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Article
Delta-Tocotrienol Modulates Glutamine Dependence
by Inhibiting ASCT2 and LAT1 Transporters in
Non-Small Cell Lung Cancer (NSCLC) Cells:
A Metabolomic Approach
Lichchavi Dhananjaya Rajasinghe, Melanie Hutchings and Smiti Vaid Gupta *
Department of Nutrition and Food Science, Wayne State University, Detroit, MI 48202, USA;
lichchavi.rajasinghe@wayne.edu (L.D.R.); dv2329@wayne.edu (M.H.)
*Correspondence: sgupta@wayne.edu; Tel.: +1-313-577-5565
Received: 30 November 2018; Accepted: 4 March 2019; Published: 13 March 2019
Abstract:
The growth and development of non-small cell lung cancer (NSCLC) primarily depends on
glutamine. Both glutamine and essential amino acids (EAAs) have been reported to upregulate mTOR
in NSCLC, which is a bioenergetics sensor involved in the regulation of cell growth, cell survival,
and protein synthesis. Seen as novel concepts in cancer development, ASCT2 and LAT transporters
allow glutamine and EAAs to enter proliferating tumors as well as send a regulatory signal to
mTOR. Blocking or downregulating these glutamine transporters in order to inhibit glutamine uptake
would be an excellent therapeutic target for treatment of NSCLC. This study aimed to validate
the metabolic dysregulation of glutamine and its derivatives in NSCLC using cellular 1H-NMR
metabolomic approach while exploring the mechanism of delta-tocotrienol (
δ
T) on glutamine
transporters, and mTOR pathway. Cellular metabolomics analysis showed significant inhibition in
the uptake of glutamine, its derivatives glutamate and glutathione, and some EAAs in both cell lines
with
δ
T treatment. Inhibition of glutamine transporters (ASCT2 and LAT1) and mTOR pathway
proteins (P-mTOR and p-4EBP1) was evident in Western blot analysis in a dose-dependent manner.
Our findings suggest that
δ
T inhibits glutamine transporters, thus inhibiting glutamine uptake into
proliferating cells, which results in the inhibition of cell proliferation and induction of apoptosis via
downregulation of the mTOR pathway.
Keywords:
cancer; mTOR; vitamin E; SLC1A5; tocotrienols; apoptosis; cell growth; cell transporters;
essential amino acids; ASCT2; glutaminolysis; alanine; glutathione; glutamate; lung;
bio actives; nutraceuticals
1. Introduction
Non-small cell lung cancer (NSCLC) presents itself as aggressive tumors arise from the airway
epithelial cells (majority) and interior parts of the lungs [
1
]. It remains one of the leading causes
of disease-related mortalities in the world. The current therapeutic options for NSCLC, which
include surgery, radiotherapy, and chemotherapy [
1
], have slightly improved NSCLC survival rate at
some developmental stages in both men and women. However, there has been a plateauing of the
overall five-year survival rate, hovering ~12–18% between the years 1975 and 2011 [
2
]. Also, several
studies report that there is a high probability of reoccurrence and development of resistance to drug
therapies in NSCLC after treatment with chemotherapeutic agents, surgical resection, and radiation
therapy [
3
]. This warrants efforts to identify novel therapeutic agents and targets for preventing and
treating NSCLC.
Metabolites 2019,9, 50; doi:10.3390/metabo9030050 www.mdpi.com/journal/metabolites
Metabolites 2019,9, 50 2 of 21
Research in nutrition-based modulation against diseases has opened up new horizons in cancer
prevention, contributing to drug discovery and development processes for numerous chronic diseases,
including cancer [
4
,
5
]. Most bioactive agents extracted from plants show minimum cell cytotoxicity
while simultaneously targeting multiple signaling pathways involved in cell growth, apoptosis,
invasion, angiogenesis, and metastasis in cancer cells [
6
,
7
]. Tocotrienols (
α
,
β
,
γ
, and
δ
), isomers
of vitamin E, are found in vegetable oils, including rice bran oil and palm oil, wheat germ, barley,
annatto, and certain other types of seeds, nuts, and grains [
8
]. They exert biological effects including
antiangiogenesis, antioxidant activities, and anticancer activities [
9
,
10
]. Our previous studies clearly
demonstrated that delta-tocotrienol (
δ
T) inhibits the proliferation and metastatic/invasion potential
while concurrently inducing apoptosis in NSCLC cells, in a dose-dependent manner [
11
]. We also
identified some of the probable molecular targets of
δ
T treatments on NSCLC [
11
–
13
]. Therefore,
δ
T is
multitargeted and can be considered a valuable potential approach to further investigate for treatment
of NSCLC.
Metabolomics, a novel, versatile, and comprehensive approach, can provide unbiased information
about metabolite concentrations, altered signaling pathways, and their interactions. Most current
cancer metabolomics studies focus on finding diagnostic biomarkers and understanding fundamental
mechanisms in cancer [
14
]. Nonetheless, this approach could also be used effectively for identifying
the efficacy of treatments [
15
]. The NSCLC metabolome is a potentially informative reflection of
the impact of the disease and its dynamics which could lead to promising developments in cancer
research, strongly geared toward the discovery of new biomarkers of disease onset, progression,
and effects of treatment regimens. Given that cancer cells, including NSCLC, show aberrant energy
metabolism [
16
,
17
], it is of interest to investigate the changes in energy metabolism in NSCLC cells
upon δT treatment, utilizing the global advantage of the metabolomic approach [18].
Glutamine plays a role as an indirect energy source in NSCLC, which produces ATP through
glutamine-driven oxidative phosphorylation [
19
]. Extra consumption of glutamine in tumors is used
for generating metabolic precursors for uncontrolled cell proliferation. These precursors include
elevated levels of nucleic acids, lipids, and proteins for cell proliferation [
20
], as well as increased GSH
production for cell death resistance [
21
]. Current literature provides further evidence that glutamine
in cancer facilitates exchange of EAAs (essential amino acids) with glutamine into proliferating cells
via glutamine transporters, which induces mTOR (mammalian target of rapamycin) activation in
NSCLC and other types of cancer [
22
,
23
]. Activated mTOR then promotes protein translation and
cell growth via activation of its downstream genes such as S6k1 and 4EBP1 [
24
]. Alanine, serine,
cysteine-preferring transporter 2 (ASCT2), also known as (SLC1A5), and bidirectional L-type amino
acid transporter 1 (LAT1) are the two primary transporters for glutamine uptake [
25
,
26
]. LAT1 enables
transport of the EAAs to improve cancer cell growth via mTOR-induced translations, and ASCT2
sustains the cytoplasmic amino acid pool to drive LAT1 function [
27
]. This collaboration of ASCT2
and LAT1 reduce apoptosis and enhance the energy production and cell growth via net delivery of
glutamine inside the cell [27].
A recent study reported that A549 and H1229 lung cancer cells show glutamine dependency,
and that deprivation of glutamine inhibits cell growth [
28
]. Decreases in glutamine uptake, cell cycle
progression, and mTORC1 pathway after inhibition of ASCT2 functionality by chemicals or shRNA
in vitro
was observed in prostate and pancreatic cancer cell lines [
29
]. Also, inhibition of LAT1 using
BCH (2-aminobicyclo-(2,2,1)-heptane-2-carboxylic acid) in H1395 lung cancer cell line reduced the
cellular leucine uptake and consequently inhibited mTOR pathway activity, which finally reduced cell
proliferation and viability [
30
]. Induction of apoptosis was also reported in hepatoma, hybridoma,
leukemia, myeloma, and fibroblast cells after glutamine deprivation [
31
,
32
]. Our preliminary
metabolomics studies showed that
δ
T treatments inhibited glutamine levels in A549 and H1299
cells. Also, in our previous studies, induction of apoptosis and inhibition of cell growth was observed
in A549 and H1299 cells in a dose-dependent manner after
δ
T treatments [
11
,
33
–
36
]. Therefore, the aim
of this study was to verify the metabolic dysregulation of glutamine and its derivatives upon
δ
T
Metabolites 2019,9, 50 3 of 21
treatment while investigating the effect of
δ
T on the expression of glutamine transporters (ASCT2 and
LAT1) and the mTOR pathway.
2. Results
2.1. δT Changes Metabolite Profiles in A549 and H1299 Cells
To investigate the changes in metabolism and metabolites with
δ
T intervention, supervised
OPLS-DA analysis was performed using NMR spectral data acquired from intracellular cell lysate.
The OPLS-DA score plot of cellular NMR metabolic profile resulting from 30
µ
M
δ
T treated and control
cells lines are shown in Figure 1A. The OPLS-DA score plot exhibited clear separation between control
and treatment groups in A549 cells and H1299 cells with
δ
T treatment; the high Q2 and R2 values
indicate a considerable difference in the cellular metabolic profile of treated cells compared to control
cells while validating the model that we used for OPLS-DA analysis.
To identify the metabolites represented in the NMR spectral regions (bins) that varied significantly
between control and treatment groups, the corresponding loading S-Line plot from the OPLS-DA
model was generated. Figure 1B shows a representative S-Line plot corresponding to the score plot
of Figure 1. These bin numbers were further analyzed to identify the significant metabolites (using
Chenomx) that contributed to the separation of the control and treatment groups seen in the OPLS-DA
model. Based on the analysis of S-Line plot bin numbers, the key bin numbers responsible for the
differences could be attributed to glutamine, glutamate and glutathione, and some amino acids in both
cell lines.
Metabolites 2018, 8, x 14 of 21
derivatives upon δT treatment while investigating the effect of δT on the expression of glutamine
transporters (ASCT2 and LAT1) and the mTOR pathway.
2. Results
2.1. δT Changes Metabolite Profiles in A549 and H1299 Cells
To investigate the changes in metabolism and metabolites with δT intervention, supervised
OPLS-DA analysis was performed using NMR spectral data acquired from intracellular cell lysate.
The OPLS-DA score plot of cellular NMR metabolic profile resulting from 30 µM δT treated and
control cells lines are shown in Figure 1A. The OPLS-DA score plot exhibited clear separation
between control and treatment groups in A549 cells and H1299 cells with δT treatment; the high Q2
and R2 values indicate a considerable difference in the cellular metabolic profile of treated cells
compared to control cells while validating the model that we used for OPLS-DA analysis.
To identify the metabolites represented in the NMR spectral regions (bins) that varied
significantly between control and treatment groups, the corresponding loading S-Line plot from the
OPLS-DA model was generated. Figure 1B shows a representative S-Line plot corresponding to the
score plot of Figure 1. These bin numbers were further analyzed to identify the significant metabolites
(using Chenomx) that contributed to the separation of the control and treatment groups seen in the
OPLS-DA model. Based on the analysis of S-Line plot bin numbers, the key bin numbers responsible
for the differences could be attributed to glutamine, glutamate and glutathione, and some amino
acids in both cell lines.
(A)
Figure 1. Cont.
Metabolites 2019,9, 50 4 of 21
Metabolites 2018, 8, x 15 of 21
(B)
Figure 1. OPLS-DA analysis of metabolome of lung cancer cell lines after treating with/without δT for
72 h. (A) OPLS-DA Scores plot based on the cellular metabolic profiling of lung cancer cell lines, namely
A549 (Top) and H1299 (Bottom); the 30 µM treatment (Yellow) and control (green) were generated using
SIMCA+ software; the results indicated that cellular metabolic profiling of lung cancer cell lines was
significantly changed after δT treatment for 72 h. (B) The S-Line plots of OPLS-DA analysis of A549 (top)
and H1299 (Bottom) from treatment (30 µM) and control (0 µM) cells. The key metabolites that changed
significantly are marked on the S-Line plot and include (1) leucine, (2) glutamine, (3) glutamate, (4)
glutathione, (5) lactate, (6) taurine, and (7) formate.
2.2. Quantification of Metabolites Reveals That δT Alters Glutamine Metabolism
Chenomx 7.6 Suite NMR software was used to probe the metabolome profiles in the treatment
and control groups. 1H-NMR spectra provided information on over 45 metabolites (both cell lines),
including amino acids, intermediates of the tricarboxylic acid cycle (TCA), energy molecules, and
nucleic acid associated molecules (Table 1).
The table shows the detailed results including p-values, mean and standard deviation from the
t-test for the groups (with or without 30 µM of δT treatment) tested. Among the metabolites that were
significantly different in concentration in the δT treated vs. control cells, we identified several
metabolites from the glutamine metabolism and related pathways that were significantly decreased
(p < 0.05) in the treatment group as compared to controls. In addition, we found that metabolites such
as leucine and some essential amino acids had significantly lower concentrations in both cell lines
after δT treatment. These essential amino acids include isoleucine, leucine, lysine, methionine, and
tryptophan. Moreover, the metabolites related to cell proliferation such as 2-oxoglutarate, citrate,
succinate, malate, aspartame, ATP, ADP, NADPH, and uracil significantly decreased (p < 0.05) in the
treatment group as compared to controls (Table 1).
Heatmap analysis from MetaboAnalyst 3.0 revealed that A549 and H1299 cell lysates had similar
changing trends in metabolites of δT treated groups versus control (Figure 2A), which suggests that
Figure 1.
OPLS-DA analysis of metabolome of lung cancer cell lines after treating with/without
δ
T for
72 h. (
A
) OPLS-DA Scores plot based on the cellular metabolic profiling of lung cancer cell lines, namely
A549 (Top) and H1299 (Bottom); the 30
µ
M treatment (Yellow) and control (green) were generated
using SIMCA+ software; the results indicated that cellular metabolic profiling of lung cancer cell lines
was significantly changed after
δ
T treatment for 72 h. (
B
) The S-Line plots of OPLS-DA analysis of A549
(top) and H1299 (Bottom) from treatment (30
µ
M) and control (0
µ
M) cells. The key metabolites that
changed significantly are marked on the S-Line plot and include (1) leucine, (2) glutamine, (3) glutamate,
(4) glutathione, (5) lactate, (6) taurine, and (7) formate.
2.2. Quantification of Metabolites Reveals That δT Alters Glutamine Metabolism
Chenomx 7.6 Suite NMR software was used to probe the metabolome profiles in the treatment
and control groups. 1H-NMR spectra provided information on over 45 metabolites (both cell
lines), including amino acids, intermediates of the tricarboxylic acid cycle (TCA), energy molecules,
and nucleic acid associated molecules (Table 1).
The table shows the detailed results including p-values, mean and standard deviation from the
t-test for the groups (with or without 30
µ
M of
δ
T treatment) tested. Among the metabolites that
were significantly different in concentration in the
δ
T treated vs. control cells, we identified several
metabolites from the glutamine metabolism and related pathways that were significantly decreased
(
p< 0.05
) in the treatment group as compared to controls. In addition, we found that metabolites
such as leucine and some essential amino acids had significantly lower concentrations in both cell
lines after
δ
T treatment. These essential amino acids include isoleucine, leucine, lysine, methionine,
and tryptophan. Moreover, the metabolites related to cell proliferation such as 2-oxoglutarate, citrate,
succinate, malate, aspartame, ATP, ADP, NADPH, and uracil significantly decreased (p< 0.05) in the
treatment group as compared to controls (Table 1).
Metabolites 2019,9, 50 5 of 21
Table 1.
List of metabolite concentrations determined using NMR in A549 (A) H1299 (B) cells. p-values
less than 0.05 were considered statistically significant for univariate analysis. Treatment column
indicates the samples with the 30 µM treatment of δT. All the concentrations are reported in µM.
(A)
Metabolite Name Mean ±SD
(Control)
Mean ±SD
(Treatment) p-Value Fold Changes
Control/Trt
Amino Acids
Aspartate 102.3 ±11.9 55.9 ±4.7 0.0016 1.8
Glutamate 80.8 ±7.9 48.7 ±4.7 0.0019 1.7
Leucine 33.7 ±4.1 17 ±3.7 0.0030 2.0
Glycine 33.1 ±1.2 20.4 ±4.2 0.0035 1.6
Alanine 31 ±1.4 19.8 ±3.9 0.0045 1.6
Glutamine 99.9 ±6.7 64.7 ±13.3 0.0073 1.5
Histidine 54 ±8.4 85.9 ±31.3 0.0815 0.6
Asparagine 116.9 ±16.2 54.5 ±13.1 0.0033 2.1
Taurine 90.3 ±19.9 78.2 ±26.8 0.2822 1.2
Valine 23.8 ±1.4 21.6 ±6.3 0.2878 1.1
Tryptophan 81.3 ±15 72.7 ±28.7 0.3340 1.1
Proline 51.9 ±49.3 63.7 ±25.7 0.3659 0.8
Lysine 41.6 ±22.8 37.2 ±6.1 0.4075 1.1
Isoleucine 31.5 ±9.9 30.6 ±7 0.4499 1.0
Methionine 5.8 ±5.3 5.5 ±3.4 0.4653 1.1
Arginine nd nd
Intermediate of TCA Cycle and Energy Metabolism
Lactate 138.5 ±5.6 99.9 ±3.6 0.0003 1.4
2-Oxoglutarate 43.6 ±3.3 29.3 ±4.7 0.0061 1.5
AMP 32.1 ±5 45 ±1.7 0.0063 0.7
Glutaric acid monomethyl ester 17.8 ±6.4 34 ±2.8 0.0077 0.5
Malate 90.2 ±10.7 48.7 ±10.3 0.0111 1.9
Succinate 9.3 ±2.6 5.2 ±2.8 0.0645 1.8
Glucose 119.1 ±53.4 187.3 ±63.7 0.1139 0.6
ADP 47.8 ±8.3 40.8 ±4.8 0.1370 1.2
Citrate 42.4 ±3.8 35.6 ±11.6 0.1959 1.2
NADH 38.4 ±3.5 43.4 ±16 0.3040 0.9
NADPH 47 ±6.3 51.3 ±12.5 0.3118 0.9
ATP 42.2 ±5.4 42.9 ±11.3 0.4653 1.0
Nucleic acid Associataed Metabolites
Uracil 98 ±14.1 60.1 ±24 0.0387 1.6
UDP-N-Acetylglucosamine 6.9 ±2.1 3.9 ±3.4 0.1266 1.8
Other
Glutathione 69.6 ±2.1 41.7 ±6.7 0.0011 1.7
Citrulline 81.9 ±5.1 63.9 ±13 0.0438 1.3
Cystine 81.4 ±6.3 58.4 ±19 0.0582 1.4
N-Acetylglucosamine 21.9 ±9.3 12.8 ±5.2 0.1065 1.7
Formate 294.3 ±68.5 312.8 ±8.9 0.3334 0.9
Fumarate 25 ±3.2 27.7 ±5 0.2363 0.9
Metabolites 2019,9, 50 6 of 21
Table 1. Cont.
(B)
Metabolite Name Mean ±SD
(Control)
Mean ±SD
(Treatment) p-Value Fold Changes
Control/Trt
Amino Acids
Aspartate 105.5 ±3.5 77.4 ±4.3 0.0010 1.4
Glutamate 80.1 ±5.7 49.3 ±6.2 0.0033 1.6
Leucine 31.8 ±1.3 18.3 ±0.8 <0.0001 1.7
Glycine 28.2 ±4.7 18.1 ±3.2 0.0561 1.6
Alanine 28.8 ±2.2 18.2 ±2.3 0.0044 1.6
Glutamine 75.3 ±5.1 53.7 ±8.4 0.0177 1.4
Histidine ND ND
Asparagine 105 ±21 84 ±23.3 0.1986 1.3
Taurine ND ND
Valine 28.8 ±4.9 21.7 ±5.6 0.1706 1.3
Tryptophan 36.8 ±2 17.8 ±11.4 0.0401 2.1
Proline 90.2 ±39.3 74.3 ±34.9 0.3453 1.2
Lysine 38.8 ±11.3 19.4 ±7.1 0.0547 2
Isoleucine 37.2 ±4.9 23.8 ±2.7 0.0138 1.6
Methionine 8.7 ±0.8 6.7 ±1.9 0.1247 1.3
Arginine 43.8 ±2.7 28.4 ±6.6 0.0189 1.5
Intermediate of TCA Cycle and Energy Metabolism
Lactate 125.8 ±7.3 122 ±15.4 0.3857 1
2-Oxoglutarate 32.5 ±7.9 17.2 ±1.5 0.0272 1.9
AMP 27.5 ±0.2 13.7 ±2 0.0003 2
Glutaric acid monomethyl ester 27.4 ±0 20.6 ±7.4 1.3
Malate 130.9 ±7.8 84.7 ±9 0.0027 1.5
Succinate 13.9 ±1.7 5.3 ±3.8 0.0215 2.6
Glucose 196.4 ±50.1 147.1 ±19.4 0.1324 1.3
ADP 33.6 ±5.1 14.9 ±7.7 0.0227 2.3
Citrate 35.2 ±0.8 25.6 ±4.3 0.0183 1.4
NADH 65.3 ±11.7 43.7 ±30.7 0.2024 1.5
NADPH 48.6 ±11.1 38.1 ±23.5 0.2996 1.3
ATP 43.5 ±7.8 22.2 ±5.5 0.0171 2
Nucleic acid Associated Metabolites
Uracil 88.5 ±11.9 40.2 ±16.3 0.0139 2.2
UDP-N-Acetylglucosamine ND
Other
Glutathione 42.3 ±4.5 28 ±6.5 0.0319 1.5
Citrulline 65.4 ±20.6 53.4 ±25.4 0.3156 1.2
Cystine 61 ±7.2 26.3 ±14.1 0.0338 2.3
N-Acetylglucosamine
Fumarate
Formate 354.5 ±90.9 346.7 ±41 0.4585 1
Tyrosine 12.9 ±0.6 67.8 ±9.1 0.0134 0.2
Metabolites 2019,9, 50 7 of 21
Heatmap analysis from MetaboAnalyst 3.0 revealed that A549 and H1299 cell lysates had similar
changing trends in metabolites of
δ
T treated groups versus control (Figure 2A), which suggests that
the supplement of
δ
T impacts both cell lines in a similar manner. At the same time, our heatmap
results also revealed that control and treatment groups supplemented with
δ
T were clustered into two
major groups (Green and Red groups at the top of the Heatmap) which suggest clear separation in two
groups with their metabolites and also validates the separation in OPLS-DA analysis. The random
forest importance plot identified 15 metabolites key in classifying the data with aspartame, alanine,
leucine, glutamate glutathione, and glutamine having the most influence on classification (Figure 2B).
To further comprehend the biological relevance of the identified metabolites from Chenomx
analysis, we performed pathway analysis using MetaboAnalyst 3.0 software [
25
]. Some of the key
altered pathways identified from pathway analysis include lysine biosynthesis, purine metabolism,
alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism, citrate cycle (TCA
cycle), and pyruvate metabolism for both cell lines (Figure 3A).
Metabolites 2018, 8, x 16 of 21
the supplement of δT impacts both cell lines in a similar manner. At the same time, our heatmap
results also revealed that control and treatment groups supplemented with δT were clustered into
two major groups (Green and Red groups at the top of the Heatmap) which suggest clear separation
in two groups with their metabolites and also validates the separation in OPLS-DA analysis. The
random forest importance plot identified 15 metabolites key in classifying the data with aspartame,
alanine, leucine, glutamate glutathione, and glutamine having the most influence on classification
(Figure 2B).
To further comprehend the biological relevance of the identified metabolites from Chenomx
analysis, we performed pathway analysis using MetaboAnalyst 3.0 software [25]. Some of the key
altered pathways identified from pathway analysis include lysine biosynthesis, purine metabolism,
alanine, aspartate and glutamate metabolism, glutamine and glutamate metabolism, citrate cycle
(TCA cycle), and pyruvate metabolism for both cell lines (Figure 3A).
Figure 2. Cont.
Metabolites 2019,9, 50 8 of 21
Metabolites 2018, 8, x 17 of 21
(A)
Figure 2. Cont.
Metabolites 2019,9, 50 9 of 21
Metabolites 2018, 8, x 18 of 21
(B)
Figure 2. Hierarchical clustering analysis of δT-altered metabolites (Heatmap) and contribution of
metabolites in A549 and H1299. The metabolites, quantified with Chenomx software analysis of NMR
spectra of A549 and H1299 cells after incubating with or without δT for 72 h, were used to generate
the heat map (A) using Metaboanalyst software. Each column represents a sample, and each row
represents the expression profile of metabolites. Blue color represents a decrease, and red color an
increase. The very top row with green color indicates the control samples and red color row indicates
the samples with the 30 µM treatment of δT. Random Forest (B) showed in bottom graphs identifies
the significant features. The features are ranked by the mean decrease in classification accuracy when
they are permuted.
Figure 2.
Hierarchical clustering analysis of
δ
T-altered metabolites (Heatmap) and contribution of
metabolites in A549 and H1299. The metabolites, quantified with Chenomx software analysis of NMR
spectra of A549 and H1299 cells after incubating with or without
δ
T for 72 h, were used to generate
the heat map (
A
) using Metaboanalyst software. Each column represents a sample, and each row
represents the expression profile of metabolites. Blue color represents a decrease, and red color an
increase. The very top row with green color indicates the control samples and red color row indicates
the samples with the 30
µ
M treatment of
δ
T. Random Forest (
B
) showed in bottom graphs identifies
the significant features. The features are ranked by the mean decrease in classification accuracy when
they are permuted.
Metabolites 2019,9, 50 10 of 21
Metabolites 2018, 8, x 19 of 21
(A)
(B)
Figure 3. The most predominant altered metabolic pathways (A) and top 25 metabolites correlated
with glutamine (B). Summary of the altered metabolism pathways (A) after treating with/without δT
for 72 h, as analyzed using MetaboAnalyst 3.0. The size and color of each circle was based on pathway
impact value and p-value, respectively. Circles, larger and higher along the Y axis, show higher impact
of pathway on the organism. The top 25 metabolites, correlating with glutamine level (B) after treating
with/without δT for 72 h. X-axis shows maximum correlation; pink color shows positive correlation
whereas blue shows negative correlation.
As random forest importance plot and pathway analysis indicate that glutamine-based
metabolites play a significant contribution to glutamine metabolism and related pathways, correlation
between other metabolites were assessed using Pearson correlation analysis to validate the relationship
between glutamine and metabolites in other pathways. Interestingly, nearly 20 metabolites showed
more than (>0.7) correlation with glutamine and metabolites belonging to the key impaired pathways
identified from pathway analysis using MetaboAnalyst 3.0 software. The metabolites in glutamine and
glutamate metabolism include glutathione, glutamate, 2-oxoglutarate which show a 0.9, 0.7, and 0.6
correlation in A549 and 0.8, 0.8, and 0.8 correlation in H1299 (Figure 3B).
2.3. δT Inhibits Glutamine Transporters (LAT-1 and ASCT2) and the mTOR Pathway in A549 and H1299
Cells
Figure 3.
The most predominant altered metabolic pathways (
A
) and top 25 metabolites correlated
with glutamine (
B
). Summary of the altered metabolism pathways (
A
) after treating with/without
δ
T
for 72 h, as analyzed using MetaboAnalyst 3.0. The size and color of each circle was based on pathway
impact value and p-value, respectively. Circles, larger and higher along the Yaxis, show higher impact
of pathway on the organism. The top 25 metabolites, correlating with glutamine level (
B
) after treating
with/without
δ
T for 72 h. X-axis shows maximum correlation; pink color shows positive correlation
whereas blue shows negative correlation.
As random forest importance plot and pathway analysis indicate that glutamine-based metabolites
play a significant contribution to glutamine metabolism and related pathways, correlation between
other metabolites were assessed using Pearson correlation analysis to validate the relationship between
glutamine and metabolites in other pathways. Interestingly, nearly 20 metabolites showed more than
(>0.7) correlation with glutamine and metabolites belonging to the key impaired pathways identified
from pathway analysis using MetaboAnalyst 3.0 software. The metabolites in glutamine and glutamate
metabolism include glutathione, glutamate, 2-oxoglutarate which show a 0.9, 0.7, and 0.6 correlation
in A549 and 0.8, 0.8, and 0.8 correlation in H1299 (Figure 3B).
Metabolites 2019,9, 50 11 of 21
2.3. δT Inhibits Glutamine Transporters (LAT-1 and ASCT2) and the mTOR Pathway in A549 and
H1299 Cells
Metabolomic analysis and subsequent quantification of metabolites using Chenomx NMR suite
(Edmonton, AB, Canada) revealed the potent effect of
δ
T on glutamine metabolism, downstream
metabolites of glutamine and essential amino acids (Figures 1and 2, Table 1). Current literature
provides evidence that glutamine uptake and some essential amino acids, including leucine,
are associated with the activation of the mTOR pathway [
37
]. Thus, Western blot analysis was
performed to investigate the effect of
δ
T on the mTOR pathway and glutamine transporters. Upon
intervention with
δ
T (30
µ
M), the glutamine transporters (LAT-1 and ASCT2) and key mTOR
pathway proteins (P-mTOR and p-4EBP-1) were found to be inhibited, relative to the untreated
controls (Figure 4).
Metabolites 2018, 8, x 20 of 21
Metabolomic analysis and subsequent quantification of metabolites using Chenomx NMR suite
(Edmonton, AB, Canada) revealed the potent effect of δT on glutamine metabolism, downstream
metabolites of glutamine and essential amino acids (Figures 1 and 2, Table 1). Current literature
provides evidence that glutamine uptake and some essential amino acids, including leucine, are
associated with the activation of the mTOR pathway [37]. Thus, Western blot analysis was
performed to investigate the effect of δT on the mTOR pathway and glutamine transporters.
Upon intervention with δT (30 µM), the glutamine transporters (LAT-1 and ASCT2) and key
mTOR pathway proteins (P-mTOR and p-4EBP-1) were found to be inhibited, relative to the
untreated controls (Figure 4).
(A)
Figure 4. Cont.
Metabolites 2019,9, 50 12 of 21
Metabolites 2018, 8, x 21 of 21
(B)
Figure 4. δT inhibits glutamine transporters (LAT-1 and ASCT2) and the mTOR pathway in A549 and
H1299 cells. (A) The expressions of LAT-1, ASCT2, p-mTOR, mTOR, p-4EBP-1, 4EBP1, and β-actin
proteins were detected by Western blot analysis in A549 and H1299 after treating with 0 µM and 30 µM
concentrations of δT for 72 h. (B) The fate of glutamine uptake in A549 and H1299 involving metabolites
(purple), associated key proteins (pink), and functions (orange). Glutamine in cancer facilitates
exchanging of EAAs (essential amino acids) into proliferating cells via glutamine transporters (LAT1
and ASCT2), which induces mTOR activation in A549 and H1299. Activated mTOR then promotes
protein translation and cell growth via activation of its downstream genes 4EBP1. The black arrows
indicate pathway direction, while the red downward arrows indicate inhibition.
3. Discussion
In this study, we used multivariate analysis of NMR spectra and NMR quantification data to
observe differences in the intracellular metabolomes. We discovered clear differences in the
intracellular metabolomes, and subsequently the contributing metabolites, of the control and δT treated
cells using OPLS-DA and Heat map analysis (Figures 1 and 2A). Also, we observed a minor difference
in the results obtained through multivariate analysis of NMR spectra and NMR quantification variation
in this analysis which is common in metabolomic data sets. This type of variation is well documented
in several publications in the current literature [6]. Most variations arise from the metabolites present
in very low concentrations. In addition, metabolites whose resonances yield a very high number of
overlapping peaks also suffer from variations in quantitation [6]. The two different methods were
therefore used in conjunction to verify the data.
Previously, using histone ELISA and ANNEXIN V stain-based flow cytometry analysis, we
reported that the 10 to 30 µM range of δT was not necrotic to A549 and H1299 cells, and that it induced
apoptosis in a dose-dependent manner [11,12]. Also, using MTS and clonogenic assays in the
previous studies, we demonstrated that 30 µM of δT inhibited cell growth significantly in the A549
and H1299 cells lines [12]. Other metabolomics investigations have also reported changes in
metabolism after inducing apoptosis in different cancer types, namely leukemia cell lines [38]. Our
data suggests that metabolite changes in the control vs. δT treated lung cancer cell populations are a
result of induction of apoptosis after δT treatment.
Figure 4. δ
T inhibits glutamine transporters (LAT-1 and ASCT2) and the mTOR pathway in A549 and
H1299 cells. (
A
) The expressions of LAT-1, ASCT2, p-mTOR, mTOR, p-4EBP-1, 4EBP1, and
β
-actin
proteins were detected by Western blot analysis in A549 and H1299 after treating with 0
µ
M and 30
µ
M
concentrations of
δ
T for 72 h. (
B
) The fate of glutamine uptake in A549 and H1299 involving metabolites
(purple), associated key proteins (pink), and functions (orange). Glutamine in cancer facilitates
exchanging of EAAs (essential amino acids) into proliferating cells via glutamine transporters (LAT1
and ASCT2), which induces mTOR activation in A549 and H1299. Activated mTOR then promotes
protein translation and cell growth via activation of its downstream genes 4EBP1. The black arrows
indicate pathway direction, while the red downward arrows indicate inhibition.
3. Discussion
In this study, we used multivariate analysis of NMR spectra and NMR quantification data to
observe differences in the intracellular metabolomes. We discovered clear differences in the intracellular
metabolomes, and subsequently the contributing metabolites, of the control and
δ
T treated cells using
OPLS-DA and Heat map analysis (Figures 1and 2A). Also, we observed a minor difference in the
results obtained through multivariate analysis of NMR spectra and NMR quantification variation in
this analysis which is common in metabolomic data sets. This type of variation is well documented in
several publications in the current literature [
6
]. Most variations arise from the metabolites present
in very low concentrations. In addition, metabolites whose resonances yield a very high number of
overlapping peaks also suffer from variations in quantitation [
6
]. The two different methods were
therefore used in conjunction to verify the data.
Previously, using histone ELISA and ANNEXIN V stain-based flow cytometry analysis,
we reported that the 10 to 30
µ
M range of
δ
T was not necrotic to A549 and H1299 cells, and that
it induced apoptosis in a dose-dependent manner [
11
,
12
]. Also, using MTS and clonogenic assays
in the previous studies, we demonstrated that 30
µ
M of
δ
T inhibited cell growth significantly in the
A549 and H1299 cells lines [
12
]. Other metabolomics investigations have also reported changes in
metabolism after inducing apoptosis in different cancer types, namely leukemia cell lines [
38
]. Our data
Metabolites 2019,9, 50 13 of 21
suggests that metabolite changes in the control vs.
δ
T treated lung cancer cell populations are a result
of induction of apoptosis after δT treatment.
The role of natural dietary components in cancer growth and progression has become a very
popular subject with minimum effect or no effect on normal cells. Several cell culture studies showed
that
δ
T was not causing apparent impairment towards the noncancerous cell lines, although it
significantly effects different cancer cell types, including lung cancer. For instance, Human Fetal
Lung Fibroblast Cells treated with 100
µ
m or higher of
δ
T did not show any toxic effect including
induction of apoptosis and DNA damage [
18
]. In another study, 10
µ
M DT3, a lower dose than our
treatment, was determined to be nontoxic, and enhanced cell viability and proliferative potential in
the human lung fibroblast cell lines MRC-5 and HFL1, as shown by WST-1 and clonogenic assays [
39
].
In addition, Immortal human pancreatic duct epithelial cell lines did not show any significant inhibitory
effect on cell proliferation and cell cycle progression when they were incubated with
δ
T [
40
]. Similarly,
normal human melanocytes treated with
δ
T (5–20
µ
g/mL) for 24 h or 48 h did not affect cell growth at
both time intervals [
41
]. Preclinical and clinical evidence also supports the use of
δ
T to reduce tumor
growth with no effects on healthy humans or animals, making δT attractive compounds. No adverse
effects were observed upon administration of 300 mg/kg dose of
δ
T, in any tissues or organs of
mice [
42
]. In humans,
δ
T can be safely administered at doses up to 1600 mg twice daily [
43
]. In another
study with osteopenic women, supplementation for 12 weeks did not affect body composition, physical
activity, quality of life, or intake of macro- and micronutrients [
44
]. All of the aforementioned studies
used
δ
T concentrations above 30
µ
M that we used for this study, and it is obvious that
δ
T does not
affect healthy cells including human fetal lung fibroblast cells. Therefore, a control arm of normal lung
cells with expressed or unexpressed LAT1 and/or ASCT2 were not included in our study design.
Further, LAT1 or ASCT2 transporters with cancer is nowadays well-assessed [
9
]. Overexpression
of LAT1 is well described in many human cancers and it certainly relates to metabolic changes occurring
in cancer development and progression [
45
]. LAT-1 is expressed in cancers of most human tissues
according to GENT database [
46
], which suggests an important role of LAT-1 expression on cancer
development. In contrast, it is poorly expressed or, in some cases, absent in most of the corresponding
noncancer human tissues [
46
]. In the immunohistochemistry analysis of the normal lung, LAT1 protein
was identified only on granular regions in the cytoplasm of chondrocytes of the bronchial cartilage,
serous cells of the bronchial glands, and alveolar macrophages within the normal lung, whereas
the expression was zero for nonciliated bronchiolar epithelial cells (Clara cells), goblet cells of the
bronchus, mucinous cells of the bronchial glands, and alveolar type I or type II cells [
47
]. In the same
study, expression of LAT1 protein appeared in the cytoplasm of bronchial surface epithelial cells as
a single nodular spot, which was considered to represent an intracellularly localized nonfunctional
protein [
47
]. ASCT2 transporters also are poorly expressed or, in some cases, absent in most of the
corresponding noncancer human tissues according to GENT database [
46
]. Hassanein et al. identified
ASCT2 transporters expressed in stage I NSCLC when compared to matched controls using shotgun
proteomic analysis [
48
]. In addition, ASCT2 deficient mice showed regular functions such as normal
B-cell development, proliferation, and antibody production [
49
]. Therefore, control arms of normal
lung cells that are expressed or unexpressed (LAT1 and ASCT2) was also not included in our study
design as there was a minimum expression and/or functionality observed for LAT1 and ASCT2 in
other tissues and noncancerous tissues.
A significant reduction of glutamine, glutamate, GSH and 2-oxoglutarate after treating with 30
µ
M
of
δ
T on NSCLC cell lines was observed (Table 1). The key aberrant pathways identified using the
pathway analysis tool include glutamate and glutamine, alanine, aspartate, glutathione metabolism,
and the TCA cycle (Figure 3). In addition, the metabolites identified from these pathways show a
strong correlation with glutamine levels (Figure 3B). Further, glutamine and its related metabolites
were identified in the S-plot of OPLS-DA analysis and the Random Forest importance plot as the key
players causing the separation, reflecting the differences in their metabolomic profiles (Figures 1and
2B). Glutamine deprivation has been shown to induce apoptosis in hepatoma, hybridoma, leukemia,
Metabolites 2019,9, 50 14 of 21
myeloma, and fibroblast cells [
50
]. In contrast, increased levels of glutamine were detected in lung
cancer tissue especially in NSCLC when compared to other types of cancer, such as colon or stomach
cancer [
47
]. Glutamine dependency has been reported in H1299 and A549 cells [
28
]. Our findings
strongly suggest the beneficial impact of
δ
T on glutamine and related pathways in non-small cell lung
cancer cells.
Considering metabolism of glutamine (Figure 5), one of its major roles in cancer cell proliferation
is to replenish the TCA cycle intermediates removed by the process called glutaminolysis,
and GSH synthesis [
30
,
31
]. In the process of glutaminolysis, the glutaminase enzyme (GLS1/2)
catalyzes the conversion of glutamine to glutamic acid and the subsequent conversion of
glutamate to
α
-ketoglutarate (2-oxoglutarate), catalyzed by glutamate dehydrogenase (GLUD) [
32
].
Aminotransferase also catalyzes the reaction from glutamate and oxaloacetate to aspartate or alanine
and
α
-ketoglutarate. In this study, a significant reduction of glutamine, glutamate, and TCA cycle
intermediates after treating with 30
µ
M of
δ
T was observed, which is an indicator of reduced energy
metabolism (Figure 5). In cancer cells, the enhanced production of 2-oxoglutarate and glutamate from
glutamine metabolism can be observed, as it helps to maintain the citric acid cycle intermediate for
energy production [
32
]. Glucose and glutamine provide substrates for macromolecular synthesis
supplying both ATP and carbon skeletons in cancer cells [
29
]. This supports uncontrolled cell
proliferation in cancer cells and requires a large number of macromolecules to create new biomass,
including DNA, proteins, and lipids [
28
]. Our data suggests that by decreasing the availability of
glutamine,
δ
T retards this process, thereby leading to inhibition of uncontrolled cell proliferation in
A549 and H1299 as reported in our previous studies [11,12,35].
Metabolites 2018, 8, x 29 of 21
Figure 5. Glutamine metabolism and the effect of δT on glutamine metabolism in A549 and H1299
cells. Glutamine mainly replenishes the TCA cycle intermediates and GSH synthesis in cancer cell
proliferation. In the process, glutaminase enzymes (GLS1/2) catalyzes the conversion of glutamine to
glutamic acid and the subsequent conversion of glutamate to α-ketoglutarate (α-kG), catalyzed by
glutamate dehydrogenase (GLUD) and amino transferase. This process supports for uncontrolled cell
proliferation in cancer cells and requires a large number of macromolecules to create new biomass,
including DNA, proteins, and lipids. The black arrows indicate the pathway’s direction, while the red
downward arrows indicate the inhibition of metabolites as an effect of δT treatment.
Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1. Table S1: List of
metabolite concentrations determined using Chenomx NMR Suite in A549 cells. Table S2: List of metabolite
concentrations determined using Chenomx NMR Suite in H1299 cells. Figure S1: Effects of δT on A549 (A) and
H1299 (B) on the metabolome of lung cancer cell lines.
Author Contributions: Conceptualization, L.D.R. and S.V.G.; Methodology, L.D.R.; Software, L.D.R.; Formal
Analysis, L.D.R. and M.H.; Investigation, S.V.G.; Resources, S.V.G.; Data Curation, L.D.R.; Writing—Original
Draft Preparation, L.D.R. Writing—Review and Editing, L.D.R., M.H., and S.V.G; Supervision, S.V.G.; Funding
Acquisition, S.V.G.
Funding: This research was conducted using intra-mural funding.
Acknowledgments: We thank Alexander Buko, Vice President Business and Product Development at Human
Metabolome Technologies America for comments on results and language and the assistance with additional
bioinformatics methods that greatly improved the manuscript. We are also immensely grateful to Dr. Bashar
Ksebati for NMR instrument support.
Conflicts of Interest: The authors declare no conflicts of interest
References
1. Society, A.C. Lung Cancer (Non-Small Cell) Availabe online:
http://www.cancer.org/acs/groups/cid/documents/webcontent/003115-pdf.pdf (accessed on 3/09/2019)
2. Society, A.C. Cancer Facts & Figures 2016; American Cancer Society: Atlanta, GA, USA, 2016.
3. Kelsey, C.R.; Clough, R.W.; Marks, L.B. Local Recurrence Following Initial Resection of NSCLC: Salvage Is
Possible with Radiation Therapy. Cancer J. 2006, 12, 283–288.
4. Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs over the 30 years from 1981 to 2010.
J. Nat. Prod. 2012, 75, 311–335, doi:10.1021/np200906s.
Figure 5.
Glutamine metabolism and the effect of
δ
T on glutamine metabolism in A549 and H1299
cells. Glutamine mainly replenishes the TCA cycle intermediates and GSH synthesis in cancer cell
proliferation. In the process, glutaminase enzymes (GLS1/2) catalyzes the conversion of glutamine
to glutamic acid and the subsequent conversion of glutamate to
α
-ketoglutarate (
α
-kG), catalyzed by
glutamate dehydrogenase (GLUD) and amino transferase. This process supports for uncontrolled cell
proliferation in cancer cells and requires a large number of macromolecules to create new biomass,
including DNA, proteins, and lipids. The black arrows indicate the pathway’s direction, while the red
downward arrows indicate the inhibition of metabolites as an effect of δT treatment.
Metabolites 2019,9, 50 15 of 21
Considering possible causes for the significant decrease in glutamine and its downstream
metabolites, we hypothesized that it may be due to inhibition of glutamine transporters. We thus
measured the protein levels of glutamine transporters, namely LAT1 and ASCT2, known to play a
fundamental role in glutamine uptake process in normal cell physiology. LAT-1 facilitates glutamine
efflux in exchange for the influx of leucine and other essential amino acids (EAA) across the cell
membrane; similarly, ASCT2 mediates uptake of neutral amino acids including glutamine [
51
].
Our observations from western blot analysis established that
δ
T treatments inhibit the expression of
LAT-1 and ASCT2 (Figure 4). We also quantified detectable EAA including leucine in cell lysates,
the concentration of which were decreased significantly after treating NSCLC cells with
δ
T by NMR
analysis. Inhibition of EAA in A549 and H1299 cells upon
δ
T treatment reflects function of LAT-1
which facilitate glutamine efflux in exchange for the influx of leucine and other essential amino acids
(EAA). This supports the beneficial effects of
δ
T on LAT1 transporters inside A549 and H1299 cells.
In addition to facilitating the transport of EAAs for protein synthesis, LAT1 and ASCT2 stimulate
the growth of cancer cells via mTOR [
27
,
52
,
53
]. In head and neck squamous cell carcinoma cell lines,
inhibition of the LAT-1 transporter using an inhibitor lowered the levels of phosphorylation of mTOR
and its downstream signaling molecules [
54
]. Thus, if the inhibition of glutamine transporters and
EAA uptake with
δ
T treatment is valid, it is logical to expect inhibition or lower activation of mTOR
pathway after treating with
δ
T in NSCLC. Indeed, we observed lower activation of mTOR along with
LAT-1 and ASCT2 after treating with
δ
T, using Western blot analysis, which illustrates that inhibition
of glutamine transporters affect the mTOR signaling pathway (Figure 4).
mTOR functions are mediated by two downstream proteins, the eukaryotic initiation factor 4E
(eIF4E)-binding protein 1 (4E-BP1) and p70 ribosomal S6 kinase 1 (p70S6K1, S6K1) (Figure 4) [
55
].
For further confirmation, we tested the expression levels of downstream genes of mTOR namely
P-4E-BP1. We observed the similar inhibitory effect on mTOR downstream proteins 4E-BP1suggesting
an inhibitory effect of glutamine transporters passing through mTOR to downstream pathway
(Figure 4). mTOR downstream proteins 4E-BP1 and S6K1 regulate F-actin reorganization,
focal adhesion formation, and tissue remodeling through the proteolytic digestion of extracellular
matrix via upregulation of matrix metalloproteinase 9 (MMP-9) [
56
]. Interestingly, in our previous
study, we observed that
δ
T reduced cell migration, invasion and adhesion in a dose- and
time-dependent manner, and inhibited MMP-9 expressions in NSCLC cells [
13
,
34
], which is an
additional supporting inhibitory function of δT.
Further, in the previous study, we demonstrated that
δ
T induces apoptosis in a dose-dependent
manner in NSCLC from Annexin based flow cytometry analysis and histone ELISA [12]. The current
literature also provides evidence to support the relationship between GSH and apoptosis. For instance,
GSH depletion in cancer cells induces apoptosis
in vitro
and
in vivo
[
57
]. Dalton TP et al. showed
GSH-depleted knockout mouse of
γ
-GCS died from massive apoptotic cell death [
58
]. Elevated levels
of GSH are also associated with apoptotic resistant phenotypes in several models of apoptosis in
previously reported studies [
59
,
60
], and GSH depletion by itself has been observed to either induce or
stimulate apoptosis [
59
,
61
]. GSH quantification, after treating with
δ
T in A549 and H1299 cells, shows
a clear decline in intercellular GSH levels in both cell lines (Table 1). The results reveal there may also
be a possible association between GSH levels and induction of apoptosis in NSCLC cells after treating
with δT.
4. Materials and Methods
4.1. Cell Culture and Treatment with δ-T
NSCLC cell lines A549 and H1299 were cultured in RPMI medium (Mediatech, Manassas, VA,
USA) supplemented with 10% fetal bovine serum and 1% penicillin and streptomycin in 5% CO
2
at
37
◦
C. The culture medium was renewed every 2 to 3 days. Adherent cells were detached by incubation
with trypsin-EDTA and centrifuged at 80
×
g. The treatment media was prepared by mixing
δ
T (<0.01%
Metabolites 2019,9, 50 16 of 21
DMSO as a vector) in the RPMI medium, whereas the control was treated only with RPMI media.
Three
δ
T solutions at concentrations of 10
µ
M, 20
µ
M, and 30
µ
M containing <0.01% DMSO were
chosen as the treatment concentration based on our previous studies.
δ
T was a gift from the American
River Nutrition for this study.
4.2. Intracellular Metabolite Extraction and Determination
We used a modified method which is explained in Saadat et al., 2018 [
62
]. In brief A549 and
H1299 lines were seeded at a density of 2
×
10
6
per 100-mm dish for 24 h, followed by replacement
of media absent or supplemented with different
δ
T concentrations (10
µ
M, 20
µ
M, and 30
µ
M) at
37
◦
C. Cells were then incubated for another 72 h before extracting metabolites. Before extracting
intracellular metabolites, existing culture media was removed on ice followed by washing twice with
ice-cold PBS. Two milliliters of ice-cold methanol was added while scraping with cell scrapers on ice.
The Petri dish was shaken for 5 min at 4
◦
C and ice-cold methanol was transferred into Eppendorf tubes.
The cell debris was removed by centrifugation and all the extraction solvents were readily removed
before NMR analysis by a Speed Vac at room temperature. Subsequently, the intracellular metabolites
powder was prepared by evaporating with methanol, and redissolving in 450
µ
L D
2
O containing
0.5
µ
M 2,2-Dimethyl-2-silapentane-5-sulfonic acid (DSS) as aspectral calibration standard and 10
µ
M
imidazole as a pH indicator. An additional Petri dish was prepared for each treatment/control with
the same conditions and cells collected from the additional petri dish were used for analyzing total
protein. The total protein quantifications include control-A549 (1.283 mg), 30
µ
M-A549 (1.099 mg),
control-H1299 (1.325 mg), and 30
µ
M-H1299 (1.276 mg). The intracellular metabolite powder was
redissolved in D
2
O and normalized based on the total protein contained in additional petri with
corresponding treatment before performing NMR. We made sure to maintain the final concentration of
internal standards at aforementioned levels.
4.3. 1H-NMR Spectroscopy
High-resolution 1H-NMR spectra of intracellular metabolites were obtained on a Varian 600
spectrometer operating at 600 MHz after normalizing the samples by total protein concentrations using
BCA Protein Assays (Thermos Fisher Scientific, Rockford, IL, USA). 1H-NMR spectra of intracellular
extracts were acquired using a 6-kHz spectral width and 64 K data points. The acquisition time was
5.44 s and the relaxation delay was 14.56 s with 64 scans.
4.4. 1H-NMR Spectroscopy Processing
After NMR analysis, Free Induction Decay (FID) files were obtained and processed using
NMR processing software ACD (Advanced Chemistry Development, Inc. Toronto, ON, Canada).
NMR spectra of all the samples were stacked and processed simultaneously. First, FID files were
Fourier-transformed to visualize spectra followed by phasing, baseline correction and binning with
the auto option of the software. After completing these steps, the full spectra, as a batch, were divided
into 1000 bins using the intelligent bucketing algorithm in ACD software, giving a numerical value for
corresponding peaks, and converted into a data table. Intelligent bucketing in ACD is an algorithm
that was designed to make decisions as to where a bucket division should be. Intelligent bucketing
chooses integral divisions based on local minima and therefore avoids the reduction of data resolution,
while aligning the spectra as a batch.
4.5. Quality Control
Relative standard deviation (RSD) values were calculated for each treatment group separately and
Technical variation within metabolomics datasets, recorded using one dimensional NMR maintained
less than <8% (reported as the median spectral RSD)
Metabolites 2019,9, 50 17 of 21
4.6. Multivariate Data Analysis: OPLS-DA
The processed, digitized NMR spectral data table from ACD software (version 10) was imported
into the SIMCA (version 15) software (Sartorius Stadium Biotech, Germany for Multivariate data
analysis (MVDA). The data table was transposed and labeled accordingly. The integrals corresponding
to the spectral region from 4.5 to 6 ppm were excluded as this region contains water peaks and
exchangeable protons. Spectral regions displaying no peaks, DMSO, and spectral regions of methanol
to all the samples were also excluded from the dataset. PCA, OPLS-DA models were created by
generating optimum number of principal components needed to fit the data, using the autofit option
in the software. Each model’s characteristics are described by how well it fits the data and its ability to
predict new data accurately. Thus the value for R2 describes how well the data fits the model while the
value of Q2 relates to the models ability to predict unknown data correctly. These are calculated by the
for the purpose of evaluating and validating the models generated. The following cutoff criteria are
used for validating the models that were generated. For NMR metabolomic data, it is recommended
that the model generated has a Q2 > 0.5, a value of R2 higher than Q2 with the difference between
them being no greater than 0.3. These criteria were adhered to for all the models utilized for the
investigation. Samples were identified and distinguished by their respective labels and colored for
visual convenience. The data was subjected to Pareto-scaling prior to analysis. The Hotelling T
2
test
(based on the 95% confidence interval) and DMOD-X test (based on the distance from the model plane)
was used to remove any statistically extreme outliers while maintaining a minimum of 4 replicates
in each group. Initially, unsupervised Principal Component Analysis (PCA) was performed to view
the clustering effects in the samples (Supplemental Materials). Subsequently, OPLS-DA, a supervised
pattern recognition method, was performed to maximize the identification of variation between
groups tested.
4.7. Metabolite Identification and Quantification from Chenomx NMR Suite
The metabolites were identified using Chenomx NMR suite (Chenomx Inc., Edmonton, AB,
Canada). The fid files from the 1D 1H-NMR spectra were imported into the Chenomx software.
This software has its own processing interface where spectra were Fourier-transformed and baseline
corrected. Phasing was done using DSS reference peak at 0.0 ppm, and the water peak was also deleted.
The processed spectra were analyzed in the profiler module of the software. The 600 MHz library
with the corresponding pH was selected. Identification and concentrations of different metabolites
were calculated by fitting the set of peaks for those compounds in the sample spectrum. If the area
was crowded with many peaks, then multiple metabolites were adjusted at one time to match the
reference spectrum closest to the sample spectrum. The identified and quantified compounds were
then exported into an excel sheet.
4.8. Additional Multivariate Data Analysis and Metabolic Pathway Identification Using MetaboAnalyst
3.0 Software
MetaboAnalyst 3.0 software, a web-based metabolomics data processing tool [
63
], was used
to statistically analyze the metabolites identified using Chenomx NMR suite. Quantified data from
Chenomx NMR suite were scaled using range scaling algorithm. Clustering differences, heat maps,
and a Random Forest analysis plot were generated. Further, the top 25 metabolites correlating with
glutamine were identified using Pearson correlation analysis and the significant features wereidentified
by Random Forest analysis. Additionally, quantified data from Chenomx NMR suite was transferred
into an excel table which allowed us to perform a Student’s t-test and calculate fold changes. A p-value
of less than 0.05 was considered to be statistically significant for univariate analysis.
Metabolic pathway identification was performed with the pathway analysis option of
Metaboanalyst 3.0 software. Briefly, the Homo Sapiens Pathway Library was selected as a reference,
and the pathway analysis was performed to generate pathway analysis output on all matched
Metabolites 2019,9, 50 18 of 21
pathways, based on the p-values from pathway enrichment analysis and pathway impact values
from pathway topology analysis.
Further, metabolites that were changing most significantly between the control and 30
µ
M
treatment were traced back to their origin, and the pathways were interpreted for metabolism changes
using current biochemistry.
4.9. Western Blot for Protein Expression Analysis
One million cells of each of A549 and H1299 were seeded in 100-mm dishes and incubated for 24 h;
then, the original media was replaced by media with/without
δ
T and incubated for another 72 h. After
72 h incubation, cells were washed with ice-cold PBS and lysed in the cold 1X cell lysis buffer (Cell
Signaling Technology, Danvers, MA, USA) for 30 min on ice with 1X protease inhibitor (Cell Signaling
Technology, Danvers, MA, USA). The cell lysate was kept at −80 ◦C overnight before quantifying.
Protein concentrations were estimated using Pierce BCA Protein Assay kit (Bio-Rad Laboratories,
Hercules, CA, USA). Total cell lysates (40
µ
g) were mixed with equal amounts of 6x laemmli buffer
(Bio-Rad Laboratories, Hercules, CA, USA), followed by boiling at 100
◦
C for 5 min. Samples were
loaded on 10% SDS-polyacrylamide gel electrophoresis, and then the gel was electrophoretically
transferred to a nitrocellulose membrane (Whatman, Clifton, NJ, USA) in transfer buffer (25 mM
Tris, 190 mM glycine, 20% methanol) using a Bio-Rad Trans-Blot
®
Turbo
™
Transfer System (Hercules,
CA, USA). The membranes were incubated for 1 h at room temperature with 5% BSA in 1x TBS
buffer containing 0.1% Tween. After incubation, the membranes were incubated overnight at
4
◦
C with primary antibodies (1:1000). The following antibodies ASCT2, LAT-1, p-mTOR, mTOR,
p-4EBP-1,4-EBP1, and B-actin (Cell Signaling Technology, Danvers, MA, USA) were used in the
analysis. The membranes were washed three times with TBS-T and subsequently incubated with the
secondary antibodies (1:5000) containing 2% BSA for 2 h at room temperature. The signal intensity was
then measured by chemiluminescent imaging with ChemiDoc XRS (Bio-Rad Laboratories, Hercules,
CA, USA).
5. Conclusions
In this work, the anticancer effects of
δ
T on NSCLC cell lines A549 and H1229 were investigated
and confirmed by 1H-NMR metabolomics analysis. A closer look into the intracellular metabolome of
NSCLC cells revealed significant and potentially beneficial alterations in glutamine concentrations
and related metabolism upon treatment with
δ
T. The data purports that
δ
T exerts its action by
inhibiting glutamine uptake into proliferating cells by inhibition of glutamine transporters, thereby
resulting in inhibition of cell proliferation and induction of apoptosis via downregulation of the mTOR
pathway (Figures 4B and 5). Through this work, NMR-based cellular metabolomics helps provide
possible opportunities for evaluating the therapeutic effect of phytochemicals and systemic changes in
cancer metabolism.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2218-1989/9/3/50/s1.
Table S1: List of metabolite concentrations determined using Chenomx NMR Suite in A549 cells. Table S2: List of
metabolite concentrations determined using Chenomx NMR Suite in H1299 cells. Figure S1: Effects of
δ
T on A549
(A) and H1299 (B) on the metabolome of lung cancer cell lines.
Author Contributions:
Conceptualization, L.D.R. and S.V.G.; Methodology, L.D.R.; Software, L.D.R.; Formal
Analysis, L.D.R. and M.H.; Investigation, S.V.G.; Resources, S.V.G.; Data Curation, L.D.R.; Writing—Original
Draft Preparation, L.D.R. Writing—Review and Editing, L.D.R., M.H., and S.V.G; Supervision, S.V.G.; Funding
Acquisition, S.V.G.
Funding: This research was conducted using intra-mural funding.
Acknowledgments:
We thank Alexander Buko, Vice President Business and Product Development at Human
Metabolome Technologies America for comments on results and language and the assistance with additional
bioinformatics methods that greatly improved the manuscript. We are also immensely grateful to Bashar Ksebati
for NMR instrument support.
Conflicts of Interest: The authors declare no conflicts of interest.
Metabolites 2019,9, 50 19 of 21
References
1.
Society, A.C. Lung Cancer (Non-Small Cell). Available online: http://www.cancer.org/acs/groups/cid/
documents/webcontent/003115-pdf.pdf (accessed on 09 March 2019).
2. American Cancer Society. Cancer Facts & Figures 2016; American Cancer Society: Atlanta, GA, USA, 2016.
3.
Kelsey, C.R.; Clough, R.W.; Marks, L.B. Local Recurrence Following Initial Resection of NSCLC: Salvage Is
Possible with Radiation Therapy. Cancer J. 2006,12, 283–288. [CrossRef] [PubMed]
4.
Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs over the 30 years from 1981 to 2010.
J. Nat. Prod. 2012,75, 311–335. [CrossRef] [PubMed]
5.
Wang, J.L.; Gold, K.A.; Lippman, S.M. Natural-agent mechanisms and early-phase clinical development.
Top. Curr. Chem. 2013,329, 241–252. [CrossRef]
6.
Aggarwal, B.B.; Shishodia, S. Molecular targets of dietary agents for prevention and therapy of cancer.
Biochem. Pharmacol. 2006,71, 1397–1421. [CrossRef]
7.
Surh, Y.J. Cancer chemoprevention with dietary phytochemicals. Nat. Rev. Cancer
2003
,3, 768–780. [CrossRef]
[PubMed]
8.
Theriault, A.; Chao, J.-T.; Wang, Q.; Gapor, A.; Adeli, K. Tocotrienol: A review of its therapeutic potential.
Clin. Biochem. 1999,32, 309–319. [CrossRef]
9.
De Silva, L.; Chuah, L.H.; Meganathan, P.; Fu, J.-Y. Tocotrienol and cancer metastasis. BioFactors
2016
,42,
149–162. [CrossRef]
10.
Constantinou, C.; Papas, A.; Constantinou, A.I. Vitamin E and cancer: An insight into the anticancer activities
of vitamin E isomers and analogs. Int. J. Cancer 2008,123, 739–752. [CrossRef] [PubMed]
11.
Rajasinghe, L.D. Anti-Cancer Effects of Tocotrienols in NSCLC. Ph.D. Thesis, Wayne State University, Detroit,
MI, USA, 2017.
12. Ji, X.; Wang, Z.; Geamanu, A.; Sarkar, F.H.; Gupta, S.V. Inhibition of cell growth and induction of apoptosis
in non-small cell lung cancer cells by delta-tocotrienol is associated with notch-1 down-regulation. J. Cell.
Biochem. 2011,112, 2773–2783. [CrossRef]
13.
Rajasinghe, L.D.; Pindiprolu, R.H.; Gupta, S.V. Delta-tocotrienol inhibits non-small-cell lung cancer cell
invasion via the inhibition of NF-
κ
B, uPA activator, and MMP-9. OncoTargets Ther.
2018
,11, 4301–4314.
[CrossRef]
14.
Kwon, H.; Oh, S.; Jin, X.; An, Y.J.; Park, S. Cancer metabolomics in basic science perspective. Arch. Pharm.
Res. 2015,38, 372–380. [CrossRef]
15.
Puchades-Carrasco, L.; Pineda-Lucena, A. Metabolomics Applications in Precision Medicine: An Oncological
Perspective. Curr. Top. Med. Chem. 2017,17, 2740–2751. [CrossRef]
16.
Tran, Q.; Lee, H.; Park, J.; Kim, S.H.; Park, J. Targeting Cancer Metabolism—Revisiting the Warburg Effects.
Toxicol. Res. 2016,32, 177–193. [CrossRef] [PubMed]
17.
Mohamed, A.; Deng, X.; Khuri, F.R.; Owonikoko, T.K. Altered glutamine metabolism and therapeutic
opportunities for lung cancer. Clin. Lung Cancer 2014,15, 7–15. [CrossRef] [PubMed]
18.
Abubakar, I.B.; Lim, S.-W.; Loh, H.-S. Synergistic Apoptotic Effects of Tocotrienol Isomers and Acalypha
wilkesiana on A549 and U87MG Cancer Cells. Trop. Life Sci. Res. 2018,29, 229–238. [CrossRef] [PubMed]
19.
Zhdanov, A.V.; Waters, A.H.C.; Golubeva, A.V.; Dmitriev, R.I.; Papkovsky, D.B. Availability of the key
metabolic substrates dictates the respiratory response of cancer cells to the mitochondrial uncoupling.
Biochim. Biophys. Acta BBA-Bioenerg. 2014,1837, 51–62. [CrossRef]
20.
Gonzalez Herrera, K.N.; Lee, J.; Haigis, M.C. Intersections between mitochondrial sirtuin signaling and
tumor cell metabolism. Crit. Rev. Biochem. Mol. Biol. 2015,50, 242–255. [CrossRef]
21.
Robert, S.M.; Sontheimer, H. Glutamate Transporters in the Biology of Malignant Gliomas. Cell. Mol. Life Sci.
CMLS 2014,71, 1839–1854. [CrossRef]
22.
Fuchs, B.C.; Finger, R.E.; Onan, M.C.; Bode, B.P. ASCT2 silencing regulates mammalian target-of-rapamycin
growth and survival signaling in human hepatoma cells. Am. J. Physiol. Cell Physiol.
2007
,293, C55–C63.
[CrossRef]
23.
Shimizu, K.; Kaira, K.; Tomizawa, Y.; Sunaga, N.; Kawashima, O.; Oriuchi, N.; Tominaga, H.; Nagamori, S.;
Kanai, Y.; Yamada, M.; et al. ASC amino-acid transporter 2 (ASCT2) as a novel prognostic marker in
non-small cell lung cancer. Br. J. Cancer 2014,110, 2030–2039. [CrossRef]
Metabolites 2019,9, 50 20 of 21
24.
Conciatori, F.; Ciuffreda, L.; Bazzichetto, C.; Falcone, I.; Pilotto, S.; Bria, E.; Cognetti, F.; Milella, M. mTOR
Cross-Talk in Cancer and Potential for Combination Therapy. Cancers 2018,10, 23. [CrossRef] [PubMed]
25.
Jeon, Y.J.; Khelifa, S.; Feng, Y.; Lau, E.; Cardiff, R.; Kim, H.; Rimm, D.L.; Kluger, Y.; Ronai, Z.e. Abstract
2440: RNF5 mediates ER stress-induced degradation of SLC1A5 in breast cancer. Cancer Res.
2014
,74, 2440.
[CrossRef]
26.
Shimizu, K.; Kaira, K.; Tomizawa, Y.; Sunaga, N.; Kawashima, O.; Oriuchi, N.; Kana, Y.; Yamada, M.;
Oyama, T.; Takeyoshi, I. P0143 ASC amino acid transporter 2 (ASCT2) as a novel prognostic marker in
non-small-cell lung cancer. Eur. J. Cancer 2014,50, e49. [CrossRef]
27.
Fuchs, B.C.; Bode, B.P. Amino acid transporters ASCT2 and LAT1 in cancer: Partners in crime? Semin. Cancer
Biol. 2005,15, 254–266. [CrossRef]
28.
van den Heuvel, A.P.J.; Jing, J.; Wooster, R.F.; Bachman, K.E. Analysis of glutamine dependency in non-small
cell lung cancer: GLS1 splice variant GAC is essential for cancer cell growth. Cancer Biol. Ther.
2012
,13,
1185–1194. [CrossRef] [PubMed]
29.
Wang, Q.; Hardie, R.A.; Hoy, A.J.; van Geldermalsen, M.; Gao, D.; Fazli, L.; Sadowski, M.C.; Balaban, S.;
Schreuder, M.; Nagarajah, R.; et al. Targeting ASCT2-mediated glutamine uptake blocks prostate cancer
growth and tumour development. J. Pathol. 2015,236, 278–289. [CrossRef] [PubMed]
30.
Imai, H.; Kaira, K.; Oriuchi, N.; Shimizu, K.; Tominaga, H.; Yanagitani, N.; Sunaga, N.; Ishizuka, T.;
Nagamori, S.; Promchan, K. Inhibition of L-type amino acid transporter 1 has antitumor activity in non-small
cell lung cancer. Anticancer Res. 2010,30, 4819–4828.
31.
Fuchs, B.C.; Bode, B.P. Stressing out over survival: Glutamine as an apoptotic modulator. J. Surg. Res.
2006
,
131, 26–40. [CrossRef] [PubMed]
32.
Matés, J.M.; Segura, J.A.; Alonso, F.J.; Márquez, J. Pathways from glutamine to apoptosis. Front. Biosci.
2006
,
11, 3164–3180. [CrossRef]
33.
Rajasinghe, L.; Gupta, S. Tocotrienols suppress non-small lung cancer cells via downregulation of the Notch-1
signaling pathway (644.1). FASEB J. 2014,28. [CrossRef]
34.
Rajasinghe, L.; Pindiprolu, R.; Razalli, N.; Wu, Y.; Gupta, S. Delta Tocotrienol Inhibits MMP-9 Dependent
Invasion and Metastasis of Non-Small Cell Lung Cancer (NSCLC) Cell by Suppressing Notch-1 Mediated
NF-κb and uPA Pathways. FASEB J. 2015,29. [CrossRef]
35.
Rajasinghe, L.D.; Gupta, S.V. Tocotrienol-rich mixture inhibits cell proliferation and induces apoptosis via
down-regulation of the Notch-1/NF-
κ
B pathways in NSCLC cells. Nutr. Diet. Suppl.
2017
,9, 103–114. [CrossRef]
36.
Rajasinghe, L.D.; Gupta, S.V. Delta Tocotrienal Inhibit mTOR Pathway by Modulating Glutamine Uptake
and Transporters in Non-Small Cell Lung Cancer. FASEB J. 2016,30. [CrossRef]
37.
Jewell, J.L.; Kim, Y.C.; Russell, R.C.; Yu, F.-X.; Park, H.W.; Plouffe, S.W.; Tagliabracci, V.S.; Guan, K.-L. Differential
regulation of mTORC1 by leucine and glutamine. Science 2015,347, 194–198. [CrossRef] [PubMed]
38.
Petronini, P.G.; Urbani, S.; Alfieri, R.; Borghetti, A.F.; Guidotti, G.G. Cell susceptibility to apoptosis by
glutamine deprivation and rescue: Survival and apoptotic death in cultured lymphoma-leukemia cell lines.
J. Cell. Physiol. 1996,169, 175–185. [CrossRef]
39.
Folkers, K.; Satyamitra, M.; Srinivasan, V. Delta-tocotrienol Mediates the Cellular Response to
Radiation-Induced DNA Damage through Upregulation of Anti-Apoptotic Effectors in Human Lung
Fibroblast Cell Lines. FASEB J. 2015,29. [CrossRef]
40.
Husain, K.; Centeno, B.A.; Coppola, D.; Trevino, J.; Sebti, S.M.; Malafa, M.P.
δ
-Tocotrienol, a natural form of
vitamin E, inhibits pancreatic cancer stem-like cells and prevents pancreatic cancer metastasis. Oncotarget
2017,8, 31554–31567. [CrossRef]
41.
Montagnani Marelli, M.; Marzagalli, M.; Moretti, R.M.; Beretta, G.; Casati, L.; Comitato, R.; Gravina, G.L.;
Festuccia, C.; Limonta, P. Vitamin E
δ
-tocotrienol triggers endoplasmic reticulum stress-mediated apoptosis
in human melanoma cells. Sci. Rep. 2016,6, 30502. [CrossRef] [PubMed]
42.
Swift, S.N.; Pessu, R.L.; Chakraborty, K.; Villa, V.; Lombardini, E.; Ghosh, S.P. Acute toxicity of subcutaneously
administered vitamin E isomers delta-and gamma-tocotrienol in mice. Int. J. Toxicol.
2014
,33, 450–458.
[CrossRef] [PubMed]
43.
Mahipal, A.; Klapman, J.; Vignesh, S.; Yang, C.S.; Neuger, A.; Chen, D.-T.; Malafa, M.P. Pharmacokinetics
and safety of vitamin E δ-tocotrienol after single and multiple doses in healthy subjects with measurement
of vitamin E metabolites. Cancer Chemother. Pharmacol. 2016,78, 157–165. [CrossRef]
Metabolites 2019,9, 50 21 of 21
44.
Shen, C.-L.; Wang, S.; Yang, S.; Tomison, M.D.; Abbasi, M.; Hao, L.; Scott, S.; Khan, M.S.; Romero, A.W.;
Felton, C.K. A 12-week evaluation of annatto tocotrienol supplementation for postmenopausal women:
Safety, quality of life, body composition, physical activity, and nutrient intake. BMC Complement. Altern.
Med. 2018,18, 198. [CrossRef] [PubMed]
45.
Scalise, M.; Galluccio, M.; Console, L.; Pochini, L.; Indiveri, C. The Human SLC7A5 (LAT1): The Intriguing
Histidine/Large Neutral Amino Acid Transporter and Its Relevance to Human Health. Front. Chem.
2018
,6.
[CrossRef] [PubMed]
46.
Shin, G.; Kang, T.-W.; Yang, S.; Baek, S.-J.; Jeong, Y.-S.; Kim, S.-Y. GENT: Gene Expression Database of Normal
and Tumor Tissues. Cancer Inform. 2011,10, CIN–S7226. [CrossRef] [PubMed]
47.
Nakanishi, K.; Matsuo, H.; Kanai, Y.; Endou, H.; Hiroi, S.; Tominaga, S.; Mukai, M.; Ikeda, E.; Ozeki, Y.;
Aida, S.; et al. LAT1 expression in normal lung and in atypical adenomatous hyperplasia and adenocarcinoma
of the lung. Virchows Archiv 2006,448, 142–150. [CrossRef] [PubMed]
48.
Hassanein, M.; Hoeksema, M.D.; Shiota, M.; Qian, J.; Harris, B.K.; Chen, H.; Clark, J.E.; Alborn, W.E.;
Eisenberg, R.; Massion, P.P. SLC1A5 mediates glutamine transport required for lung cancer cell growth and
survival. Clin. Cancer Res. 2013,19, 560–570. [CrossRef] [PubMed]
49.
Masle-Farquhar, E.; Bröer, A.; Yabas, M.; Enders, A.; Bröer, S. ASCT2 (SLC1A5)-Deficient Mice Have Normal
B-Cell Development, Proliferation, and Antibody Production. Front. Immunol. 2017,8. [CrossRef]
50.
Chen, L.; Cui, H. Targeting Glutamine Induces Apoptosis: A Cancer Therapy Approach. Int. J. Mol. Sci.
2015,16, 22830–22855. [CrossRef]
51.
Kanai, Y.; Hediger, M.A. The glutamate/neutral amino acid transporter family SLC1: Molecular,
physiological and pharmacological aspects. Pflugers Archiv 2004,447, 469–479. [CrossRef]
52.
Petroulakis, E.; Mamane, Y.; Le Bacquer, O.; Shahbazian, D.; Sonenberg, N. mTOR signaling: Implications for
cancer and anticancer therapy. Br. J. Cancer 2006,94, 195–199. [CrossRef]
53.
Shaw, R.J.; Cantley, L.C. Ras, PI (3) K and mTOR signalling controls tumour cell growth. Nature
2006
,441,
424–430. [CrossRef]
54.
Yamauchi, K.; Sakurai, H.; Kimura, T.; Wiriyasermkul, P.; Nagamori, S.; Kanai, Y.; Kohno, N. System L
amino acid transporter inhibitor enhances anti-tumor activity of cisplatin in a head and neck squamous cell
carcinoma cell line. Cancer Lett. 2009,276, 95–101. [CrossRef]
55.
Laplante, M.; Sabatini, D.M. mTOR signaling at a glance. J. Cell Sci.
2009
,122, 3589–3594. [CrossRef]
[PubMed]
56.
Zhou, H.; Huang, S. Role of mTOR Signaling in Tumor Cell Motility, Invasion and Metastasis. Curr. Protein
Pept. Sci. 2011,12, 30–42. [PubMed]
57.
Circu, M.L.; Aw, T.Y. Glutathione and modulation of cell apoptosis. Biochim. Biophys. Acta
2012
,1823,
1767–1777. [CrossRef]
58.
Dalton, T.P.; Chen, Y.; Schneider, S.N.; Nebert, D.W.; Shertzer, H.G. Genetically altered mice to evaluate
glutathione homeostasis in health and disease. Free Radic. Biol. Med.
2004
,37, 1511–1526. [CrossRef] [PubMed]
59.
Friesen, C.; Kiess, Y.; Debatin, K.M. A critical role of glutathione in determining apoptosis sensitivity and
resistance in leukemia cells. Cell Death Differ. 2004,11, S73–S85. [CrossRef] [PubMed]
60.
Cazanave, S.; Berson, A.; Haouzi, D.; Vadrot, N.; Fau, D.; Grodet, A.; Letteron, P.; Feldmann, G.; El-Benna, J.;
Fromenty, B.; et al. High hepatic glutathione stores alleviate Fas-induced apoptosis in mice. J. Hepatol.
2007
,
46, 858–868. [CrossRef]
61.
Armstrong, J.S.; Steinauer, K.K.; Hornung, B.; Irish, J.M.; Lecane, P.; Birrell, G.W.; Peehl, D.M.; Knox, S.J.
Role of glutathione depletion and reactive oxygen species generation in apoptotic signaling in a human B
lymphoma cell line. Cell Death Differ. 2002,9, 252–263. [CrossRef]
62.
Saadat, N.; Liu, F.; Haynes, B.; Nangia-Makker, P.; Bao, X.; Li, J.; Polin, L.; Gupta, S.; Mao, G.; Shekhar, M.P.
Nano-targeted Delivery of Rad6/Translesion Synthesis Inhibitor for Triple Negative Breast Cancer Therapy.
Mol. Cancer Ther. 2018. [CrossRef]
63.
Xia, J.; Wishart, D.S. Web-based inference of biological patterns, functions and pathways from metabolomic
data using MetaboAnalyst. Nat. Protoc. 2011,6, 743–760. [CrossRef]
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