vulnerabili-
ty 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., Zamporlini, F., Piacente, F., Moschoi, R.,
Raffaelli,N., Quattrone,A., Nencioni,A., Peyron, J.-F.,
and Provenzani,A. (2018) Cancer cell metabolic plas-
ticity allows resistance to NAMPT inhibition but in-
variably induces dependence on LDHA, Cancer Me-
tab., 6, 1, https://doi.org/10.1186/s40170-018-0174-7.
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