Survival and Proliferation of T-cell Acute Lymphoblastic Leukaemia Depends on mTOR-regulated Glutamine Uptake and EAAT1 Activity

Abstract

T-cell acute lymphoblastic leukaemia (T-ALL) is a cancer of the immune system. Approximately 20% of paediatric and 50% of adult T-ALL patients relapse and die from the disease. To improve patient outcome new drugs are needed. With the aim to identify new therapeutic targets, we integrated transcriptomics and metabolomics data, including live-cell NMR-spectroscopy, of cell lines and patient samples. We found that T-ALL cells have limited energy availability, resulting in down-regulated mTOR-signalling and reduced autophagy. Despite this, mTOR kinase remains active and essential for the glutamine-uptake and rapid proliferation, as glutamine supplies three nitrogen atoms in purines and all atoms but one carbon in pyrimidines. We show that EAAT1, a glutamate-aspartate transporter normally only expressed in the CNS, is crucial for glutamine conversion to nucleotides and that T-ALL cell proliferation depends on EAAT1 function, identifying it as a target for T-ALL treatment. Finally, we performed an in silico screen and identified a novel EAAT1-specific allosteric inhibitor.

Full text

1
Survival and Proliferation of T-cell Acute Lymphoblastic Leukaemia Depends on mTOR-
regulated Glutamine Uptake and EAAT1 Activity
Vesna S. Stanulović
1
, Michelle A.C. Reed
1
, Hemalvi Patani
1,4
, Sandeep Potluri
1,
Eleni
Georgiadou
1,5
, Jennie Roberts
1
, Sovan Sarkar
1
, Guy Pratt
1,2
, Alan M. Jones
3
, Ulrich
Günther
1
,
Christian Ludwig
4
and Maarten Hoogenkamp
1,*
1
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, West
Midlands, B15 2TT, United Kingdom
2
Centre for Clinical Haematology, Queen Elizabeth Hospital Birmingham, West Midlands,
B15 2GW, Birmingham, United Kingdom
3
School of Pharmacy, University of Birmingham, Birmingham, West Midlands, B15 2TT,
United Kingdom
4
Institute of Metabolism and Systems Research, University of Birmingham, Birmingham,
West Midlands, B15 2TT, United Kingdom
4
Current address: Bart's Cancer Institute, Queen Mary University of London, London,
Greater London, EC1M 6BQ, United Kingdom
5
Current address: Division of Diabetes, Endocrinology and Metabolism, Department of
Medicine, Imperial College London, London, Greater London, W12 0NN, United Kingdom.
*
Correspondence: m.hoogenkamp@bham.ac.uk
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Summary
T-cell acute lymphoblastic leukaemia (T-ALL) is a cancer of the immune system.
Approximately 20% of paediatric and 50% of adult T-ALL patients relapse and die from the
disease. To improve patient outcome new drugs are needed. With the aim to identify new
therapeutic targets, we integrated transcriptomics and metabolomics data, including live-cell
NMR-spectroscopy, of cell lines and patient samples. We found that T-ALL cells have limited
energy availability, resulting in down-regulated mTOR-signalling and reduced autophagy.
Despite this, mTOR kinase remains active and essential for the glutamine-uptake and rapid
proliferation, as glutamine supplies three nitrogen atoms in purines and all atoms but one
carbon in pyrimidines. We show that EAAT1, a glutamate-aspartate transporter normally
only expressed in the CNS, is crucial for glutamine conversion to nucleotides and that T-ALL
cell proliferation depends on EAAT1 function, identifying it as a target for T-ALL treatment.
Finally, we performed an in silico screen and identified a novel EAAT1-specific allosteric
inhibitor.
Key words: T-ALL, T-cell Acute Lymphoblastic Leukaemia, mTOR, AMPK, lysosome, NMR
spectroscopy, glutamine, glutamate, aspartate, live-cell NMR, de novo nucleotide synthesis,
SLC1A3, EAAT1, EAA1, GLAST-1
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Introduction
For a cancer to proliferate, cells need to increase their size and replicate their DNA, which
requires large quantities of proteins, lipids and nucleotides, as well as energy. Metabolic
changes and adaptations during tumorigenesis enable tumour cell growth and survival by
altering metabolic pathways, such as glycolysis and glutaminolysis [1-3] and signal
transduction cascades that regulate metabolic processes, such as the PI3K-AKT-mTOR
pathway [4-6]. The compilation of the metabolic changes results in a distinct metabolic
phenotype. Annotation of the rate-limiting steps that support the oncogenic metabolic
adaptations can lead to the identification of useful therapeutic targets for the development of
new anti-cancer therapies.
In order to understand and identify oncogenic processes that support rapid proliferation, we
developed a methodology that assessed and integrated malignant gene expression, signal
transduction and metabolism. We used T-cell acute lymphoblastic leukaemia (T-ALL) as a
model. T-ALL is a haematological malignancy of the T-cell lineage that occurs in adults and
children. Gene expression profiling studies showed that paediatric T-ALL patients can be
grouped into four major clusters: TAL/LMO, TLX/HOXA, Proliferative and Immature (ETP
ALL) [7, 8]. Most T-ALL have additional mutations in genes instrumental for signal
transduction pathways such as NOTCH (>60%), PTEN-PI3K-AKT, JAK-STAT and FBXW7
[9, 10]. Monitoring of minimal residual disease (MRD) is a response based treatment
protocol that is used for T-ALL patient stratification into standard, intermediate and high risk
groups [11]. Even though treatment outcome has improved, approximately 20% of paediatric
and 50% of adult T-ALL patients have refractory disease or relapse and most of these die
from the disease [12, 13]. Refractory disease is more frequent in T-ALL displaying an early
T-cell phenotype, which most commonly occurs in ETP and TAL/LMO T-ALL [14, 15].
SIL/TAL chromosomal microdeletion (1p32q) is the most common genetic rearrangement in
T-ALL [7]. SIL-TAL deletion positions the promoter of the constitutively active STIL gene in
front of the coding sequences of the TAL1 transcription factor that is crucial for
haematopoietic and T-cell development and differentiation [16].
In this study we integrated gene expression data, signal transduction activity and metabolic
studies, using cell lines and purified primary patient samples. We found that T-ALL cell cycle
progression depends on mTOR-dependent glutamine uptake. Employing metabolic labelling
we found that glutamine provides all but one carbon in the pyrimidine ring and three of the
four nitrogen atoms in purines. Furthermore, analyses of the metabolic processes supporting
glutamine metabolic pathways and the gene expression of T-ALL cell lines and patient
samples showed that T-ALL cells express the glutamate-aspartate antiporter EAAT1
(encoded by SLC1A3) [8]. In the healthy adult body, EAAT1 is only present in the neurons
and glia of the central nervous system where it is required for the uptake of the cytotoxic
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glutamate from the glutamatergic synapses in exchange for aspartate [17, 18]. In T-ALL,
EAAT1 is required for glutamate import into mitochondria in exchange for glutamine-derived
aspartate that is used as a substrate for nucleotide production in the cytoplasm. We show
that T-ALL survival depends on EAAT1 function and validates EAAT1 as a therapeutic target
for treating T-ALL. Subsequently, we performed a drug screen and identified a novel potent
EAAT1-specific allosteric inhibitor.
Results
SIL-TAL cells exhibit suppressed carbohydrate and upregulated nucleotide
metabolism
Based on the prevalence and the chance of refraction/relapse, we have decided to focus our
efforts on ETP and SIL-TAL T-ALL. We chose three cell lines that carry the SIL-TAL
rearrangement (DU.528, HSB2 and CCRF-CEM) and one cell line without that immuno-
phenotypically belongs to the ETP T-ALL group (ARR) [19-23]. RNA-seq identified 4508
differentially expressed genes between the cell lines. Principal component analysis and
hierarchical clustering found that the three SIL-TAL cell lines clustered separately from ARR,
with HSB2 and CCRF_CEM being most similar (Figure 1A,S1) Hierarchical clustering
grouped differentially expressed genes into 11 groups (Figure 1A). Genes suppressed in the
SIL-TAL cells grouped in cluster 1 (C1), whereas C9 and C10 comprised upregulated genes.
Gene functional annotation analyses indicated that ARR and SIL-TAL cells had a number of
differentially regulated metabolic pathways (Figure 1B,S2). C1 was associated with
carbohydrate catabolism, protein processing in the endoplasmic reticulum and amino-acid
biosynthesis, whereas C10 involved purine and pyrimidine synthesis, sphingolipid
metabolism and C9 T cell receptor and Rap1 signalling (Figure 1B). Additionally, genes that
were upregulated in at least two of the SIL-TAL cell lines (C8 and C11) were implicated in
signal transduction pathways such as MAPK, Hippo, Rap-1 (C8) and TGF-β, Ras, Rap-1,
JAK-STAT, AMPK, mTOR (C11) (Figure S2). Based on this we conclude that the SIL-TAL
metabolic phenotype in comparison to ARR is characterised by suppressed utilisation of
carbohydrates and amino acid/protein synthesis, and upregulated nucleotide metabolism.
Validation of the RNA-seq by qPCR confirmed differential gene expression for
argininosuccinate synthase 1 (ASS1; cluster C1) and Acyl-CoA synthetase short-chain
family member 1 (ACSS1; cluster C10), two genes whose products have metabolic functions
(Figure 1C,D). ASS1 converts citrulline and aspartate into L-argininosuccinate, an
intermediate in arginine biosynthesis, which is also important for alanine, aspartate and
glutamine metabolism. ACSS1 encodes a protein that catalyses the conversion of acetate to
acetyl-CoA [24-26]. ASS1 was only expressed in ARR cells, whereas ACSS1 had the
opposite pattern, with higher expression levels found in the SIL-TAL cells (Figure 1C,D).
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Protein synthesis is supressed in SIL-TAL cells
We decided to assess whether the findings of the gene expression analyses were reflected
in the T-ALL metabolism. To test if the amino acid synthesis is suppressed in SIL-TAL cell
lines, we measured de novo protein synthesis using Click-iT OP-puro methodology [27]. The
quantification revealed that SIL-TAL cells exhibited 30-40% lower protein synthesis than
ARR (Figure 2A,B).
The mTOR signalling pathway is the main regulator of protein synthesis based on the
availability of amino acids and cellular energy status [28-30]. Leucine, arginine, and
glutamine, have been identified as effective activators of mTORC1 and their depletion leads
to the inhibition of the mTOR pathway and suppression of de novo protein synthesis [31, 32].
Based on (i) the downregulated protein synthesis in SIL-TAL, (ii) their inability to produce
arginine due to the absence of the ASS1 enzyme and (iii) suppressed amino acid
biosynthesis based on the gene ontology, we expected attenuated signalling through the
mTOR pathway. To test the importance of the mTOR pathway on T-ALL cells we employed
rapamycin, an inhibitor of the mTOR kinase activity and found that upon treatment SIL-TAL
cell proliferation was reduced, so that after 4 days the number of treated cells was 40%
compared to the vehicle-treated control (Figure 2C) [33, 34]. Rapamycin treatment of ARR
did not affect cell proliferation, demonstrating that ARR was insensitive to mTOR inhibition,
whereas SIL-TAL cell lines required active mTOR kinase activity for their proliferation.
In order to better understand mTOR signalling in the T-ALL cell lines, we assayed the
endogenous levels and phosphorylation status of proteins constituting the PI3K-AKT-mTOR
signalling cascade under normal growth conditions and in rapamycin treated cells. Binding of
growth factors to G-protein coupled receptors results in PI3K activation and generation of
PI3P that further activates PDK1 to phosphorylate itself (S241) and AKT1 (T308) [35-37].
Opposing this is the phosphatase PTEN, which dephosphorylates PI3P and inhibits AKT1
signalling [38-40]. Western blot analyses revealed that T-ALL cells had active PDK-1 (p-
PDK1
S241
), only CCRF-CEM, which harbours a PTEN deletion, contained active AKT1 (p-
AKT1
T308
) (Figure 2D) [41]. In CCRF-CEM, AKT1 was additionally phosphorylated at the
mTORC1 target site S473 and this activating mark was absent in the rapamycin treated
cells, showing that mTOR has intact kinase activity in this cell line (Figure 2D) [42, 43]. The
presence of inactive AKT1 in ARR, DU.528 and HSB2 and constitutively active AKT1 in
CCRF-CEM cells implies that mTOR signalling is uncoupled from growth factor-AKT
stimulation in all four T-ALL cell lines.
Next, we examined the AKT substrates c-RAF
S259
and AKT1S1/PRAS40
T246
[44-46]. As
expected, both sites were phosphorylated in CCRF-CEM cells, but also in ARR (Figure 2D).
Since PRAS40 is an inhibitor of the mTOR signalling cascade and the phosphorylation of
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PRAS40 by AKT1, at T246, relieves mTORC1 from PRAS40 inhibition, our results imply that
the mTORC1 signalling is not suppressed by PRAS40 in ARR cells.
RHEB is a small GTPase required for mTOR lysosomal activation, which is negatively
regulated by the tuberous sclerosis complex (TSC) complex [47-50]. AKT inhibits the TSC
complex and allows RHEB to stimulate mTOR signalling [49, 51-54]. The loss of any of the
members of the TSC-complex renders mTOR insensitive to regulation through amino acid
availability and growth factors [55-57]. Examination of the gene expression data for the
genes encoding the proteins of the mTOR signalling pathway revealed defective TSC1 gene
expression in ARR cells. Due to alternative splicing, TSC1 transcripts lack four exons (exons
3-7) including the translation start site (Figure S3A-C) and consequently ARR fails to
generate TSC1 protein (Figure 2E). The lack of TSC1 implies that mTOR signalling in ARR
cells cannot be inhibited by the TSC complex. Additionally, rapamycin failed to supress
mTOR and 4E-BP phosphorylation (mTOR
S2448
, mTOR
S2481
, 4E-BP1
T37/46
, 4E-BP1
T70
and 4E-
BP1
S65
) but did reduce the phosphorylation of p70S6K
T389
and S6 ribosomal protein
(rpS6
S235
) (Figure 2D,E). Taken together, ARR cells are characterised by PRAS40 and TSC-
independent mTOR signalling with rapamycin-insensitive phosphorylation of 4E-BP. Based
on this we conclude that in ARR cells mTOR and protein synthesis regulated by 4E-BP is
independent of growth factor and amino acid signalling.
Another regulator of the mTOR signalling is the binding partner Raptor, which mediates
mTOR interaction with 4E-BP1 and p70S6K [58-61]. As a result of low cellular energy status,
AMP kinase (AMPK) phosphorylates S792 on Raptor leading to mTORC1 inhibition and cell
cycle arrest [62]. Surprisingly, p-Raptor
S792
was observed in all cell lines implying active
AMPK and reduced energy availability. In SIL-TAL cells p-Raptor
S792
occurred together with
reduced RHEB-driven mTOR auto-phosphorylation (S2481) and mTOR phosphorylation by
AKT1 at S2448 (Figure 2E) [63, 64]. The commonalities found between the T-ALL cells
suggest that T-ALL cells have low energy status that activates AMPK to phosphorylate
Raptor and suppress mTOR signalling. Active AMPK and p-Raptor
S792
had limited effect on
mTOR activity in ARR cells showing that mTOR is constitutively active. The result of
supressed mTOR signalling in SIL-TAL was reduced p-4E-BP1
T37/46
in comparison to ARR.
Additionally, SIL-TAL cells had reduced phosphorylation on S65 or T70, with DU.528
exhibiting low phosphorylation on both residues. Since the function of 4E-BP is to inhibit cap-
dependent translation by binding to the translation initiation factor eIF4E, hyper-
phosphorylation of 4E-BP1 by AKT and mTOR disrupts the interaction with eIF4E resulting
in activation of cap-dependent mRNA translation [65-68]. 4E-BP phosphorylation at T37 and
T46 is required for subsequent S65 and T70 phosphorylation and the release of eIF4E [67,
69, 70]. Therefore, we conclude that the SIL-TAL 4E-BP phosphorylation status is not
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optimal for supporting mRNA translation and it corroborates our observation that SIL-TALs
have reduced de novo protein synthesis.
The consequence of supressed mTOR signalling on p70S6K in SIL-TAL cells was only
observed is HSB2 cells, where p-p70S6K
T389
was the lowest. Despite this, phosphorylation
of the p70S6K target rpS6 was observed and remained sensitive to rapamycin showing that
AMPK-mediated mTOR suppression does not affect rpS6 phosphorylation in SIL-TALs.
Lower protein synthesis and reduced phosphorylation of mTOR and 4E-BP or p70S6K was
found in all three SIL-TAL cell lines. A possible reason for this is limited amino acid
availability. Amino acids are substrates for protein synthesis and drivers for the Rag
GTPase-dependent recruitment of mTORC1 to lysosomes, where growth factor-controlled
RHEB activates mTORC1 [47, 71, 72]. Lysosomal co-localisation of mTORC1 and its
interacting partners on the lysosomal membrane is an additional level of mTOR regulation,
which is required for correct mTORC1 signalling and is independent of mTOR kinase activity
[47, 71, 73]. To examine mTOR localisation under normal growth conditions and upon
mTOR inhibition by rapamycin, we imaged localisation of mTOR and the lysosomal marker
LAMP1 in parallel with monitoring lysosomal acidification with a live-cell LysoTracker dye
(Figure 2F) [74-76]. In all the cell lines mTOR partly co-localised with LAMP1 staining but
also had nuclear and cytoplasmic localisation. Surprisingly, upon rapamycin treatment,
mTOR was almost exclusively co-localised with punctual LAMP1 staining (Figure 2F). This
increase in lysosomal mTOR localisation upon rapamycin treatment reveals that the
regulation of mTOR localisation was functional and suggests that the T-ALL cells had limited
amino acid availability, which was improved upon mTOR inhibition, most likely as a
consequence of reduced protein synthesis and cell proliferation, which would lead to
increase in intracellular amino acid availability.
While ARR had intense LysoTracker staining that overlapped with LAMP1 and was
increased by rapamycin treatment, SIL-TAL cells had weak to absent staining that was
further reduced upon rapamycin treatment (Figure 2F). This lack of lysosomal acidification
could be a possible cause of limited amino acid availability as acidified lysosomes are
required for protein degradation, supplying the cell with free amino acids.
Activated AMPK in all T-ALL cell lines
Lysosomal acidification is facilitated by vacuolar-type H
+
-ATPase (v-ATPase) that hydrolyses
the ATP and transports H
+
across the lysosome membrane, resulting in lysosomal
acidification [77]. v-ATPase function is regulated by the cellular energy status, where an
increase in the AMP/ATP ratio activates AMPK, which in turn phosphorylates and inhibits the
v-ATPase, leading to preservation of the cellular energy stores [47, 71, 73, 78]. The
observed inadequate lysosomal acidification and p-Raptor
S792
suggests active AMPK in T-
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ALL cells (Figure 2D,F). Indeed, p-AMPK
T172
, a mark of AMPK activation was detected as
well as the phosphorylation of the AMPK targets, ULK1
S555
and Beclin-1
S93
(Figure 3A) [79-
83]. ULK1 and Beclin-1 are important for autophagosome formation and initiation of
autophagy, a regulated self-digestion that is induced by starvation [84, 85]. AMPK stimulates
autophagy directly by phosphorylating ULK-1(S555) and Beclin-1(S93) and indirectly by
inhibiting mTOR through p-Raptor
S792
([62, 82, 83]). Our results demonstrate that all T-ALL
cells have activated AMPK, which induces autophagy by relaying the signal to ULK-1 and
Beclin-1.
Impaired autophagy and lysosomal function in SIL-TAL
Even though autophagy is induced in T-ALL, the observed impaired lysosomal acidification
in SIL-TAL cells implies that these cells have a limited ability to hydrolyse cargo delivered by
autophagosomes, which would cause an obstructed autophagic flux. ULK-1 and Beclin-1
activate the autophagy-related (ATG) protein machinery, resulting in the lipidation of the
cytosolic protein LC3-I to LC3-II and integration into autophagosomes, double-layered
cytoplasm-engulfing vesicles (reviewed in [86]). LC3-II interaction with the cargo receptors,
such as p62, allows autophagosomes to engulf cytoplasmic components, including cytosolic
proteins and organelles. Subsequent fusion with lysosomes facilitates cargo degradation by
lysosomal hydrolases and the release of nutrients [86]. In order to determine whether T-ALL
cells have effective autophagy, we examined the levels of LC3I, LC3II and the cargo
receptor p62, as well as the localisation of LC3II in the control and rapamycin treated cells.
All four cell lines exhibited higher LC3I than LC3II levels and punctual cytoplasmic LC3II,
which only in ARR overlapped with lysosomal LAMP1 staining, revealing the presence of
autophagosomes in all T-ALL cells, but fusion with lysosomes only in ARR cells (Figure
3B,C). Rapamycin-induced autophagy in ARR cells lead to elevated production of LC3I and
LC3II, hence autophagosomes and their fusion with lysosomes, as seen by increased
punctual LC3II staining that co-localised with LAMP1 (Figure 3B,C). ARR also show the
lowest levels of p62, confirming effective cargo clearance (Figure 3C). Altogether, these
results demonstrate functional autophagy only in ARR. Rapamycin treatment had no
noticeable effect on LC3II and p62 abundance in DU.528 and HSB2 cells albeit some
increase of LC3II was observed in CCRF-CEM cells (Figure 3B,C). SIL-TAL cells exhibited
punctual LC3II staining, which did not co-localise with LAMP1 and even after rapamycin
treatment co-localisation was sporadic. The limited LC3II-LAMP1 co-localisation and
reduced lysosomal acidification indicate that SIL-TAL cells have obstructed autophagic
clearance.
In order to find out whether reduced lysosomal acidification is the consequence of a low
energy status/AMPK activity, or impaired v-ATPase function, we inhibited AMPK using
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dorsomorphin [87]. Treatment reduced the abundance of LC3I and LCII in all four T-ALL cell
lines. Additionally, LC3 protein level was elevated in ARR and to a lower extent in HSB2
cells while p62 remained constant, except in DU.528 cells where dorsomorphin caused a
mild reduction (Figure 3D). Immuno-fluorescent staining of the treated cells showed that the
SIL-TAL cells had functional v-ATPase and the capacity to acidify their lysosomes, as seen
by LysoTracker staining accumulated in LAMP1 positive lysosomes, a proportion of which
was co-localised with autophagosomal LC3II (Figure 3E). The ARR cell line displayed an
increase in the amount of punctual, co-localised LC3II and LAMP1 staining. In line with the
western blot analyses these results show that inhibition of AMPK activity leads to lysosomal
acidification and activated autophagy in SIL-TAL cells and to a further increase in autophagic
activity in ARR cells. Therefore, we conclude that all T-ALL cell lines have functional v-
ATPase and that reduced autophagy is caused by the unfavourable energy status and active
AMPK.
mTOR Regulates Glutamine and Glucose Utilisation
So far, we observed the consequences of limited ATP and amino acid availability on the
mTOR/AMPK signalling, autophagy and protein synthesis. Despite this, the T-ALL cell lines
undergo rapid cellular proliferation. In order to better understand how the T-ALL cells source
metabolites to fuel rapid proliferation, we measured their metabolism by establishing their
metabolite uptake/release and their intracellular metabolite levels.
Metabolite uptake was assessed by measuring metabolite levels in the media, 24h after
medium change, by NMR spectroscopy. We identified 22 metabolites, whose levels were
significantly changing in at least one of the T-ALL cell lines. Hierarchical clustering of the
observed changes showed that all four cell lines uptake lysine, glucose, phenylalanine and
glutamine and release lactate, pyruvate, glutamate and pyroglutamate (Figure 4A). mTOR
inhibition by rapamycin resulted in reduced glucose and glutamine uptake and lactate
release in all T-ALL cells, indicating that glucose and glutamine uptake depended on an
active mTOR pathway (Figure 4B).
T-ALL metabolic phenotype
NMR analysis of intracellular metabolite levels was used to determine differences in
metabolite levels between the cell lines, 24h after changing the growth medium. Hierarchical
clustering of the metabolites that showed significantly different levels between any two cell
lines, identified four groups of metabolites (Figure 4C, S4A; metabolic clusters MC1-4). MC1
and MC3 are comprised of metabolites more abundant in SIL-TAL cells and ARR,
respectively. Phosphatidylcholine, glycerophosphatidylcholine (GPC) and the amino acids
alanine, glutamate, aspartate and cysteine-derived taurine are constituents of MC1, while
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AMP, succinate, NAD
+
, glutathione, as well as creatine and phosphocreatine are members
of MC3. Therefore, direct measurements show that ARR cells have the highest AMP and the
lowest ATP/ADP level, confirming that ARR have a low energy status, in line with the active
AMPK seen in Figure 3A. Metabolites with low abundance in CCRF-CEM cells were featured
in the MC2 cluster. This cluster included glutamine, which similar to MC3 metabolites, was
found at the highest level in ARR (Figure 4C). We therefore observe that SIL-TAL have a
specific metabolic phenotype, characterised by lower glutamine levels and higher levels of
the glutamine-derived metabolites glutamate, aspartate and alanine. Taking into
consideration that (i) T-ALL uptake glutamine, which is a precursor for glutamate and
aspartate, (ii) rapamycin reduces glutamine utilisation and (iii) that glutamine, glycine and
aspartate are substrates for de novo nucleotide synthesis, glutamine could be the main
source for de novo nucleotide synthesis in T-ALL.
Intracellular metabolite levels are very dynamic and change as a response to metabolite
uptake and cellular demand. In order to address the dynamics of the metabolite levels, we
compared the abundance of intracellular metabolites at the time of growth-media change
(0h) to that found 8h and 24h later (Figure 4D). A similar response to media change in all
four cell lines was found for glutamate and myoinositol, whose availability decreased upon
media supplementation. Elevated intracellular concentration of glutamate is consistent with
our previous finding that T-ALL release glutamate into the media, as well as glutamate-
derived pyroglutamate (Figure 4A). A polarised response between SIL-TAL and ARR was
observed for AMP, which was the only metabolite with increasing levels in SIL-TAL cells
while decreasing in ARR (Figure 4D). Based on the finding that in SIL-TAL cells media
change led to increasing AMP levels, while ATP/ADP levels were overall maintained, we
conclude that SIL-TAL cells have an increasing AMP to ATP/ADP ratio upon medium
change. This supports our finding that SIL-TAL cells had active AMPK and reduced energy
availability, as described above (Figure 3A).
Opposite dynamics were observed for ARR and SIL-TALs for NAD
+
, tyrosine, isoleucine,
leucine, phenylalanine, valine and glutamine, whose concentrations were lower in SIL-TAL
at 8h and/or 24h, and higher in ARR. This reduction of the intracellular abundance of
essential amino acids is an experimental observation that directly corroborates our previous
conclusion that SIL-TAL have reduced amino acid availability (Figure 4D,2A,D,F). In ARR,
the levels of these metabolites were elevated at 8h and for a few at 24h, and additionally
included aspartate, alanine, GPC and nucleotide sugars (UDP-glucose, UDP-Galactose and
UDP-GalNAc). Lower availability and only a transient increase after medium
supplementation of alanine, glutamate, aspartate and taurine in ARR compared to SIL-TAL
cells show that ARR have a high requirement for amino acids to facilitate higher rate of de
novo protein synthesis (Figure 4C,D,2A,B). The SIL-TAL cells rapidly utilise essential amino
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acids upon medium change, followed by their intracellular accumulation. Altogether,
measurements of the intracellular metabolite levels and the changes occurring during the
cell growth and proliferation confirm that T-ALL have an unfavourable energy status with
high AMP/ATP ratios and that SIL-TAL cells have limited availability of essential amino acids
in line with the previously observed reduced de novo protein synthesis.
Inhibition of the mTOR Pathway Restores Metabolite Levels in T-ALL Cell Lines
As we showed that mTOR inhibition reduced glucose/glutamine uptake and blocked SIL-TAL
proliferation (Figure 4B,2C), we next measured the effect of rapamycin on intracellular
metabolite levels. NMR measurements identified 28 metabolites that had a significantly
different abundance after 24h rapamycin treatment in at least one of the cell lines (Figure
4E). Hierarchical clustering of the observed log2 fold changes divided these metabolites into
six rapamycin-responsive metabolic clusters (RMC1-6). The most pronounced effect of
rapamycin was increased intracellular levels of the majority of metabolites. In all four cell
lines the largest increase was observed for glutamine and alanine levels, while AMP and
succinate levels had the opposite response (Figure 4E,S4B). A differential effect of
rapamycin on SIL-TAL in comparison to ARR was particularly visible in RMC2 and RMC4.
The levels of the amino acids phenylalanine, leucine, tyrosine, isoleucine and valine (RMC2)
increased to a greater degree in SIL-TAL cells, whereas the increase in aspartate, glycine,
glutamate and alanine (RMC4) was more pronounced in ARR (Figure 4E). The observed
increase in the amino acids availability upon mTOR inhibition explains the mTOR
recruitment to lysosomes upon rapamycin treatment and proves that T-ALL have limited
amino acid availability.
Glutamine is Essential for SIL-TAL Proliferation and Survival and Fuels Aspartate and
Nucleotide Biosynthesis
The polarised effect of mTOR inhibition on ARR vs SIL-TAL, seen primarily as an increase in
aspartate, glycine, glutamate and alanine levels could be dueto the continuous proliferation
in ARR and suppressed proliferation in SIL-TAL cells. As all four amino acids can be derived
from glutamine and glutamine, glycine and aspartate are used as substrates for nucleotide
synthesis, we tested the effect of glutamine withdrawal on cell proliferation. Omitting
glutamine from the growth medium, while maintaining all other nutrients, including 10%
foetal bovine serum, SIL-TAL cell numbers were reduced by 95% after 8 days of treatment
(Figure 5A). Glutamine deprivation caused a decrease in ARR numbers over the first four
days of culture, after which ARR recovered and resumed proliferation at the original rate.
Apoptosis assays of the cells grown in glutamine-free medium revealed that the SIL-TAL cell
lines had an increased rate of apoptosis and cell death (Figure S5). For ARR, this was
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12
initially also the case, but normalised after four days (Figure S5). Therefore, we conclude
that glutamine is important for T-ALL cell proliferation.
Glutamine has several significant roles in metabolic processes such as fuelling the TCA
cycle through glutaminolysis and acting as a nitrogen donor in transamination and
transamidation reactions that are used for the production of non-essential amino acids. Also,
together with aspartate and glycine, glutamine feeds de novo nucleotide synthesis. In order
to assess glutamine contribution to T-ALL metabolic processes we performed tracer
experiments using [2,5-
15
N] Glutamine and [3-
13
C] Glutamine. When cells were grown in the
presence of [2,5-
15
N] Glutamine, in addition to the expected
15
N labelled glutamate (data not
shown), we observed
15
N incorporation into the amino group of aspartate and alanine within
8h of treatment in all four cell lines (Figure 5B and S6A). Resonances arising from Hβs of
aspartate (2.80-2.81ppm) and alanine (1.48-1.49ppm) are shown. At 0h, a doublet was seen
due to coupling to the Hα. However, at later time points (Figure 5B, 8 and 24h) there was
increased spectral complexity because Hβs were also weakly coupled to
15
N. The resulting
spectrum was a weighted average of a doublet of doublets (from
15
N-labelled aspartate) and
a simple doublet (unlabelled aspartate). Both aspartate and alanine were found at the lowest
level prior to labelling in ARR and CCRF-CEM cells. While CCRF-CEM signal intensity was
maintained at a similar level at 8h and 24h, ARR showed a transient increase at 8h and a
reduction to the original level by 24h (Figure 5B). A similar effect was observed for alanine,
although the signal intensity in ARR remained the same at 8 and 24h (Figure S6A). This
finding demonstrates that glutamine-derived
15
N was incorporated into aspartate and alanine
by transamination of oxaloacetate and pyruvate, respectively.
Glutamine, glycine and aspartate are amino acids used as substrates for nucleotide
synthesis, so in order to assess the glutamine contribution to nucleotide biosynthesis we first
acquired 2D-
1
H,
15
N heteronuclear single quantum coherence (HSQC) NMR spectra of
15
N
uniformly labelled ATP, GTP and UTP standards, where we observed five
1
H-
15
N
interactions for [U-
15
N] ATP (a-d), three for [U-
15
N] GTP (a-c) and three for [U-
15
N] UTP (a-c)
(Figure 5C). Spectra acquired from [2,5-
15
N] Glutamine-labelled T-ALL cells showed
15
N
incorporation into nucleotides at the 1, 3 and 9 positions, the 3 and 9 positions and the 1 and
3 positions in adenine, guanine and uracil respectively (Figure 5C). As expected, based on
the known contribution of glutamine to nucleotide synthesis, transamidation using
15
N-
glutamine resulted in label incorporation at N-3 and N-9 in purines and the N-3 in pyrimidines
(Supplementary Illustration SI1,2). However, label incorporation at N-1 in purines and N-3 in
pyrimidines is sourced from glutamine-derived
15
N-aspartate and shows that glutamine
ultimately supplies two out of three nitrogens in purines and both nitrogens in pyrimidines.
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So far, we demonstrated that glutamine transamination of oxaloacetate gives rise to
aspartate. As it is possible that glutamine supplies oxaloacetate as well through
glutaminolysis, which would subsequently mean that aspartate is completely derived from
glutamine, we used [3-
13
C] Glutamine in labelling experiments to test this. In the course of
the TCA cycle, [3-
13
C] Glutamine is converted to [2-
13
C] Fumarate, a symmetrical molecule
which is hydrated equally to [2-
13
C] Malate and [3-
13
C] Malate, and further to [2-
13
C] or [3-
13
C] oxaloacetate and aspartate (Supplementary Illustration SI3). Acquired 2D-
1
H,
13
C HSQC
NMR spectra revealed
13
C incorporation in fumarate, malate, oxaloacetate and aspartate
(Figure 5D,S6B and data not shown). Resonances for
1
H-
13
C moieties were derived from
both [2-
13
C] and [3-
13
C] labelling, as shown for aspartate and malate (a and b respectively,
Fig 5D,S6B). Quantification of the signals from the labelled samples, relative to the naturally
occurring
13
C in the control samples, showed that within 24h, 50% of all aspartate was [2-
13
C] or [3-
13
C] Aspartate (Figure S6E). These findings confirmed our hypothesis that two
molecules of glutamine give rise to a single aspartate molecule; first glutamine supplies the
backbone via the TCA cycle, while the second is used for transamination.
A further implication of these findings was that carbons derived from glutamine would, via
aspartate, get incorporated into pyrimidines, giving rise to [5-
13
C] or [6-
13
C] Uridine
(Supplementary illustration SI2). Resonances for
1
H-
13
C moieties arising from [5-
13
C] Uridine
or [6-
13
C] Uridine were indeed observed in 2D-
1
H,
13
C HSQC NMR spectra (peaks a and b,
Figure 5D). This strongly implies that the carbon at position 4 also originates from glutamine
via aspartate. Together, our results suggest that glutamine serves as a source for all but one
of the atoms in the pyrimidine ring.
Examination of the
13
C incorporation in other detectable metabolites found a significant label
accumulation into proline, as would be expected since glutamate is used as a substrate for
proline synthesis (Figure S6C). Surprisingly, [3-
13
C] Proline was detected in all four cell lines,
but [2-
13
C] Proline was found only in the SIL-TAL cells, indicating that SIL-TAL derived some
glutamate from the TCA-cycle while ARR used glutamate almost exclusively derived directly
from glutamine. Additional differences were observed between ARR and the SIL-TAL, in the
area of the spectra that is characteristic for the aspartate-derived metabolites arginine,
ornithine, citrulline and argininosuccinate (
13
C- ppm 30.2-30.8, Figure S6D). Due to overlaps
between their spectra we were not able to identify the individual contributions of these
metabolites. However, it is likely that the observed differences can be attributed to low
argininosuccinate and arginine levels in SIL-TAL cells due to the finding that SIL-TAL cells
lack ASS1 expression, an enzyme essential for their biosynthesis.
Patient-derived T-ALL and T-ALL cell lines have similar metabolic uptake
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To determine if glutamine is not only used by patient derived cell lines, but also by primary T-
ALL patient isolates, we measured live-cell real-time metabolite uptake by NMR. T-ALL
CD34
+
/CD7
+
cells were isolated from two T-ALL patients at presentation. The flow cytometry
profile revealed that both samples had cytoplasmic CD3 expression, but lacked CD3 surface
expression. T-ALL_1 consisted of 60% CD34
+
blasts, half of which were CD7
+
. T-ALL_2 was
predominantly CD7
+
, as well as CD5
+
, CD2
+
, CD38
+
and CD4
+
. CD34
+
/CD7
+
cells were
isolated and 10
6
cells were resuspended in RPMI medium supplemented with GlutaMAX (L-
glutamine/L-alanine dipeptide) and used for measuring metabolite uptake. GlutaMAX is a
temperature-stable source of glutamine [88]. The L-glutamine/L-alanine dipeptide is
hydrolysed, by peptidases located on the plasma membrane, into L-Glutamine and L-
Alanine, which can then be taken up by the cells [89]. Therefore, the extracellular glutamine
and alanine concentration depends on GlutaMAX hydrolysis and their utilisation by the cells.
Using live-cell NMR we observed decreasing GlutaMAX availability in both patient samples
with a greater rate of hydrolysis observed in T-ALL_2 (Figure 6A). Additionally, T-ALL_1 was
taking up glucose and pyruvate and releasing lactate. Glutamine and alanine had bi-modal
kinetics with an initial decrease in concentration followed by a transient increase and a final
period of more rapid uptake. Changing metabolite availability in T-ALL_2 culture was very
similar for all the observed metabolites with concentrations steadily increasing by up to 25%
during the first 10 hours of the experiment followed by 20% decrease in the final 3h of the
measurements (Figure 6A). Live-cell NMR was also performed with ARR and DU528 cell
lines and similar dynamics of GlutaMAX hydrolysis were observed (Figure S7A). These
results confirm that primary T-ALL cells utilise glutamine similarly to T-ALL cell lines.
T-ALL cells express SLC1A3 and its protein product EAAT1 is localised in the
mitochondria
We demonstrated that the contribution of glutamine, directly and through aspartate, is crucial
for T-ALL nucleotide synthesis and proliferation. As glutamine uptake and its direct
contribution to nucleotide metabolism and TCA cycle is indispensable for whole organism
survival, we focused on identifying a useful therapeutic target for T-ALL treatment on
metabolic processes supporting glutamine-derived aspartate synthesis (Supplementary
Illustration SI3). In particular, the glutamate-aspartate anti-port across the mitochondrial
membrane would be a particularly interesting target as it would not target enzymatic
conversions directly. Aspartate export and glutamate import into mitochondria is known to be
facilitated by 3 different antiporters SLC1A3, SLC25A12 and SLC25A13 [90, 91]. While
SLC25A12 and SLC25A13 are ubiquitously expressed in all tissues, the expression of
SLC1A3 gene is mainly restricted to CNS [92]. Analyses of our RNAseq data, as well as
recently published gene expression data of 265 T-ALL patient samples, revealed that
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15
SLC1A3 is expressed in our model cell lines and in 95% of patient samples (Figure 6B,C)[8].
Additionally, EAAT1, the protein encoded by SLC1A3, was detected in the mitochondria of
the T-ALL cell lines. SLC1A3/EAAT1 was not detected in the acute myeloid leukaemia
(AML) cell line Kasumi-1, which does not express SLC1A3 (Figure 6D,E) [93]. Together,
these results show that, in addition to the common glutamate-aspartate antiporters, T-ALL
express the high affinity antiporter EAAT1.
SLC1A3/EAAT1 is essential for oncogenic de novo nucleotide synthesis
The mitochondrial localisation of EAAT1 in T-ALL cells supports the possibility that this
protein is involved in an oncogenic glutamate-aspartate antiport in T-ALL and crucial for T-
ALL proliferation and survival. In order to assess the importance of EAAT1 for T-ALL survival
we performed SLC1A3 knock-down using shRNA. Five shSLC1A3 were designed and
tested for their capacity to supresses the expression of SLC1A3 cDNA. Mouse fibroblasts
were co-transduced with retrovirus expressing SLC1A3-IRES-GFP and shSLC1A3. The
efficiency of the shRNA was measured relative to the negative control, shFF3, which targets
firefly luciferase and the positive control, shGFP, that supresses GFP expression originating
from the second cistron of the SLC1A3-IRES-GFP mRNA. shSLC1A3_1 and particularly _2
had the capacity to supress EAAT1 protein levels similar to shGFP, while the negative
control shFF3 did not have any effect on EAAT1 protein levels (Figure 7A). shSLC1A3_1
and _2 were cloned into a piggybac backbone that supports doxycycline-inducible shRNA
expression and integrated into the T-ALL cell genomes. Induction of shSLC1A3 lead to
rapid cell death in ARR, DU.528 and CCRF_CEM cells within 5 days of the doxycycline
treatment (Figure 7B). We were unable to produce the same results with HSB2 cells since
these cells were affected by doxycycline treatment alone. These results corroborate that
SLC1A3/EAAT1 is required for T-ALL cell proliferation and survival.
In healthy adults EAAT1 is normally expressed in the CNS [18, 92]. With the purpose to
inhibit its function in the CNS, two specific allosteric inhibitors, UCPH-101 and UCPH-102
were developed [94, 95]. Both drugs exhibited inhibitory effects on T-ALL proliferation and,
while UCPH-101 had a cytotoxic effect that reduced the survival of the control AML cell line
Kasumi-1, UCPH-102 had a specific anti-proliferative effect only on T-ALL cells (Figure 7C).
Cytotoxicity connected with UCPH-101 can be explained by the presence of known
toxicophores. Taken together, SLC1A3 knock-down and pharmacological inhibition caused
the supression of T-ALL proliferation. This effect is similar to the effect of glutamine removal
from the growth medium (Figure 5A). Finally, in order to confirm the mechanism of UCPH-
102 mediated SLC1A3 inhibition, we measured the metabolite uptake/release. UCPH-102
abolished glutamine uptake, while statistically significant changes in all four T-ALL cell lines
were only observed for glutamate and aspartate (Figure 7D and S7B). EAAT1 inhibition
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increased glutamate release in the ARR cell culture and decrease in the others, while
aspartate was reduced in the growth medium of all four cell lines (Figure 7D). Reduced
apartate release into the medium implies limited intracellular availability of aspartate that
would impact on nucleotide synthesis, hence T-ALL proliferation. This corroborates our
finding that EAAT1 is required for T-ALL survival and proliferation.
Development of EAAT1 inhibitors to block T-ALL cell proliferation
UCPH-101 and UCPH-102 were developed with the aim to reach the brain and therefore to
cross the blood-brain barrier. Despite extensive development from the original hit
compounds, these two compounds still have many unfavourable characteristics, including
toxicophores and a very short half life in vivo [96]. We therefore set out to identify new small
molecule inhibitors of EAAT1 with potential to be be further developed for future T-ALL
treatment. As the UCPH compounds show high specificity due to their allosteric inhibitory
function, an in silico approach was chosen on basis of the available crystal structure of
EAAT1 in complex with UCPH-101 [95]. The in silico screen was performed by Domainex
Ltd, using their database of approximately 2M commercially available compounds with lead-
like characteristics. Compounds were selected that have the potential to interact at the same
position as UCPH-101 and were screened in an in vitro cell assay, using CCRF_CEM and
the Kasumi-1 cell line as a negative control. This cellular proliferation screen resulted in
compounds that showed an enhanced effect on the growth of CCRF_CEM when compared
to the results from Kasumi-1. EAAT1inh_1 showed a significant effect at concentrations
below 1 uM (Figure 7F).
Discussion
Describing metabolic pathways is pivotal for understanding the processes that are essential
for embryonic development, homeostasis and disease progression. Metabolic processes are
interconnected and many steps are supported by several different enzymes that can be
located in different cellular compartments and even different tissues and organs.
Understanding the capacity of the cells to utilise a certain metabolic pathway based on the
gene expression and combining this with the measurements of cellular demand and output is
essential for building a bigger picture of the metabolic processes that support cellular
proliferation and survival. One of the main characteristics of cancer cells is increased
proliferation. During the oncogenic transformation, changes in gene expression and cellular
signalling allow uncontrolled proliferation. In order to successfully achieve this, cancer cells
may use processes that are more commonly observed during embryonic development, when
large quantities of nutrients are used for growth and organogenesis. Understanding the
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17
metabolic pathways that support oncogenic proliferation can help us identify cancer-specific
processes and rate limiting steps that can be used for developing new therapies.
Our results demonstrate that integration of gene expression, signal transduction and
metabolic studies provides a powerful approach for the dissection of key metabolic
processes. We show that, in T-ALL cells, mTOR regulates glutamine uptake. Glutamine is
converted to glutamate that enters the mitochondria where Glutamate Dehydrogenase
(GLUD1,2) uses it to generate α-ketoglutarate that will enter the TCA cycle (Figure 7E).
Additionally, mitochondrial Glutamate Oxaloacetate Transaminase (GOT2) uses glutamate
as a donor for the amino group, which is transferred to oxaloacetate, another TCA cycle
intermediate, resulting in generation of aspartate and α-ketoglutarate. Aspartate is then
transported out of the mitochondria in exchange for a new glutamate molecule, continuing to
fuel the TCA cycle and generating aspartate. Together, glutamine and aspartate are used as
substrates for nucleotide synthesis (Figure 7E). Similar processes are likely to occur in other
cancers. Indeed, recently EAAT1 has been implicated in supporting proliferation in several
cancer cell lines representing solid cancers [97, 98]. Both studies showed a main role for
EAAT1 in the uptake of aspartate from the medium, especially under conditions of glutamine
deprivation or asparaginase treatment. In both studies removing EAAT1 under normal cell
culture conditions showed little or no effect in contrast to our work on T-ALL cells. This
difference can be explained as the solid tumour cell lines were mainly dependent on EAAT1
for aspartate/glutamate uptake from the environment and the lack thereof resulted in a
combinatorial effect on the TCA cycle, the electron transport chain, and de novo
glutamine/glutamate and nucleotide synthesis. T-ALL cells on the other hand are dependent
on the availability of glutamine. They rely heavily on the function of EAAT1 on the inner
membrane of the mitochondria, where glutamate/aspartate antiport function is required for
de novo nucleotide production. This process is heavily dependent on the availability of
glutamate, which is derived from glutamine. Our NMR tracer experiments show clearly how
both the carbon and nitrogen atoms of glutamine are used for aspartate, purine and
pyrimidine biosynthesis. Sun et al. also showed that asparaginase treatment resulted in a
cellular depletion of glutamine/glutamate, possibly explaining why asparaginase treatment is
often effective in treatment of T-ALL [99].
Systemic targeting of the metabolic enzymes that conduct the glutamine to aspartate
conversion or nucleotide synthesis would be detrimental. Our finding that the transport of
aspartate to the cytoplasm is an essential step in nucleotide production and that it is
facilitated by aberrantly expressed EAAT1, identifies this protein as a novel therapeutic
target for developing treatments for T-ALL. EAAT1 is normally present in the CNS on the
plasma membrane of neurons and glia where it uptakes glutamate from the glutamatergic
synapses [17]. Based on the RNA expression and protein localisation assessed with three
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18
different specific antibodies, the Human Protein atlas Database reports that EAAT1 is not
found outside of the CNS (https://www.proteinatlas.org/ENSG00000079215-SLC1A3/tissue)
[100]. However, EAAT1 expression has been reported in neonatal cardiomyocytes where,
similar to T-ALL, it is also localised in mitochondria together with two other glutamate-
aspartate antiporters, i.e. ARALAR1 and CITRIN [90]. Altogether, this means that the
aberrant expression of SLC1A3 in T-ALL renders this gene as an oncogene and EAAT1 a
therapeutic target. We therefore performed a screen for novel small molecule inhibitors and
we present EAAT1inh_1 compound as a potent EAAT1 inhibitor.
The rate limiting reaction in aspartate synthesis is a glutamate-aspartate antiport, which is
normally carried out by ARALAR1 and CITRIN (SLC25A12 and SLC25A13) [91]. ARALAR1
and CITRIN are calcium-binding mitochondrial carrier proteins that import a glutamate
molecule together with a H+ into the mitochondria in exchange for the export of an aspartate
anion [101-103]. Their activity depends on the mitochondrial membrane potential that is
maintained by cytoplasmic ATP [104]. Both transporters are present in the majority of cells
and tissues (reviewed in [105]). We found that SLC25A12 is expressed in all four T-ALL cell
lines, while SLC25A13 was found only in DU.528. With this in mind, an obvious question is
why mitochondrial EAAT1 is necessary for T-ALL survival. One possibility is that EAAT1 has
a higher Kd for glutamate than ARALAR1and CITRIN and therefore facilitates a higher rate
of antiport across the mitochondrial membrane. Even though the kinetics of all three
transporters are reported, the experiments were not performed in comparable experimental
conditions and EAAT1 capacity was always addressed in respect to its membranous
localisation [105-107]. The second possibility is that the intracellular conditions, such as the
pH, ATP availability and mitochondrial action potential, are restrictive for ARALAR1 and
CITRIN function, whereas EAAT1 is independent of these factors.
Acknowledgments
We would like to thank Dr. A.W. Langerak, (Erasmus Medical Centre, Rotterdam, NL) for the
provision of ARR and DU.528 cell lines and Prof P.N. Cockerill (University of Birmingham,
UK) for HSB2 and CCRF-CEM. We would like to thank Dr. M. McGrew (University of
Edinburgh, UK) for the PB_tet-on_Apple_shGFP plasmid and Prof. L. Bunch for pcDNA3-
EAAT1 plasmid. We would like to acknowledge BlueBEAR High Performance Computing
(HPC) service for supporting the analyses of the genome-wide data and Biomolecular NMR
Facility at the Henry Wellcome Building for Nuclear Magnetic Resonance (HWB-NMR),
University of Birmingham. This work was supported by Bloodwise, through a Bennett
Fellowship to M.H. [11002], the Medical Research Council and the University of Birmingham.
Authorship Contributions
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19
Original Concept V.S.S., Project Planning, Experimental Design, Project supervision, V.S.S.,
M.H.; Genome-wide data acquisition and Bioinformatical analyses V.S.S., E.G., H.P., M.H.;
Statistical analyses V.S.S.; NMR data acquisition and analyses M.A.C.R, J.R., V.S.S, U.G.,
C.L.; Drug screen V.S.S.; All other Experimentation, Data Acquisition, Processing and
Analyses V.S.S., M.H.; Provision of patient samples S.P., G.P.; Reagents provision S.S.;
Figures V.S.S.; Writing of the Manuscript, V.S.S., M.A.C.R. and M.H.
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20
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Legends
Figure 1. Differential gene expression and gene ontology analysis for T-ALL cell lines.
A) Heat map showing hierarchical clustering of RNAseq gene expression data, based on
Pearson correlation with complete linkage clustering of all differentially expressed genes
between the T-ALL cell lines. Clusters are marked by blue triangles and numbered from 1 to
11. Scale bar represents log2 FPKM values. B) Gene ontology enrichment analysis for
cluster 1, 9 and 10. Terms are ordered based on Modified Fisher Extract P-value and shown
as percentage of input genes (% genes). C) UCSC genome browser screenshots showing
the distribution of reads across the ASS1 and ACSS1 from a representative set of RNAseq
tracks. D) Validation of RNAseq results by qPCR, relative to rRNA levels. Data points are the
mean of at least three independent samples measured in duplicate ± StDev.
Figure 2. T-ALL cell lines have aberrant mTOR signalling. A) Protein synthesis was
assessed by Click-iT OPP protein synthesis kit and imaged by confocal microscopy.
Representative images are shown. B) Quantification of protein synthesis using CellProfiler
software in arbitrary units [AU]. C) Rapamycin inhibits SIL-TAL proliferation. Cell cultures
were set up in the presence of 10nM rapamycin or vehicle (DMSO) and cultured for 4 days.
Four independent cell cultures were assayed per cell line and each point represents the
mean ± StDev. Significant differences were found when ARR was compared to any of the
SIL-TAL cell lines at day 3 and 4 (p<0.05). D-E) Protein levels and phosphorylation status of
mTOR regulators (D) and mTOR effectors (E). Western blot analysis of 150µg cell extract.
Treatment was DMSO (-,Ctrl) or 10nM rapamycin (+,Rap) for 24h. PonceauS staining was
used to confirm equal loading. Each experiment was performed at least three times and
representative results are shown. F) Confocal microscopy images of immunofluorescent
staining for mTOR (green), LAMP1 (red), LysoTracker (grey), DAPI (blue) shows the nuclei.
The overlay panel shows mTOR/LAMP1 localisation.
Figure 3. T-ALL cell lines have constitutively active AMPK that inhibits autophagy and
lysosomal function in SIL-TAL. A-C) The effect of mTOR inhibition on autophagy. A)
Western blots showing protein levels and phosphorylation status of AMPK, ULK-1 and
Beclin-1. B) Confocal microscopy images illustrate LC3 (green) and LAMP1 (red)
distribution. DAPI (blue) shows the nuclei. Boxed area from the overlay image is enlarged in
inset. C) p62 and LC3 protein levels. D,E) The effect of AMPK inhibition on autophagy. D)
LC3 and p62 protein level. E) Confocal images of the LC3 (green), LysoTraker (red), LAMP1
(magenta) immunostainings. Treatment was 24h 10nM rapamycin (+,Rap) or 4h 5µM
dorsomorphine (+,D). Control was DMSO (-,Ctrl). PonceauS was used to confirm equal
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26
loading. Experiments were performed at least three times and representative results are
shown.
Figure 4. Metabolite levels in T-ALL cells are dynamic and sensitive to mTOR activity.
A) Heat map showing hierarchical clustering of the growth medium metabolites after 24h.
Data are the average of four independent experiments ± StDev. Scale bar represents log2
relative metabolite concentration. Metabolites with significantly different levels are shown. B)
Rapamycin changes metabolite uptake and release. T-ALL cell lines were grown in the
presence of rapamycin or vehicle (DMSO) for 24h. Relative metabolite levels are the mean
of four independent experiments. Two-tailed t-test identified the difference in glutamine,
glucose and lactate concentration as significantly different with p<0.05 between the cells
grown with vehicle to rapamycin in all four cell lines. C) Heat map showing hierarchical
clustering of intracellular metabolite levels after culturing T-ALL cells for 24h. Data are the
average of at least three independent experimental measurements ± StDev. Four different
metabolite clusters (MC1-4) were identified. Scale bar represents log2 metabolite levels.
Only metabolites with significantly different levels between at least two of the cell lines are
shown. D) Abundance of intracellular metabolites at 8h or 24h, relative to the time of medium
change (0h). Fold change was presented only for metabolites with significantly different
relative levels with p<0.05. E) Heat map showing hierarchical clustering of the log2 ratio
between metabolite levels in rapamycin treated cells and the control. Metabolites were
isolated after culturing T-ALL cells for 24h in the presence of 10nM rapamycin or vehicle
(DMSO) from at least three independent samples. Six different metabolite clusters are
labelled (RMC1-6). Scale bar represents log2 ratio between rapamycin-treated cells and the
control. Only metabolites with significantly different levels are shown. GPC, L-Alpha
glycerylphosphorylcholine; GalNAc, Acetylgalactosamine, GlcNAc, N-Acetylglucosamine.
Figure 5. T-ALL cells utilise glutamine-derived nitrogen and carbon for de novo
nucleotide synthesis. A) Glutamine deprivation inhibits SIL-TAL proliferation. T-ALL cell
lines were cultured in medium with 10% FCS, with or without 2mM GlutaMax. Four
independent cell cultures were assayed per cell line and each point represents the mean ±
StDev. Significant differences were found when ARR was compared to any of the SIL-TAL
cell lines at day 6 and 8 (p<0.05). B-D) Metabolite tracing experiments using [2,5-
15
N]
Glutamine and [3-
13
C] Glutamine. T-ALL cell lines were grown in the presence of 2mM [2,5-
15
N] Glutamine) (B,C) or [3-
13
C] Glutamine) (D). B) Overlay of 1D
1
H-NMR spectra showing
the Hβ-aspartate resonance after 0, 8 and 24h and schematic representations of [2,5-
15
N]
Glutamine and the observed [2-
15
N] Aspartate.
15
N are in red and shading indicates the
observed
3
J scalar couplings between the aspartate Hβs and glutamine-derived
15
N. The X-
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27
axis shows the chemical shift relative to TMSP in ppm and the Y-axis indicates TSA scaled
intensity. C) Resonances observed in
1
H-
15
N-HSQC for [U-
15
N] ATP, [U-
15
N] GTP, [U-
15
N]
UTP
standards and for T-ALL cells extracts grown in the presence of Glutamine-
15
N
2
for 24h.
Resonances are marked by letters a-e. Schematics show ATP, GTP and UTP with colour-
coded atoms based on the substrate of their origin (glutamine-purple, aspartate-orange,
glycine-green, carbonate-black and
15
N-red). Blue shaded lines indicate observed couplings
annotated a-e. D) Resonances observed in
1
H-
13
C-HSQC for T-ALL cells grown in the
presence of [3-13C] Glutamine for 24h. Resonances are marked by letters a-e. Schematic
on the right shows aspartate and UTP with colour-coded atoms based on the substrate of
their origin. Blue shaded lines indicate observed couplings annotated a-e.
Figure 6. EAAT1 is expressed in T-ALL and localised in mitochondria. A) Patient
derived T-ALL cells have similar metabolic uptake to T-ALL cell lines. Only metabolites with
changing concentration are illustrated. B) Summary of the SLC1A3 expression levels as
assessed by FPKM values from the RNA-seq of 265 patient samples. Data are grouped by
the level of expression. C) SLC1A3 mRNA expression level in T-ALL cell lines and AML cell
line Kasumi-1, relative to rRNA level assessed by qPCR. D) Western blot analysis of EAAT1
protein level using 150 µg total cell extract. PonceauS shows equal loading. E) Immuno-
fluorescent imaging shows that EAAT1 (green) co-localises with MitoTracker Red CMXRos
in T-ALL but not Kasumi-1.
Figure 7. EAAT1 is essential for T-ALL proliferation and survival. A) Protein level of
EAAT1 and the reporter GFP protein upon the suppression of SLC1A3-IRES-GFP mRNA by
shFF3 (negative control), shGFP (positive control) and five different shSLC1A3_1-5.
Experiment was performed in duplicate. PonceauS illustrates equal loading. B) Knock-down
of the SLC1A3 gene by shSLC1A3_1 and shSLC1A3_2 leads to ARR, DU.528 and
CCRF_M cell death. C) To test the effect of EAAT1 inhibition, T-ALL and AML cells were
cultured for six days in the presence of vehicle (DMSO), 25 µM UCPH-101, or 25 µM UCPH-
102. Each data point is an average of three independent measurements ± StDev. D) UCPH-
102 induced changes in metabolite uptake and release. T-ALL cell lines were grown in the
presence of 25µM or vehicle (DMSO) for 48h. Relative metabolite levels are the mean of
four independent experiments. Two-tailed t-test identified the difference in glutamate and
aspartate concentration as significantly different with p<0.05 between the cells grown with
vehicle to UCPH-102 in all four cell lines. E) Model illustrating the function of mitochondrial
EAAT1 function. F) Identification of novel EAAT1-specific allosteric inhibitor. The left graph
shows the effect of the inhibitor at 25 µM on proliferation of the Kasumi-1 and T-ALL cell
lines. The right histogram shows potency of drug at the indicated inhibitor concentrations on
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CCFR-CEM cells over the course of seven days. Each data point is relative to the starting
number of cells and represents an average of three independent measurements ± StDev.
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C
Figure 1
B
D
ARR
DU.528
HSB2
CCRF_M
0
5
10
15
20 ASS1
0
0.1
0.2
0.3 ACSS1
ARR
DU.528
HSB2
CCRF_M
mRN A level relative to rRN A
ARR
DU528
HSB2
CCRF
0.29
1
-1
1
Log2 FPKM 1 7
1
2
3
4
5
6
7
8
9
10
11
A
Cluster_1_Genes Supressed in SIL-TAL
Biosynthesis of antibiotics
Thyroid hormone signaling pathway
Protein processing in endoplasmic reticulum
Carbohydrate digestion and absorption
Carbon metabolism
Citrate cycle (TCA cycle)
Aldosterone-regulated sodium reabsorption
Metabolic pathways
Biosynthesis of amino acids
HIF-1 signaling pathway
Aldosterone synthesis and secretion
Calcium signaling pathway
Central carbon metabolism in cancer
Cholinergic synapse
Ubiquitin mediated proteolysis
Glutamatergic synapse
Type II diabetes mellitus
Pancreatic secretion
% of genes
0 5 10
Cluster_9_Genes Upregulated in SIL-TAL
T cell receptor signaling pathway
Rap1 signaling pathway
Neurotrophin signaling pathway
Axon guidance
Primary immunodeficiency
% of genes
0 2 4
Cluster_10_Genes Upregulated in SIL-TAL
Pyrimidine metabolism
Metabolic pathways
Peroxisome
Purine metabolism
Sphingolipid metabolism
% of genes
02468
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Figure 2
A
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 2 4
No of Cells Rapamycin / Control
Time [days]
ARR
DU528
HSB2
CCRF
D
E
F
0
0.2
0.4
0.6
0.8
1
ARR DU.528 HSB2 CCRF_M
Fluorescence
Intensity [AU]
B
C
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C
ARR
DU.528
HSB2
CCRF_M
- + - + - + - + rapamycin
P62
LC3-I
LC3-II
LC3-I
LC3-II
PonceauS
Figure 3
p-AMPKS172
AMPK
p-ULK1S555
ULK1
p-BeclinS93
Beclin
PonceauS
ARR
DU.528
HSB2
CCRF_M
- + - + - + - + rapamycin
AB
D
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Threonine
Lactate
Formate
Pyruvate
Serine
Alanine
Proline
Glutamate
Pyroglutamate
Lysine
Glucose
Isoleucine
Histidine
Valine
Tyrosine
Leucine
Arginine
Aspartate
Glycine
Tryptophan
Phenylalanine
Glutamine
Figure 4
ARR
CCRF_M
DU.528
HSB2
-1 0 1
Log2 Medium+Cells / Medium
C
Log2 AU 11 22
MC:
1
2
3
4
ARR
DU.528
HSB2
CCRF_M
Alanine
GPC
Phosphatidylcholine
Glutamate
Taurine
Aspartate
ATP/ADP
Myoinositol
Fumarate
UDP-GalNAc
UDP-GlcNAc
UDP-Glucose
Glutamine
Creatine
Phosphocreatine
Succinate
AMP
NAD+
Glutathione
Leucine
Glycine
B
A
ARR 8/0h
ARR 24/0h
DU.528 8/0h
DU.528 24/0h
HSB2 8/0h
HSB2 24/0h
CCRF_M 8/0h
CCRF_M 24/0h
-1 Log2 Fold Change 1
Tyrosine
Isoleucine
Leucine
Phenylalanine
Valine
NAD+
Glutamine
Aspartate
UDP-GlcNAc
GPC
UDP-Galactose
UDP-Glucose
Alanine
UDP-GalNAc
Glutathione
Glutamate
Phosphocreatine
Fumarate
Taurine
ATP/ADP
Phosphatidylcholine
Lactate
Acetate
Glycine
AMP
Succinate
GXP
Creatine
Myoinositol
D
-1 1
Log2 Rap/Ctrl
ARR
DU. 528
HSB 2
CCR F_M
Succinate
AMP
UMP
Lactate
Phenylalanine
Leucine
Tyrosine
Isoleucine
Valine
Creatine
Taurine
Myoinositol
Glutamine
UDP-GlcNAc
UDP-GalNAc
Aspartate
Glycine
Glutamate
Acetate
Benzoate
Phosphatidylcholine
GPC
Alanine
UDP-Glucose
Fumarate
Phosphocreatine
NAD+
Glutathione
RMC:
1
2
3
4
5
6
E
ARR
DU.528
HSB2
CCRF_M
Glutamine
ARR
DU.528
HSB2
CCRF_M
Glucose
ARR
DU.528
HSB2
CCRF_M
Lactate 1.5
1
0.5
8
6
4
2
1.5
1
0.5
Relative Level [AU]
Ctrl
rapamycin
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Asp
0h 8h 24h
2. 81 2.80 1H [ppm]
2. 81 2.80 2. 81 2.80
ARR
DU.528
HSB2
CCRF_M
a
DU.528
13C
[ppm]
UTP
ARR DU.528 HSB2 CCRF_M
144 -
105 -
8 7.9
6 5.9
105.5 -
6 5.9 6 5.9 6 5.9
145 -
8 7.9 8 7.9 8 7.9
a a a a
b b b b
Asp
ARR DU.528 HSB2 CCRF_M
55 -
4 3.9 4 3.9 4 3.9 4 3.9
39.4 -
1H [ppm]
Myo-Inositol
ARR HSB2 CCRF_M
75 -
4.1 4.1 4.1 4.1
2.9 2.7 2.9 2.7 2.9 2.7 2.9 2.7
b b‘ b b’ b b’ b b’
a a a a
b b’
Figure 5
B
D
A
0
0.2
0.4
0.6
0.8
1
1.2
0 2 4 6 8 10
No of Cells 0mM / 2mM
Glutamine
Time [days]
Glutamine Effect on Cell Proliferation
ARR
DU.528
HSB2
CCRF_M
C
8.15 8.05 8.15 8.05 8.15 8.05 8.15 8.05 8.15 8.05
205 -
230 -
170 -
15N
[ppm]
a
b d
e
c
a’ a a’’
b’ b d
e
a’ a a’’
b’ d
e
a’ a a’’
b d
e
a’ a a’’
b’ b d
e
[U-15N] ATP ARR DU.528 HSB2 CCRF_M
8.5 8.2 8.5 8.2 8.5 8.2 8.5 8.2 8.5 8.2
170 -
[U-15N] GTP ARR DU.528 HSB2 CCRF_M
230 -
1H [ppm]
a
b
c
b’ b b’ b b’ b b’ b
Intensity [AU]
15N
[ppm] [U-15N] UTP ARR DU.528 HSB2 CCRF_M
6.0 8.0 6.0 8.0 6.0 8.0 6.0 8.0 6.0 8.0
160 -
145 -
1H [ppm]
b b b b b
a c a a’ c a a’ c a a’ c a a’ c
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0
0.5
1
1.5
2
2.5
ARR DU.528 HSB2 CCRF_M Kasumi
SLC1A3 mRNA level
Rel ati ve mR NA
l ev el
C
FPRK 0
Low
[<0.1]
Medium
[0.1-1]
High
[1-40]
No of
T-ALL 10 133 85 37
B
SLC1A3 mRNA levels in T-ALL patients
Figure 6
50
75
100
125
150
0 2 4 6 8 10 12 14
Lactate Alanine
GlutaMAX Pyruvate
Glutamine Glucose
50
75
100
125
150
0 2 4 6 8 10 12 14
T-ALL_1
T-ALL_2
Time [h]
Time [h]
Relative Metabolite Levels Relative Metabolite Levels
A
WB:
EAAT1
PonceauS
ARR
DU.528
HSB2
CCRF_M
Kasumi1
D
DAPI EAAT1 MitoTracker Overlay
ARR
DU.528
HSB2
CCRF_M
Kasumi1
E
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A
shFF3
shGFP
shSLC1A3_1
shSLC1A3_2
shSLC1A3_3
shSLC1A3_4
shSLC1A3_5
WB:
EAAT1
GFP
PonceauS
0
100
200
300
D1 D3 D5
ARR
B
Time [Days]
Time [Days]
Relative Cell Number
0
100
200
300
D1 D3 D5
DU.528
0
20
40
D1 D3 D5
CCRF_M
Time [Days]
shFF3
shSLC1A3_1
shSLC1A3_2
C
Time (days)
Total cell number (*106)
0
1
2
3
4
0 2 4 6
0
10
20
30
0 2 4 6
Time (days)
UCPH-101 UCPH-102
0 0.5 1 1.5 2
ARR
DU
HSB
CEM
Glutamate
D
0 1 2 3
ARR
DU
HSB
CEM
Aspartate
Ctrl
UCPH-102
Metabolite level [AU]
Metabolite level [AU]
Figure 7
E
Relative Cell Number
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F
EAAT1inh_1
30
20
10
Kasumi-1
ARR
DU.528
HSB2
CCRF_M
25
5
2.5
1.25
0.62
0.31
0.15
0.07
0
250
200
150
100
50
Concentration [μM]
Total cell number (*106)
Total cell number (*106)
EAAT1inh_1
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