Abstract
The integrated stress response (ISR) is a corrective physiological
programme to restore cellular homeostasis that is based on the
attenuation of global protein synthesis and a resource-enhancing
transcriptional programme. GCN2 is the oldest of four kinases that are
activated by diverse cellular stresses to trigger the ISR and acts as
the primary responder to amino acid shortage and ribosome collisions.
Here, using a broad multi-omics approach, we uncover an ISR-independent
role of GCN2. GCN2 inhibition or depletion in the absence of
discernible stress causes excessive protein synthesis and ribosome
biogenesis, perturbs the cellular translatome, and results in a dynamic
and broad loss of metabolic homeostasis. Cancer cells that rely on GCN2
to keep protein synthesis in check under conditions of full nutrient
availability depend on GCN2 for survival and unrestricted tumour
growth. Our observations describe an ISR-independent role of GCN2 in
regulating the cellular proteome and translatome and suggest new
avenues for cancer therapies based on unleashing excessive mRNA
translation.
Graphical Abstract
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Introduction
Normal cellular function and tissue health rely upon the precise
regulation of the cellular proteome to ensure the provision of specific
proteins at ideal concentrations at all times ([62]Vabulas & Hartl,
2005; [63]López-Otín et al, 2013; [64]Kaushik & Cuervo, 2015; [65]Hipp
et al, 2019; [66]Kurtishi et al, 2019; [67]Anisimova et al, 2020;
[68]Takemon et al, 2021). To maintain proteostasis, cells must
coordinate mRNA translation and protein degradation, which in turn are
balanced with metabolic processes that orchestrate the provision and
use of macromolecular building blocks and energy. Under anabolic
conditions, such as those driven by oncogenic signalling in cancer
cells, the increase in global protein synthesis, which is tied to an
increase in the biogenesis of ribosomal proteins, ribosomal RNA, and
translation factors, places an additional burden on the cellular
mechanisms of proteostasis ([69]Kamphorst et al, 2015; [70]Sullivan et
al, 2019; [71]Faubert et al, 2020; [72]Pavlova et al, 2022; [73]Finley,
2023).
The integrated stress response (ISR) is a fundamental homeostatic
reaction and one of the pivotal mechanisms of cellular proteome
regulation ([74]Harding et al, 2003). In animals, four kinases couple
different stress signals to the phosphorylation of the translation
initiation factor eIF2α, which results in global attenuation of mRNA
translation. At the same time, the preferential translation of the
transcription factor ATF4 triggers a programme of resource optimisation
that includes suppression of mTORC1, activation of autophagy, and
increased amino acid synthesis and uptake ([75]Tallóczy et al, 2002;
[76]Harding et al, 2003; [77]B’chir et al, 2013; [78]Ye et al, 2015;
[79]Bröer et al, 2016). GCN2 is the oldest of the four eIF2α kinases
and is conserved in all eukaryotes. GCN2-mediated ISR signalling
modulates a range of biological processes such as circadian physiology
([80]Pathak et al, 2019), learning and memory ([81]Costa-Mattioli et
al, 2005), haematopoietic and skeletal stem cell function ([82]Hu et
al, 2020; [83]Li et al, 2022), hepatic red blood cell clearance and
iron metabolism ([84]Toboz et al, 2022), and antitumour immunity
([85]Halaby et al, 2019). Although GCN2 is activated in response to
diverse stresses, the best-studied role of GCN2 is in the cellular
adaptation to amino acid deficiency, where GCN2 is activated by
uncharged tRNAs ([86]Wek et al, 1989, [87]1995; [88]Dong et al, 2000).
GCN2 activation also occurs upon ribosome stalling as a consequence of
translation elongation problems and requires the interaction of GCN2
with the ribosomal P stalk ([89]Wek et al, 1995; [90]Dong et al, 2000;
[91]Harding et al, 2000; [92]Castilho et al, 2014; [93]Ishimura et al,
2016; [94]Inglis et al, 2019; [95]Masson, 2019; [96]Wu et al, 2020). In
line with their high demand for amino acids to sustain proliferation
and growth, GCN2 promotes cancer cell survival under conditions of
nutrient scarcity ([97]Ye et al, 2010; [98]Parzych et al, 2019;
[99]Cordova et al, 2022; [100]Missiaen et al, 2022; [101]Nofal et al,
2022). GCN2 also protects haematopoietic cancer cells from deleterious
effects of asparaginase treatment and proteasome inhibition, two
commonly used anti-cancer therapies that trigger intracellular amino
acid depletion ([102]Suraweera et al, 2012; [103]Nakamura et al, 2018;
[104]Saavedra-García et al, 2021). We have recently shown that a subset
of tumours of diverse tissue origins share transcriptional signatures
with cancer cell lines whose survival is dependent on GCN2 without
suffering evident stress ([105]Saavedra-García et al, 2021). Together
with recent observations that GCN2 supports the proliferation of colon
cancer cells under nutrient-rich conditions ([106]Piecyk et al, 2024),
the observations indicate that GCN2 regulates important cellular
functions in an ISR-independent manner.
Using an integrated systems-level approach that includes transcriptome,
proteome, translatome, and metabolome profiling, we show that in a
subset of cancer cells, GCN2 prevents excessive ribosome biogenesis and
protein synthesis under conditions of optimal nutrient availability.
This function of GCN2 is distinctly different from and independent of
its canonical role in the ISR, is largely regulated on a translational
level, and is required for unimpeded tumour growth and cancer cell
survival and for maintaining metabolic homeostasis.
Results
GCN2 maintains the cancer cell transcriptome and proteome independently of
the ISR
According to the Cancer Dependency Map (DepMap) portal, ∼8% of cancer
cell lines are dependent on GCN2 based on CRISPR screening
([107]https://www.proteinatlas.org/humanproteome/tissue/druggable). To
confirm the deleterious effect of GCN2 inactivation in dependent cell
lines, we treated 16 different solid tumour cell lines of varying
degrees of GCN2 dependency, as well as four different types of primary
healthy cells: CD34^+ haematopoietic stem cells, mesenchymal stromal
cells, human umbilical vein endothelial cells (HUVECs), and human
dermal fibroblasts (HDFs), with the well-characterised GCN2 inhibitor,
GCN2iB ([108]Nakamura et al, 2018; [109]Saavedra-García et al, 2021;
[110]Missiaen et al, 2022). We confirmed that GCN2 inhibition triggers
cell death in cell lines defined as GCN2-dependent by DepMap under
conditions of optimal nutrient availability, but not in those with low
GCN2 dependency, or in the healthy primary cells we tested ([111]Fig
1A). Based on their response to 48 h of 1 μM GCN2iB treatment, cancer
cell lines were classified into three categories: highly dependent
cells, which showed greater than 50% cell death; dependent cells, which
exhibited 10–50% cell death; and independent cells with less than 10%
death after treatment. We further confirmed GCN2 dependency by knocking
down GCN2 in 3 cell lines ([112]Figs 1B and [113]S1A–C). Thus, survival
of a subset of cancer cells depends on GCN2 in the absence of nutrient
depletion or other stressors known to activate GCN2 or the ISR. To
study the mechanistic basis for this potentially ISR-independent role
of GCN2, we first carried out transcriptome analysis by RNA sequencing
(RNA-seq) in the melanoma cell line A375 as a model for GCN2-dependent
cells. Using gene set enrichment analysis (GSEA) ([114]Korotkevich et
al, 2021 Preprint), we identified “MYC targets” and “E2F targets” as
the most positively enriched categories ([115]Fig 1C), indicating that
GCN2 inhibition results in the activation of a transcriptional
programme conducive to cell growth and proliferation despite its
detrimental effect on viability. On the contrary, we observed that
major metabolic pathways such as “glycolysis” and “fatty acid
metabolism” were negatively enriched upon GCN2 inhibition ([116]Fig
1C). Many of the genes included in the Hallmark term “MYC targets” are
involved in proteostasis control, including genes coding for ribosomal
and proteasomal subunits and translation initiation factors. To further
explore how GCN2 regulates the proteome under conditions of nutrient
abundance, we therefore carried out quantitative proteome profiling of
GCN2iB-treated A375 cells. Our GSEA of differentially expressed
proteins was largely concordant with the results of the transcriptome
analysis, confirming “MYC targets” and “E2F targets” as the most
enriched and “glycolysis” and “fatty acid metabolism” as suppressed
pathways ([117]Fig 1D). Notable pathways with discordant involvement
between transcriptomic and proteomic responses included “Unfolded
Protein Response” and “protein secretion,” which were induced at the
mRNA level but repressed at the protein level ([118]Fig 1C and D). In
line with these results, Gene Ontology (GO) analyses of both the
transcriptome and proteome data showed a dominant up-regulation of
mRNAs and proteins involved in ribosome and ribonucleoprotein complex
biogenesis, whereas processes linked to extracellular matrix structure
and organisation were repressed ([119]Fig S1D).
Figure 1. GCN2 regulates the cancer cell proteome and transcriptome.
[120]Figure 1.
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(A) Heatmap showing the mean change in viability in the indicated
primary cells and cell lines. Data are expressed as log[2]
GCN2iB-treated/DMSO control cells (cell lines, HDFs, and HUVECs, n = 3;
CD34^+, n = 2; mesenchymal stromal cells, n = 4). The cancer cell line
dependency classification is based on the indicated percentage of cell
death after 48 h of GCN2iB treatment at 1 μM. (B) Viability of A375
cells after GCN2 knockdown. Data are shown as the mean ± SEM of four
independent experiments. Statistical significance was determined by
two-way ANOVA and Sidák’s test for multiple comparisons. *P < 0.05, **P
< 0.005. (C) GSEA of mRNA-level changes in A375 cells treated with
GCN2iB (1 μM, 48 h), as determined by RNA-seq, shown as lollipop plots
indicating normalised enrichment scores, and enrichment plots of
selected Hallmark categories. (D) GSEA of protein abundance changes in
A375 cells treated with GCN2iB, as determined by TMT labelling
proteomics, shown as lollipop plots indicating normalised enrichment
scores (NES), and enrichment plots of selected Hallmark categories.
Lollipop colours in (C, D) indicate the degree of statistical
significance (grey = not significant). (C, E) Correlation of NES from
GSEA of RNA-seq data from cultured cells treated with GCN2iB as
described in (C) and from tumour samples after 10 d of oral GCN2iB
treatment. (C, F) Correlation of NES from GSEA of RNA-seq data from
cultured cells treated with GCN2iB as described in (C) and tumour
samples after 15 d of doxycycline-induced shRNA expression. (E, F) Red
and blue dots indicate significantly (adjusted P < 0.05) up-regulated
and down-regulated Hallmark categories. Dark red and blue highlight
selected categories of interest.
Figure S1. Transcriptome and proteome changes induced by GCN2 inhibition or
depletion.
[122]Figure S1.
[123]Open in a new tab
(A) Viability of A2058 cells after GCN2 knockdown. Data are shown as
the mean ± SEM of four independent experiments. Statistical
significance was determined by two-way ANOVA and Sidák’s test for
multiple comparisons. (B) Viability of MiaPaCa2 cells after GCN2
knockdown. Data are shown as the mean ± SEM of four independent
experiments. Statistical significance was determined by two-way ANOVA
and Dunnett’s test for multiple comparisons. (C) Immunoblot analysis of
GCN2 levels in A375, A2058, and MiaPaCa2 cells for days after
transduction with lentiviral particles carrying one of two different
shRNAs against GCN2 or scramble control (Scr). (D) Top 10 induced and
repressed Biological Process (BP) Gene Ontology (GO) terms in the
transcriptome and proteome of A375 cells after treatment with GCN2iB
(48 h, 1 μM). (E) GCN2 transcript levels as determined by RNA-seq in
xenografted tumours generated with A375 cells carrying a shRNA against
GCN2 or a scramble control. Data are shown as the mean ± SEM of four
mice. (F) Fold tumour growth relative to tumour volume at baseline
after 10 d of treatment with 10 mg/kg GCN2iB. Data are shown as the
mean ± SEM of six mice. (G) Fold tumour growth relative to tumour
volume at baseline after 15 d of shRNA induction with doxycycline. Data
are shown as the mean ± SEM of four mice. (E, F, G) Statistical
significance was determined by an unpaired t test. *P < 0.05, **P <
0.005, ***P < 0.001, ****P < 0.0001.
We next inhibited or knocked down GCN2 in xenografted A375 cells to
study its role in vivo. In the pharmacological model, mice were
injected with A375 cells and treated with GCN2iB for 10 d from the time
when tumours became palpable. In the genetic model, we xenografted A375
cells carrying a doxycycline-inducible shRNA targeting GCN2 ([124]Fig
S1E). With these approaches, we observed a reduction in tumour growth
of 24% and 23%, respectively ([125]Fig S1F and G). Although only the
reduction in tumour growth in response to inhibitor treatment was
statistically significant, the effect of GCN2 knockdown was numerically
comparable. Taken together, the results provide strong support for the
notion that GCN2 is required for unimpeded tumour growth in vivo.
Moreover, GSEA of RNA-seq data from tumour sample mRNA showed a
transcriptome response to prolonged in vivo GCN2 inhibition or
depletion that was highly concordant with our findings in cultured
cells after short-term pharmacological inhibition, including the
induction of “MYC targets” and the repression of “glycolysis” ([126]Fig
1E and F).
Intrigued by the fact that we did not observe enrichment in ISR-related
pathways in response to GCN2 inhibition or depletion, we then directly
compared the effect of GCN2 inhibition on A375 cells that were growing
under conditions of optimal nutrient availability with the response in
cells that were depleted of glutamine. Although GCN2iB widely
suppressed the induction of ATF4 targets that was triggered by
glutamine depletion, it had no such effect on the expression of key ISR
transcripts in cells that were not deprived of glutamine ([127]Figs 2A
and [128]S2A). Moreover, GCN2iB blocked the increase in eIF2α
phosphorylation that was triggered by glutamine depletion but had no
effect on eIF2α phosphorylation under conditions of full glutamine
availability ([129]Fig 2B). Thus, the function of GCN2 in cells that do
not suffer from nutrient depletion is distinctly different from its
role as a central ISR regulator in response to amino acid scarcity.
Figure 2. GCN2 restrains translation independently of the ISR.
(A) mRNA expression levels as determined by RNA-seq of selected ATF4
targets in A375 cells cultured in complete medium (2 mM glutamine,
100%Q) or glutamine-depleted medium (250 nM glutamine, 12.5%Q) and
treated with GCN2iB (1 μM, 48 h). Data are shown as the mean ± SEM of
four independent experiments. Statistical significance was determined
by two-way ANOVA and Sidák’s test for multiple comparisons. (A, B)
Immunoblot analysis of the indicated proteins in A375 cells cultured
and treated as described in (A). (C) Immunoblot analysis of
puromycinylated proteins in A375 cells treated for 48 h with 1 μM
GCN2iB (left), and bar graphs showing the quantification of
puromycinylated proteins (right). Data are shown as the mean ± SEM of
four independent experiments. Statistical significance was determined
by a paired t test. (D) Left panel, immunoblot analysis of
puromycinylated proteins in A375 cells expressing the indicated
constitutive (n = 3) or doxycycline-inducible shRNAs (n = 4). Right
panel, bar charts showing the quantification of puromycinylated
proteins. Data are shown as the mean ± SEM. Statistical significance
was determined by two-way ANOVA and Tukey’s test for multiple
comparisons. (C, D) No puro, extract from A375 cells grown without
puromycin; CHX, treatment with 200 μg/ml cycloheximide for 3 h before
puromycin addition. (E) Immunoblot analysis of puromycinylated proteins
in A375 cells treated for 48 h with 1 μM ISRIB (left), and bar graphs
showing the quantification of puromycinylated proteins (right). Data
are shown as the mean ± SEM of three independent experiments. (F) mRNA
levels of selected ISR genes in A375 cells grown in complete and
glutamine-depleted medium and in the presence of 1 μM ISRIB for 48 h.
mRNA levels were determined by qRT–PCR; data are shown as the mean ±
SEM, n = 3. Statistical significance was determined by two-way ANOVA
and Sidák’s test for multiple comparisons. *P < 0.05, **P < 0.005, ***P
< 0.001, ****P < 0.0001.
Source data are available for this figure.
[130]Figure 2.
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Figure S2. Transcriptomic response to GCN2 inhibition depends on nutrient
context.
[134]Figure S2.
[135]Open in a new tab
(A) Heatmap representation of ATF4 targets transcript-level changes as
determined by RNA-seq. Data are shown as the log[2] fold change
relative to DMSO control, n = 4. A375 cells were cultured in complete
medium (2 mM glutamine, 100%Q) or in glutamine-depleted medium (250 nM
glutamine, 12.5%Q) and in the presence of 1 μM GCN2iB or vehicle
control (DMSO). Samples were collected after 48 h and processed for RNA
sequencing and immunoblotting. (B) Bar graph showing the fold change in
the viability of A375 cells in response to ISRIB and GCN2iB. Cell
viability was measured using CellTiter 96 AQueous Non-Radioactive Cell
Proliferation Assay after 48 h of drug treatment; data are shown as the
mean ± SEM, n = 3. (C) Expression levels of selected ribosomal genes in
A375 cells grown in complete medium in the presence of 1 μM ISRIB for
24 h and 48 h. mRNA levels were determined by qRT–PCR; data are shown
as the mean ± SEM, n = 3. (D) Immunoblot analysis of S6 kinase
phosphorylation in A375 cells grown in complete medium (2 mM glutamine,
100%Q) or medium with reduced glutamine concentration (250 nM
glutamine, 12.5%Q), and treated for 24 h and 48 h with 1 μM GCN2iB
(top), and bar graphs showing the quantification of phosphorylated S6K
relative to total S6K (bottom). Data are shown as the mean ± SEM, n =
3. (C, D) Statistical significance was determined by two-way ANOVA and
Sidák’s test for multiple comparisons. *P < 0.05, **P < 0.005.
Our observations so far indicate that GCN2 inhibition or depletion
leads to an up-regulation of the protein biosynthesis machinery both at
the mRNA and at the protein level. To determine whether GCN2 is indeed
required to keep protein synthesis in check, we performed a puromycin
incorporation assay, which showed a 1.8-fold increase in protein
synthesis in GCN2iB-treated cells ([136]Fig 2C). We then quantified
protein synthesis in A375 cells expressing shRNAs against GCN2 under a
constitutive or a doxycycline-inducible promoter. The results show that
a 45–70% reduction in GCN2 protein levels led to a 1.6–2.1-fold
increase in global protein synthesis ([137]Fig 2D).
To further verify that the cell death and the observed increase in
protein synthesis after GCN2 inhibition are not linked to ISR
inhibition, we cultured A375 cells in the presence of ISRIB, a
well-characterised ISR inhibitor ([138]Sidrauski et al, 2013). ISRIB
had no discernible impact on cell viability at concentrations up to 5
μM ([139]Fig S2B). Similarly, treatment with 1 μM ISRIB in complete
medium did not result in increased translation ([140]Fig 2E).
Furthermore, ISRIB did not alter the expression of key ISR components
in cells grown in complete medium although it effectively suppressed
their up-regulation under amino acid deprivation ([141]Fig 2F). Lastly,
inhibition of the ISR had no significant effect on ribosome biogenesis
at the mRNA level ([142]Fig S2C).
Given the increased protein synthesis resulting from GCN2 inactivation,
we then assessed the activity of mTORC1, a key regulator of protein
synthesis that can be inhibited by GCN2 under nutrient-poor conditions
([143]Ye et al, 2015). As indicated by lower levels of phosphorylation
of S6K, we observed the expected mTORC1 repression after glutamine
depletion, which was blocked by GCN2 inhibition. Under conditions of
optimal nutrient availability, we observed no difference in S6K
phosphorylation as a surrogate marker for mTORC1 activity after 24 or
48 h of GCN2 inhibition ([144]Fig S2D), despite the increase in protein
synthesis at that time point. Thus, GCN2 curtails global protein
synthesis in some cancer cells growing under conditions of unrestricted
nutrient availability without changes in ISR signalling or mTORC1
activity.
We then conducted transcriptome analysis on five additional cancer cell
lines with different degrees of GCN2 dependency (see [145]Fig 1A), as
well as HUVECs to represent primary healthy cells, in which GCN2 was
inhibited. We observed that GCN2-independent cell lines clustered
differently from GCN2-dependent lines and primary cells ([146]Fig S3A)
and that the most dependent cell lines (A375, MiaPaCa2, and A375MA2)
clustered together, whereas the least dependent cell line (A549)
clustered close to independent cell lines (HepG2 and IPC298) and
primary cells (HUVECs). Moreover, clustering based on groups of
translation-related genes revealed up-regulation of transcripts coding
for ribosomal proteins, MYC targets, and translation initiation factors
in GCN2-dependent but not in GCN2-independent cells or primary cells
([147]Fig S3B–E). Furthermore, analysis of ribosomal genes in mRNA
levels in HUVECs and HDFs after GCN2iB treatment revealed that GCN2
inhibition does not increase ribosome biogenesis at the mRNA level in
primary cells ([148]Fig S4A and B).
Figure S3. Cluster analysis identifies differences in response to GCN2
inhibition in GCN2-dependent and GCN2-independent cell lines.
[149]Figure S3.
[150]Open in a new tab
Heatmap visualisations of hierarchical clustering analysis of RNA-seq
data obtained from six cancer cell lines (IPC298, HepG2, A549, A375MA2,
MiaPaCa2, and A375) of different tissue origins and with different
degrees of GCN2 dependency, and human umbilical vein endothelial
primary cells. (A, B, C, D, E) Clustering performed based on
transcript-level changes upon 48 h of GCN2iB treatment on all
significantly deregulated genes (A), ribosomal proteins (B), the
Hallmark categories MYC_targets_V1 (C) and MYC_targets_V2 (D), and
translation initiation factors (E).
Figure S4. Expression of ribosomal genes in primary cells upon GCN2
inhibition.
[151]Figure S4.
[152]Open in a new tab
(A) Expression levels of selected ribosomal genes in human umbilical
vein endothelial cells after GCN2 inhibition, as determined by RNA-seq.
(B) Expression levels of selected ribosomal genes in human dermal
fibroblasts (HDFs) after GCN2 inhibition, as determined by qRT–PCR. (A,
B) 1 μM GCN2iB. Data are shown as the mean ± SEM, n = 3. Statistical
significance was determined by two-way ANOVA and Sidák’s test for
multiple comparisons. **P < 0.005.
We also carried out puromycinylation assays in another highly dependent
cell line (A2058), one dependent cell line (MiaPaCa2), two
GCN2-independent cell lines (IPC298 and HepG2), and two types of
primary cells (HUVECs and HDFs) and observed evidence for increased
protein synthesis in A2058 and MiaPaCa2 cells under conditions of GCN2
inhibition, whereas no changes were observed in IPC298, HepG2 cells, or
the primary cells ([153]Fig S5A). Moreover, as we had observed in A375
cells, we did not observe changes in the expression of ATF4 targets
indicative of ISR activation in any of the cells after GCN2 inhibition
under conditions of full nutrient availability ([154]Fig S5B). Finally,
we carried out a proteomic analysis of two GCN2-independent cancer cell
lines and HUVECs after GCN2 inhibition and observed that GCN2
inhibition had essentially no effect on the proteome of these cells
([155]Fig S6A–D).
Figure S5. Differential regulation of protein synthesis upon GCN2 inhibition
in GCN2-dependent and GCN2-independent cell lines.
[156]Figure S5.
[157]Open in a new tab
(A) Immunoblot analysis of puromycinylated proteins in the indicated
cell lines and primary cells (left), and bar graphs showing the
quantification of puromycinylated proteins (right). Data are shown as
the mean ± SEM of three independent experiments. Statistical
significance was determined by a paired t test. (B) Heatmap
representation of ATF4 targets transcript-level changes as determined
by RNA-seq of the indicated 6 cell lines and primary cells. Data are
shown as the log[2] fold change, cell lines, n = 4; HUVECs, n = 3. In
(A, B), cells were treated with 1 μM GCN2iB for 48 h. *P < 0.05.
Figure S6. GCN2iB does not significantly alter the proteomes of
GCN2-independent cancer cells or primary cells.
[158]Figure S6.
[159]Open in a new tab
(A, B, C, D) Volcano plots representing the change in protein levels in
A375 (A), HepG2 (B), IPC298 (C), and HUVECs (D) treated for 48 h with
GCN2iB (1 μM) as determined by TMT labelling proteomics. Red dots
indicate proteins significantly (adjusted P < 0.05) up-regulated, and
blue dots indicate proteins significantly down-regulated. The green
line indicates the threshold for statistical significance.
GCN2 inactivation has differential effects on the transcriptional and
proteomic regulation of fundamental cellular processes
Next, we analysed the extent to which different cellular processes are
regulated by GCN2 at the transcriptome or proteome level. We found a
positive and significant but overall moderate correlation between the
changes in mRNA and protein abundance in GCN2iB-treated cells ([160]Fig
3A). Although 71% of differentially expressed genes and proteins
(DEGPs) changed concordantly (both mRNA and protein levels increased or
decreased) in response to GCN2iB, 29% showed a discordant response
([161]Fig 3B). To better understand this differential regulation, we
divided DEGPs into five groups depending on whether mRNA and protein
levels were unchanged, increased, or decreased, and categorised
proteins according to their cellular localisation ([162]Uhlén et al,
2015) ([163]Fig 3B). This analysis revealed important differences
between the 5 DEGP groups. Arguably, the biggest difference we observed
was that the proportion of secreted proteins was twice as high in the
concordantly repressed compared with the concordantly induced category
([164]Fig 3B), a finding that is consistent with the repression of
extracellular matrix–related pathways described above ([165]Fig 1C). We
then carried out a KEGG pathway enrichment analysis of the 5 DEGP
categories ([166]Fig 3B). Although metabolic and extracellular
matrix–related pathways dominated the concordantly repressed category
([167]Fig 3B, bottom left panel), RNA processing and transport, and
ribosomal pathways topped the concordantly induced category ([168]Fig
3B, upper right panel). “Ribosome” was also the top enriched pathway in
the category of DEGPs marked by decreased mRNA but increased protein
levels ([169]Fig 3B, upper left panel). The DEGP category of reduced
protein and increased mRNA levels ([170]Fig 3B, bottom right panel) was
dominated by “proteasome,” which governs the coordinated breakdown of
most cellular proteins. Plotting DEGPs belonging to significantly
enriched GO terms on the mRNA/protein correlation diagram further
confirmed and illustrated the differential transcriptomic and proteomic
effects of GCN2 inhibition on ribosome biogenesis, proteasome subunits,
and the extracellular matrix ([171]Fig S7A–C). These results suggest a
central role of GCN2 in the differential regulation of the
transcriptome and proteome of cancer cells. Specifically, inhibition of
GCN2 up-regulates processes linked to protein synthesis, especially
ribosome biogenesis, whereas key metabolic pathways and extracellular
matrix–related processes are predominantly repressed. Considering that
most drug targets are proteins, we then used our proteomic data to
investigate the effects of GCN2 inhibition on the druggable proteome of
A375 cells. The results show that GCN2 inhibition significantly
deregulated 484 out of 1,053 druggable proteins listed in the Human
Protein Atlas
([172]https://www.proteinatlas.org/humanproteome/tissue/druggable
accessed on 28th July 2023). Of these, 306 were down-regulated and 178
were up-regulated. The latter include 66 proteins for which active
ligands are known and 15 for which one or more approved drugs exist
([173]Fig 3C). Thus, GCN2 inhibition deregulates the cellular proteome,
thereby potentially altering its druggability.
Figure 3. Differential regulation of the transcriptome and proteome by GCN2.
[174]Figure 3.
[175]Open in a new tab
(A) Correlation analysis of mRNA and protein expression changes in A375
cells treated with 1 μM GCN2iB for 48 h. Each dot represents a
transcript and corresponding protein. Colours indicate cellular
localisation of proteins, and grey dots represent transcripts and
proteins with non-significant (ns) changes in mRNA and protein
abundance. (B) Doughnut charts depict the proportion of intracellular,
secreted, and transmembrane proteins. Bubble plots show the top 10
enriched KEGG pathways with bubble sizes indicating the number of genes
and bubble colours representing statistical significance levels. (C)
Heatmap representation of GCN2iB-induced protein expression changes of
66 up-regulated proteins (top) for which at least one active
preclinical ligand (middle) or approved drug (bottom) is registered in
ChEMBL.
Figure S7. Differential regulation of functional gene/protein groups.
[176]Figure S7.
[177]Open in a new tab
(A, B, C) Correlation plots showing the log[2]FC for each mRNA (x-axis)
and protein (y-axis) in A375 cells treated with 1 μM GCN2iB for 48 h;
genes belonging to Gene Ontology (GO) categories of interest are
coloured in purple: (A) GO:0022613 Ribonucleoprotein complex biogenesis
(left) and GO:0042254 Ribosome biogenesis (right); (B) GO:0000502
Proteasome complex (left) and GO:0005839 Proteasome core complex
(right); and (C) GO:0030198 Extracellular matrix organization (left)
and GO:0043062 Extracellular structure organization (right).
GCN2 restrains ribosome biogenesis
To understand the mechanisms underlying the differential regulation of
the proteome by GCN2, we performed ribosome profiling (Ribo-seq), a
deep sequencing approach based on the proposition that the density of
ribosome-protected fragments (RPFs) of a particular transcript provides
an indication of the rate of protein synthesis ([178]Ingolia et al,
2009). First, using standard quality control analyses, we confirmed
that RPFs showed the anticipated size distribution and predominantly
mapped to annotated coding regions ([179]Fig S8A and B). Given that
GCN2 has been shown to play a key role in the resolution of ribosome
collisions ([180]Ishimura et al, 2016; [181]Wu et al, 2020), we then
investigated whether GCN2 inhibition caused an increase in ribosome
stalling. However, our data showed no significant changes in codon
occupancy ([182]Fig 4A) or in the pause score ([183]Kumari et al, 2018)
([184]Fig S8C), indicating that GCN2 is not required for the prevention
or resolution of ribosome collisions in A375 cells that are not
depleted of nutrients. Next, we investigated whether inhibition of GCN2
alters translation efficiency (TE), which is a measure of the number of
ribosomes that occupy a specific transcript and is defined as the ratio
of RPFs to mRNA abundance. We identified 604 genes with significantly
different TE (differential TE genes, DTEGs) in GCN2iB-treated cells. Of
these, 55.4% exhibited increased TE, indicating a significant increase
in ribosome occupancy and thus translation, whereas 44.6% showed
reduced TE ([185]Fig 4B). Moreover, the median shift in TE for genes
exhibiting increased TE was significantly greater than that observed
for genes with decreased TE ([186]Fig S8D), in line with our
observation that GCN2 inactivation increases global protein synthesis
([187]Fig 2C and D). Furthermore, GO enrichment analysis of DTEGs
revealed that translation- and ribosome-related terms dominated the
most enriched pathways ([188]Fig S8E).
Figure S8. Ribo-seq quality control and ribosomal protein translation.
[189]Figure S8.
[190]Open in a new tab
(A) Plots showing the expected read length distribution of the
ribosomal footprints for the 4 Ribo-seq experiments performed in this
study. (B) Chart showing the percentage of reads aligning to the 5′UTR,
the ORF, the 3′UTR, or non-coding regions (other). (C) Comparison plot
of the mean pause score of 10,095 individual sites for A375 cells
treated with GCN2iB (y-axis) versus vehicle control (x-axis). (D)
Violin plot showing the log[2]FC in translation efficiency (TE) in A375
cells treated with 1 μM GCN2iB for 48 h; the black line indicates the
median. (E) Bubble plots depicting the top 10 enriched Biological
Process (BP) Gene Ontology (GO) terms of genes with increased (top) and
reduced (bottom) TE in GCN2iB-treated A375 cells (1 μM, 48 h). (F)
Comparison profiles showing ribosome occupancy along three
representative ribosomal mRNAs in A375 cells treated with GCN2iB (red)
or DMSO (black). ****P < 0.0001.
Figure 4. GCN2 controls translation of ribosomes and translation factors.
[191]Figure 4.
[192]Open in a new tab
(A) Codon occupancy at ribosomal A and P sites as determined by
Ribo-seq in A375 cells treated with 1 μM GCN2iB for 48 h (n = 2). Dots
show normalised RPF counts for the indicated amino acids and stop
codons. (B) Volcano plot showing genes with differential translation
efficiency (TE) in A375 cells treated with GCN2iB (1 μM, 48 h). Labels
indicate genes with the lowest adjusted P-value in the up-regulated and
down-regulated groups. Labels in the bold font indicate genes directly
involved in protein translation. (C) Scatter plot of changes between
GCN2iB-treated cells compared with DMSO-treated controls in Ribo-seq
data (y-axis) and paired RNA-seq data (x-axis). Each dot represents a
gene and is coloured according to its regulatory grouping as indicated.
(C, D) Bubble plots show the most enriched Biological Process (BP) Gene
Ontology (GO) terms in the groups depicted in (C). (E) Bar chart
showing changes in TE of ribosomal subunit transcripts in
GCN2iB-treated cells.
We then further subcategorised DTEGs in line with a previously
published classification ([193]Chothani et al, 2019), according to
their transcriptional or translational regulation. The “exclusive”
group consists of transcripts whose change in TE is driven by increased
or decreased ribosome occupancy with no change in mRNA levels (n = 249
genes), whereas the “intensified” group (n = 91) is composed of
transcripts whose change in abundance is accompanied by a concordant
increase or decrease in ribosome occupancy. Transcripts with “buffered”
regulation (n = 264) show changes in ribosome occupancy that offset the
change in mRNA abundance. “Forwarded” transcripts are characterised by
mRNA changes that are not accompanied by a change in TE (n = 2,307)
([194]Fig 4C). Subsequent GO analysis of the genes included in these
categories showed that processes linked to the regulation of
translation and ribosome biogenesis dominated the exclusive and
intensified categories and showed some enrichment in the buffered and
forwarded categories ([195]Fig 4D). Remarkably, 91.2% (93 out of 102)
of ribosomal protein mRNAs exhibited higher ribosome occupancy in
GCN2iB-treated cells ([196]Figs 4E and [197]S8F), indicating that they
were translated more actively. Thus, GCN2 regulates the expression of
ribosomal proteins on the transcriptional and even more so on the
translational level.
Given that protein synthesis relies on proteasome function to degrade
proteins and recycle amino acids ([198]Vabulas & Hartl, 2005;
[199]Suraweera et al, 2012), we analysed Ribo-seq data of proteasome
subunits and detected no change in the TE of genes encoding proteasomal
subunits ([200]Fig S9A). This finding adds to our transcriptome and
proteome analyses, which showed an increase in proteasome subunit mRNAs
but the lower level of proteasomal proteins ([201]Figs 3B and [202]S7).
Thus, the increase in global protein synthesis that is unleashed by
GCN2 inhibition and linked to increased ribosome biogenesis is not
accompanied by an increase in proteasome biogenesis. Given that we had
observed repression of ECM-related pathways on both the transcript and
the protein level ([203]Fig 3B), we also inspected our Ribo-seq data
for changes in ECM mRNA translation and found reduced ribosome
occupancy for most of the fibrous ECM proteins, proteoglycans, and
integrins ([204]Fig S9B). Thus, GCN2 keeps global mRNA translation in
check but is required to maintain ECM protein synthesis.
Figure S9. Regulation of translation by GCN2.
[205]Figure S9.
[206]Open in a new tab
(A) Heatmap representation of the changes in mRNA, protein abundance,
and TE for 20S and 19S proteasome subunit genes in GCN2iB-treated A375
cells (1 μM, 48 h). (B) Heatmap representation of the changes in mRNA
expression, ribosome density, and protein abundance of extracellular
matrix proteins and integrins in GCN2iB-treated A375 cells (1 μM, 48
h). In (A, B), data are shown as the mean of n = 4 for mRNA, n = 3 for
protein, and n = 2 for ribosome density. (C) Network analysis of
selected enriched Reactome pathways in GCN2iB-treated cells. The node
size indicates the number of genes in the pathway, and thickness of the
edges, the degree of overlap between pathways.
To visualise the relation between processes that are regulated by GCN2
on a translational level, we carried out a network analysis of enriched
Reactome pathways. The results of this analysis highlight that pathways
directly involved in proteome control, such as rRNA processing,
translation, post-translational protein modifications, protein
localisation, and protein degradation, are closely interconnected with
other key cellular functions, notably metabolic and cell cycle–related
processes ([207]Fig S9C).
GCN2 inhibition triggers a dynamic loss of metabolic homeostasis
Given the substantial energy and resource demands of protein synthesis,
we then set out to determine the metabolic sequelae of GCN2 inhibition.
We first quantified intracellular ATP levels in GCN2iB-treated live
cells and found them to be significantly reduced by 11% ([208]Fig 5A).
We then carried out time-resolved untargeted metabolome. Our data
revealed a widespread disturbance of cell metabolism with 113 (13.1%),
209 (24.2%), and 298 (34.5%) of 865 detected metabolites significantly
deregulated after 12, 24, and 36 h of GCN2 inhibition, respectively
([209]Figs 5B and [210]S10A). Amino acids and lipids were the most
deregulated metabolic families at each time point. Specifically, we
found 16 out of 20 proteogenic amino acids to be significantly reduced
([211]Fig 5C), an observation compatible with greater demand during
heightened translation. An in-depth analysis of the lipid superpathway
showed evidence of augmented fatty acid catabolism together with a
substantial increase in sphingosines, phospholipids, and ceramides
([212]Fig 5D), lipids that are integral components of cell membranes.
In conjunction with the transcriptional and translational up-regulation
of components of the ER translocon and signal peptidase complexes
([213]Fig S10B), these findings may be indicative of a cellular attempt
to expand the ER to accommodate enhanced protein folding.
Figure 5. Loss of metabolic homeostasis upon GCN2 inhibition.
[214]Figure 5.
[215]Open in a new tab
(A) Bar chart showing the quantification of intracellular ATP levels as
determined by a luciferase-based assay and normalised to the number of
live cells. Data are shown as the mean ± SEM of four independent
experiments. Statistical significance was determined by two-way ANOVA
and Sidák’s test for multiple comparisons, ***P < 0.001. (B) Number and
classification of deregulated metabolites, quantified by
ultra-high-performance liquid chromatography–tandem mass spectrometry
(UHPLC-MS/MS). (C) Heatmap showing the relative concentration of
proteogenic amino acids. (D) Distribution of deregulated lipid
families, with a pie chart size representing the absolute number of
deregulated lipids. (E) Heatmap showing the relative concentration of
glycolysis intermediates. (C, E) Data are shown as the mean intensity
normalised to DMSO-treated cells of four independent experiments. All
panels show results in A375 cells cultured in complete medium and
treated with 1 μM GCN2iB for the indicated times.
Figure S10. Effect of GCN2 inhibition on the metabolome of A375 cells.
[216]Figure S10.
[217]Open in a new tab
(A) Volcano plots showing the deregulated metabolites in A375 cells
after GCN2 inhibition. Significantly (adjusted P < 0.05) deregulated
metabolites are shown in red. (B) Heatmap representation of the changes
in mRNA expression, ribosome density, and protein abundance of subunits
of the translocon and signal peptidase complexes in the ER. (C) Heatmap
representations of the changes in mRNA expression, ribosome density,
and protein abundance of enzymes in the glycolysis pathway. (B, C) Data
are shown as the mean of n = 4 for RNA-seq, n = 3 for proteomics, and n
= 2 for Ribo-seq. (D) Heatmap showing the relative concentration of
tricarboxylic acid cycle intermediates in GCN2iB-treated A375 cells.
Data are shown as the mean intensity normalised to DMSO, n = 4. All
panels show results in A375 cells treated with 1 μM GCN2iB for the
indicated times.
Finally, we analysed processes involved in energy generation. Despite
finding no changes in glucose levels, we observed a reduction in the
concentration of downstream metabolites in the glycolytic pathway that
was most pronounced at the 36-h time point ([218]Fig 5E), in line with
our transcriptome, proteome, and translatome data ([219]Figs 1C and D
and [220]S10C). On the contrary, TCA cycle components remained largely
unchanged ([221]Fig S10D).
Taken together, the data demonstrate that GCN2 inhibition causes a
dynamic and broad loss of metabolic homeostasis that culminates in ATP
deficiency.
Discussion
The cellular functions of GCN2 in healthy and cancerous cells have
largely been elucidated under stress conditions such as amino acid
deficiency, where GCN2 triggers the ISR, an adaptive signalling network
that responds to proteostasis perturbations. Here, using cancer cell
lines with different degrees of GCN2 dependency as experimental models,
we show that GCN2 plays a key role in regulating the cellular proteome
in a manner that is independent of the ISR ([222]Figs 2A, B, and E and
[223]S2A and B), results that are supported by our observations that
the ISR inhibitor ISRIB had no relevant effect on the viability or
transcriptome of GCN2-dependent cells ([224]Figs 2F and [225]S2B and
C). These observations, together with our findings on cells in which
GCN2 was genetically depleted, also rule out the possibility that the
GCN2 inhibitor we used in our study activated the ISR ([226]Carlson et
al, 2023; [227]Szaruga et al, 2023). Thus, the role of GCN2 as a
regulator of translation in non-stressed cells, which we describe here,
differs significantly from its role as a translation regulator in the
ISR. Although stress-induced GCN2 activation in the canonical ISR
attenuates steady-state translation via eIF2α phosphorylation, our
analyses show that at least in certain cells, GCN2 prevents excessive
translation in the absence of nutrient depletion or other known stress
conditions. Translatome analyses show that this is accompanied and
likely enabled by the up-regulation of ribosome biogenesis. However, it
remains unknown exactly how the depletion or inhibition of GCN2
triggers this response. In the absence of changes in ISR signalling, it
can be assumed that what we observe does not simply constitute the
silencing of an ISR that has been activated at basal growth conditions
by an unknown stress. It is worth noting that a role of GCN2 in
nutrient-rich conditions has recently been described for colorectal
cancers. However, in contrast to our work, GCN2 inhibition was observed
to trigger a reduction in protein synthesis and mTORC1 activity
([228]Piecyk et al, 2024). Thus, the role of GCN2 in regulating
translation and the cellular proteome appears to be highly cell- and
context-dependent. Moreover, transcriptomic and proteomic analyses of
primary cells treated with GCN2iB suggest that this role of GCN2 is not
functionally relevant in non-transformed cells ([229]Figs S3, [230]S4,
[231]S5, and [232]S6). It remains to be determined why the
ISR-independent role of GCN2 in preventing excessive translation is
restricted to certain cancer cells.
In cancers, increased protein synthesis as part of an enhanced anabolic
programme to sustain tumour growth is tied to an increase in the
biogenesis of ribosomal proteins, ribosomal RNA, and translation
factors to enhance translation capacity ([233]Iritani & Eisenman, 1999;
[234]Inoki et al, 2002; [235]Ma et al, 2005; [236]Truitt & Ruggero,
2016) and is typically under the control of hyperactive oncogenes such
as MYC. Indeed, anabolic programmes driven by MYC have been shown to
trigger an adaptive stress response controlled by GCN2 and ATF4
([237]Tameire et al, 2019; [238]Croucher et al, 2021). These
observations are compatible with the notion that a MYC-driven increase
in protein synthesis requires a higher level of coordination between
amino acid use and availability, thereby making cells more reliant on
the ISR and GCN2. However, this does not provide an explanation for the
induction of MYC targets by GCN2 inactivation, unless GCN2 has a
hitherto unknown inhibitory effect on MYC in some cancer cells. To our
knowledge, no such link is known. Moreover, the observation that GCN2
inactivation is detrimental to certain cancer cells while at the same
time leading to the induction of MYC targets and protein anabolism,
typically linked to tumour growth, is intriguing. However, MYC can
trigger apoptosis in cells where growth signals are insufficient
([239]Askew et al, 1991; [240]Evan et al, 1992, [241]1994).
For some time, the only known target of GCN2 kinase activity was eIF2α.
However, recent research has provided increasing evidence for the
existence of additional targets ([242]Dokládal et al, 2021; [243]Ge et
al, 2023; [244]Stonyte et al, 2023). It is therefore possible that the
ISR-independent roles of GCN2 that we observe are mediated by one of
the known or other yet to be defined GCN2 targets. In addition, despite
being principally a cytosolic kinase, GCN2 can localise to the
nucleolus ([245]Nakamura & Kimura, 2017) suggesting not only a possible
explanation for a direct role in ribosome biogenesis, but also the
possibility of hitherto unknown interactions with nuclear proteins. It
is therefore tempting to speculate that at least in some cancers, GCN2
has a direct or indirect inhibitory effect on MYC, potentially through
regulation of one of MYC’s multiple partners ([246]Lourenco et al,
2021). Such an interaction could have important implications for GCN2
in the physiological regulation of MYC ([247]Eilers & Eisenman, 2008)
and for anti-cancer therapies, particularly those targeting strongly
MYC-driven tumours.
One of the questions arising from our observations is how excessive
protein synthesis triggered by GCN2 inactivation could be detrimental
to at least some cancer cells. Mechanistically, our observations
provide several potential explanations for how this occurs. First and
foremost, it is important to note that mRNA translation is a highly
resource-intensive and energy-consuming process that uses between a
quarter and almost half of cellular ATP supplies ([248]Buttgereit &
Brand, 1995; [249]Princiotta et al, 2003). Thus, the approximate
doubling of global protein synthesis we observed upon GCN2 inhibition
or knockdown can be expected to substantially enhance metabolic
demands. Indeed, the dynamic and broad loss of metabolic homeostasis we
observed is compatible with the notion that the excessive protein
production that was triggered by GCN2 inactivation exceeded the
adaptive capacity of cells. Our findings are also compatible with a
recently proposed counterintuitive approach to cancer therapy that is
based on the deliberate overactivation of oncogenic signals to
overwhelm the stress response capacity of cancer cells ([250]Dias et
al, 2024).
One other likely important issue is the failure of cells to enhance
proteasome capacity in parallel with the increase in ribosome
biogenesis and protein synthesis. This is an important problem as the
use of amino acids to produce new proteins must be balanced with
increased provision through protein degradation, a key role of the
proteasome. Indeed, protein synthesis upon nutrient restriction relies
on the proteasome ([251]Vabulas & Hartl, 2005), and proteasome
inhibition rapidly causes intracellular amino acid scarcity even when
extracellular amino acid supplies are plentiful ([252]Suraweera et al,
2012; [253]Parzych et al, 2019; [254]Saavedra-García et al, 2021). It
is therefore reasonable to assume that the combination of excessive
translation and insufficient proteasomal protein degradation accounts
for the decline in intracellular amino acid levels that we observed.
Because amino acids not only serve as macromolecular building blocks
for proteins but are also converted into energy ([255]DeBerardinis et
al, 2008), it is likely that this decrease in intracellular amino acids
also contributed to the decrease in ATP levels we observed. The
reduction in glycolysis we observed may also hinder the maintenance of
energy homeostasis in cells.
Approximately one third of cellular proteins are membrane-bound or
destined for secretion. These proteins must be post-translationally
modified and folded in the ER ([256]Dobson, 2004) and include ECM
proteins, such as collagens, which are particularly resource-intensive
to produce. It was therefore intriguing to observe that ECM proteins
featured prominently among those suppressed by GCN2 inactivation on a
transcript, protein, and translational level. Given that tumours that
produce ATP at a slow rate suppress protein synthesis to cut down on
energetically expensive functions ([257]Bartman et al, 2023), it is
reasonable to hypothesise that the production of proteins that are
particularly resource-intensive was reduced because of lacking
resources. It is worth noting that we observed an induction of
ER-related pathways in cells in which GCN2 was inhibited, particularly
in some translocation channels ([258]Figs 1C and [259]S10B). Together
with our metabolomic data that show a substantial increase in lipids
that are typically found in membranes, the results suggest a cellular
response directed at expanding the secretory apparatus. However, we did
not find convincing evidence of ER stress, a condition characterised by
the excessive accumulation of misfolded proteins in the ER ([260]Walter
& Ron, 2011), suggesting that the secretory pathway was not overwhelmed
by the enhanced protein synthesis that was unleashed by GCN2
inactivation.
Our observations reveal a hitherto unrecognised function of GCN2 in
curtailing protein synthesis and ribosome biogenesis that is
independent from its canonical role in the ISR. They add to the growing
body of evidence that the proteome and its regulation at the
translational level play central but understudied roles in cancer
biology and lend support to the emerging concept of therapeutic
overactivation of oncogenic signalling as a potential cancer treatment
strategy.
Materials and Methods
Cell culture
The source and culture conditions for the cell lines used in this work
are detailed in Table S1.
Table S1. [261]Source and culture conditions for cells used in this
work.^ (11.7KB, xlsx)
CD34^+ haematopoietic stem cells and mesenchymal stromal cells, as well
as the research ethics approval for their use, have been previously
described ([262]Loaiza et al, 2018). Briefly, CD34^+ cells and
mesenchymal stromal cells were sourced from the Imperial College
Healthcare Tissue and Biobank (ICHTB, Human Tissue Authority licence
12275). ICHTB is approved by the UK National Research Ethics Service to
release human material for research (12/WA/0196).
GCN2 knockdown in A375 cells
Plasmids and sequences used for constitutive shRNA-mediated GCN2
knockdown have been described previously ([263]Parzych et al, 2019).
Oligonucleotides with the same sequences were cloned into an
EZ-Tet-pLKO vector ([264]Frank et al, 2017) (85966; Addgene).
Lentiviral particles were produced by transfecting HEK293 cells with a
shRNA-carrying plasmid and the envelope and packaging vectors (pMD2.G,
pRSV.REV, pMDLg/pRRE, a kind gift from Prof. Anastasios Karadimitris,
Imperial College London) using Lipofectamine 2000 (11668030;
Invitrogen), and virus-containing supernatants were collected 48 and 72
h after transfection. A375 cells were transduced by spinoculation with
500 μl of the lentivirus-containing supernatant. For induction of shRNA
expression with doxycycline, 500 ng/ml of doxycycline (D9891;
Sigma-Aldrich) was added to the cell culture medium.
GCN2 inhibition
Cells were incubated for the indicated times with 1 μM of GCN2
inhibitor GCN2iB (HY-112654; MedChemExpress) or vehicle control (DMSO,
D2650; Sigma-Aldrich), and medium was changed every 24 h.
Cell viability assay
Cell viability was determined using CellTiter 96 AQueous
Non-Radioactive Cell Proliferation Assay (MTS) (G5430; Promega),
following the manufacturer’s instructions. Absorbance was measured in a
FLUOstar Omega (BMG Labtech) or a Tecan Spark multiplate reader.
ATP measurement
ATP quantification was carried out with ATP Bioluminescence Assay Kit
CLS II (11699695001; Roche) following the manufacturer’s instructions.
Luminescence was measured using a Tecan Infinite M200 microplate
reader.
Puromycinylation assay
For semi-quantitative analysis of protein synthesis, puromycin (P8833;
Sigma-Aldrich) was added to cell cultures at a final concentration of 5
μg/ml for 10 min, after which cells were collected for protein
extraction and Western blotting as described below.
Protein extraction and Western blot
Cell pellets were resuspended in ice-cold RIPA buffer (R0287;
Sigma-Aldrich) supplemented with phosphatase and protease inhibitors
(PhosSTOP, 4906845001, and cOmplete EDTA-free, COEDTAF-RO; Roche) and
incubated on ice for 20 min. Lysates were then cleared by
centrifugation at 14,000g for 10 min at 4°C. The protein concentration
was measured with the Pierce BCA protein assay kit (23225; Thermo
Fisher Scientific) according to the manufacturer’s instructions.
Extracts were diluted with 6x SDS loading buffer, boiled for 4 min, and
subjected to SDS–PAGE, and gels were transferred to PVDF membranes
using the Bio-Rad Mini-PROTEAN Tetra electrophoresis and wet-blotting
system. Membranes were blocked with 5% BSA in 0.05% NP-40/Tris-buffered
saline (TBS/NP-40) for 1 h at RT and incubated with the primary
antibody diluted in 0.5% BSA/TBS/NP-40 overnight at 4°C. The next day,
membranes were washed three times for 15 min at RT with TBS/NP-40,
incubated with the appropriate secondary antibody diluted in 0.5%
BSA/TBS/NP-40, washed three more times for 15 min at RT, and developed
using Immobilon Crescendo Western HRP substrate (WBLUR0500; Millipore).
Images were obtained with a Hyperfilm-ECL film (GE28906839; Cytiva) or
iBright CL750 (Invitrogen). The antibodies and working dilutions used
in this study are as follows: anti-P-eIF2α (1:1,000, 9721; Cell
Signaling), anti-eIF2α (1:2,000, 9722; Cell Signaling), anti-puromycin
(1:5,000, MABE343; Merck Millipore), anti-GCN2 (1:2,000, 3302; Cell
Signaling), anti-lamin B1 (1:2,000, 12586; Cell Signaling),
anti-β-tubulin (1:3,000, 2146; Cell Signaling), anti-P-S6 kinase
(1:2,000, 9S34; Cell Signaling), anti-S6 kinase (1:2,000, 2708; Cell
Signaling), goat anti-rabbit-HRP (1:10,000, A16096; Thermo Fisher
Scientific), and rabbit anti-mouse-HRP (1:10,000, A16160; Thermo Fisher
Scientific). Western blot quantification was performed with Fiji
software ([265]Schindelin et al, 2012).
Tumour xenografts
Genetic depletion of GCN2
All animal experiments were performed in accordance with the United
Kingdom Home Office Guidance on the Operation of the Animals
(Scientific Procedures) Act 1986 Amendment Regulations 2012 and within
the published National Cancer Research Institutes Guidelines for the
welfare and use of animals in cancer research ([266]Workman et al,
2010). Experiments were conducted under Project Licence Number
PP1780337. A total of 1.5 × 10^6 A375 cells in 100 μl PBS were injected
into the rear flank of female NOD SCID Gamma mice. When tumours reached
quantifiable size, mice were administered 0.5 mg/ml doxycycline (D9891;
Sigma-Aldrich) in 0.5% sucrose water for 15 d. Tumour measurements were
performed with callipers every 2 d, and mouse weight was monitored
throughout the experiment. Tumour volume (TV) was calculated as TV =
length (mm) × width (mm) × depth (mm) × ∏/6.
Pharmacological inhibition of GCN2
5 × 10^6 A375 cells in 200 μl PBS with Matrigel (1:1) were injected
into female BALB/c nude mouse flanks. When tumours reached 125–150
mm^3, animals were randomised into treatment and control groups. Mice
were then treated with 10 mg/kg GCN2iB (or 0.5% methylcellulose vehicle
control) by oral gavage twice a day, for 10 d. Mice were weighed, and
tumour dimensions were measured with callipers twice a week. TV was
calculated as TV = 0.5 × (long diameter) × (short diameter)^2, in mm.
RNA extraction
RNA extraction from cultured cells was performed with the ReliaPrep RNA
Miniprep Systems kit (Z6012; Promega) according to the manufacturer’s
instructions. RNA from xenograft tumours was extracted using TRIzol
(15596026; Invitrogen) and cleaned with the RNeasy mini kit (74104;
QIAGEN) following the manufacturer’s protocols.
Quantitative real-time polymerase chain reaction (qRT–PCR)
1 μg RNA was used for cDNA synthesis using GoScript Reverse
Transcription System (Promega) following the manufacturer’s guidelines.
2 μl of a 1:20 dilution of the synthesised cDNA was used for qRT–PCR
using GoTaq qPCR Master Mix (Promega) in a QuantStudio 6 qPCR system
(Thermo Fisher Scientific). mRNA levels were quantified using the ΔCt
method, using HPRT as a control for normalisation. The primers used in
this work are listed in Table S2.
Table S2. [267]List and sequence of primers used in this work.^ (9.5KB,
xlsx)
RNA sequencing
RNA samples were quantified using Qubit 4.0 Fluorometer (Life
Technologies), and RNA integrity was checked with the RNA kit on
Agilent 5300 Fragment Analyzer (Agilent Technologies). RNA-sequencing
libraries were prepared using NEBNext Ultra II RNA Library Prep Kit for
Illumina (NEB) following the manufacturer’s instructions. Briefly,
mRNAs were first enriched with oligo(dT) beads. Enriched mRNAs were
fragmented for 15 min at 94°C. First-strand and second-strand cDNAs
were subsequently synthesised. cDNA fragments were end-repaired and
adenylated at 3′ ends, and universal adapters were ligated to cDNA
fragments, followed by index addition and library enrichment by
limited-cycle PCR. Sequencing libraries were validated using NSG Kit on
Agilent 5300 Fragment Analyzer and quantified by using Qubit 4.0
Fluorometer. The sequencing libraries were multiplexed and loaded on
the flow cell on the Illumina NovaSeq 6000 instrument according to the
manufacturer’s instructions. The samples were sequenced using a 2 × 150
Pair-End (PE) configuration v1.5. Image analysis and base calling were
conducted by NovaSeq Control Software v1.7 on the NovaSeq instrument.
Raw sequence data (.bcl files) generated from Illumina NovaSeq were
converted into fastq files and demultiplexed using Illumina bcl2fastq
program version 2.20. One mismatch was allowed for index sequence
identification. RNA library preparation and sequencing were conducted
by Azenta.
Tandem mass tag (TMT) labelling proteomics
S-Trap processing of samples
80 × 10^6 cells per sample were collected, washed twice with ice-cold
PBS supplemented with phosphatase and protease inhibitors (PhosSTOP,
4906845001, and cOmplete EDTA-free, COEDTAF-RO; Roche), and snap-frozen
in liquid nitrogen. Cell pellets were lysed in 3 ml lysis buffer (100
mM TEAB, 5% SDS) and sonicated for 15 s three times to shear DNA, and
protein concentration was estimated using the micro-BCA assay. Aliquots
of 500 μg (two batches for each sample) of protein were processed using
S-Trap mini protocol (ProtiFi) as recommended by the manufacturer with
little modifications. Protein’s disulphide bonds were first reduced in
the presence of 20 mM DTT for 10 min at 95°C, then alkylated in 40 mM
IAA for 30 min in the dark. After sample application into an S-Trap
mini spin column, trapped proteins were washed 5 times (500 μl) with
S-Trap binding buffer. Double digestion with trypsin (Pierce; Thermo
Fisher Scientific) (1:40) was carried out first overnight at 37°C in
160 μl 50 mM TEAB, and then for another 6 h at the same temperature.
Elution of peptides from S-Trap mini columns was achieved by
centrifugation at 1,000g for 1 min after the addition of 160 μl 50 mM
TEAB, then 160 μl 0.2% aqueous formic acid, and finally 160 μl 50%
(vol/vol) acetonitrile containing 0.2% (vol/vol) formic acid. The
resulting tryptic peptides were pooled and dried a couple of times in
SpeedVac by resuspension/drying in 50 μl 100 mM TEAB or Milli-Q until
the pH was around 8.5 (checked by pH strips). Peptides were then
quantified using Pierce Quantitative Fluorometric Peptide Assay (Thermo
Fisher Scientific).
TMT labelling and high-pH reversed-phase fractionation
Tryptic peptides (200 μg, each sample) were dissolved in 200 μl of 150
mM TEAB. TMT labelling was performed according to the manufacturer’s
instructions (Thermo Fisher Scientific). The different TMT-16 plex
labels (1 mg) (Thermo Fisher Scientific) were dissolved in 40 μl of
anhydrous acetonitrile, and each label was added to a different sample.
The mixture was incubated for 1 h at RT, and the labelling reaction was
stopped by adding 8 μl of 5% hydroxylamine per sample. After labelling
with TMT, samples were checked for labelling efficiency, then mixed,
desalted, and dried in SpeedVac at 30°C. Samples were redissolved in
200 μl ammonium formate (10 mM, pH 9.5), and peptides were fractionated
using high-pH RP chromatography. A C18 column (XBridge Peptide BEH, 130
Å, 3.5 μm, 2.1 × 150 mm; Waters) with a guard column (XBridge, C18, 3.5
μm, 2.1 × 10 mm; Waters) was used on Ultimate 3000 HPLC (Thermo Fisher
Scientific). Buffers A and B used for fractionation consist,
respectively, of (A) 10 mM ammonium formate in Milli-Q water, pH 9.5,
and (B) 10 mM ammonium formate, pH 9.5, in 90% acetonitrile. Fractions
were collected using a WPS-3000FC autosampler (Thermo Fisher
Scientific) at 1-min intervals. The column and guard column were
equilibrated with 2% Buffer B for 20 min at a constant flow rate of 0.2
ml/min. Fractionation of TMT-labelled peptides was performed as
follows: 190 μl aliquots were injected onto the column, and the
separation gradient was started 1 min after the sample was loaded onto
the column. Peptides were eluted from the column with a gradient of 2%
Buffer B to 20% Buffer B in 6 min, then from 20% Buffer B to 45% Buffer
B in 51 min, and finally from 45% Buffer B to 100% Buffer B within 1
min. The column was washed for 15 min in 100% Buffer B. The fraction
collection started 1 min after injection and stopped after 80 min
(total 80 fractions, 200 μl each). Formic acid (30 μl of 10% stock) was
added to each fraction and concatenated in groups of 20 fractions for
total proteome.
LC-MS analysis
Mass spectrometry data were collected using an Orbitrap Eclipse mass
spectrometer (Thermo Fisher Scientific) coupled to Dionex UltiMate 3000
RS (Thermo Fisher Scientific). LC buffers used are the following:
Buffer A (0.1% formic acid in Milli-Q water [vol/vol]) and Buffer B
(80% acetonitrile and 0.1% formic acid in Milli-Q water [vol/vol]). For
total proteome analysis, an equivalent of 1 μg of each fraction was
loaded at 15 μl/min onto a trap column (100 μm × 2 cm, PepMap nanoViper
C18 column, 5 μm, 100 Å; Thermo Fisher Scientific) equilibrated in 0.1%
TFA. The trap column was washed for 6 min at the same flow rate with
0.1% TFA and then switched in-line with a Thermo Fisher Scientific
resolving C18 column (75 μm × 50 cm, PepMap RSLC C18 column, 2 μm, 100
Å), which was equilibrated at 3% Buffer B for 19 min at a flow rate of
300 nl/min. The peptides were eluted from the column at a constant flow
rate of 300 nl/min with a linear gradient from 10% buffer to 18% within
83 min, from 18% B to 27% Buffer B in 45 min, and then from 27% B to
90% Buffer B within 5 min. The column was then washed with 90% Buffer B
for 8 min. The column was kept at a constant temperature of 50°C.
The Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific) was
operated in a positive ionisation mode, equipped with an easy spray
source. The source voltage was set to 2.5 Kv, and the ion transfer tube
was set to 275°C.
The scan sequence began with an MS1 spectrum (Orbitrap analysis;
resolution 120,000; mass range 380–1,500 m/z; RF lens was set to 30%,
AGC target was set to standard, maximum injection time was set to Auto,
microscan 1, Monoisotopic peak determination was set to peptide,
intensity threshold 5 × 10^3, charge state 2–6, data dependant mode was
set to cycle time, time between master scans was set to 3 s, and
exclusion duration was set to 60 s). MS1 spectra were acquired in a
profile mode.
MS2 analysis consisted of CID and was carried out as follows: isolation
mode quadrupole, isolation window 0.7, collision energy mode fixed, CID
collision energy 30%, CID activation time 10 ms, activation Q 0.25,
normalised AGC target was set to standard, maximum injection time 50
ms, microscan was set to 1, detector type ion trap, and mass range
400–1,200.
After acquisition of each MS2 spectrum, we collected an MS3 spectrum
using the following parameters: number of SPS precursor was set to 10,
MS isolation window 0.7, MS2 isolation window was set to 2, activation
type HCD, HCD collision energy 55%, Orbitrap resolution 50,000, scan
range 100–500, normalised AGC target 400%, maximum injection time 120
ms, and microscan was set to 1. Data for both MS2 and MS3 were acquired
in centroid mode. The real-time search feature was active during the
analysis using uniprot-proteome _up000005640.fasta.
Ribosome profiling
Stranded mRNA-seq and ribosome profiling (Ribo-seq) libraries were
generated by EIRNA Bio ([268]https://eirnabio.com) from flash-frozen
cell pellets. Cell pellets were lysed in ice-cold polysome lysis buffer
(20 mM Tris, pH 7.5, 150 mM NaCl, 5 mM MgCl[2], 1 mM DTT, 1% Triton
X-100) supplemented with cycloheximide (100 μg/ml). For stranded
mRNA-seq, total RNA was extracted from 10% of lysate using TRIzol,
before mRNA was poly(A)-enriched, fractionated, and converted into
Illumina-compatible cDNA libraries. For Ribo-seq, the remaining lysates
were digested in the presence of 35U RNase 1 for 1 h. After RNA
purification and size selection of ribosome-protected mRNA fragments on
15% urea–PAGE gels, contaminating rRNA was depleted from samples using
EIRNA Bio’s custom biotinylated rRNA depletion oligos before the
enriched fragments were converted into Illumina-compatible cDNA
libraries. Both stranded mRNA-seq libraries and Ribo-seq libraries were
sequenced using 150PE on Illumina’s NovaSeq 6000 platform to depths of
20 million and 100 million raw read pairs per sample, respectively.
Untargeted metabolomic profiling
A375 cells were treated with 1 μM GCN2iB (or DMSO as a vehicle control)
for 12, 24, and 36 h, with a medium change at 24 h. At each time point,
cells were collected by trypsinisation and washed twice in PBS, and
cell pellets were snap-frozen in liquid nitrogen. Untargeted metabolic
profiling was performed by Metabolon, Inc with the Global Discovery HD4
panel, which uses ultra-high-performance liquid chromatography–tandem
mass spectrometry (UHPLC-MS/MS). Sample preparation, mass detection,
quantification, and initial data analysis were carried our following
the Metabolon, Inc protocols described previously ([269]Evans et al,
2009, [270]2012, [271]2014; [272]DeHaven et al, 2010). Further pathway
analysis was performed with MetaboAnalyst 5.0
([273]www.metaboanalyst.ca).
Bioinformatics analyses
RNA sequencing
Raw data processing
Read count abundance was generated from raw data FASTQ files by
pseudo-aligning these to the Homo sapiens GENCODE “comprehensive”
reference transcriptome (GRCh38.p12/release 39) using Salmon v1.6.0
([274]Patro et al, 2017). Transcript isoform-level counts were then
imported to R Programming Language (R) ([275]R Core Team, 2016) and
summarised to gene-level counts (adjusting for gene length) using the
tximport package in R.
Protein-coding genes were then isolated from the raw counts based on
the GENCODE biotype keyword “protein_coding.” Genes with zero counts
across all samples were removed. The raw counts of the remaining genes
were then converted to a DESeq2 ([276]Love et al, 2014) object for
normalisation. For downstream analyses, the negative
binomial–distributed normalised counts were converted to regularised
log (rlog) expression levels via the rlog function of DESeq2 in R.
Variance-stabilised expression levels were also generated.
Differential expression analysis
Differential expression analysis was conducted on the negative
binomial–distributed normalised counts with an FDR set at 5%. After
differential expression, log (base 2) fold changes (log[2]FC) were
shrunk via the lfcshrink function of DESeq2. A gene was defined as
differentially expressed if it passed the Benjamini–Hochberg q ≤ 0.05.
ATF4/DDIT3 targets
ChIP-seq targets of ATF4 and CHOP were extracted from a mouse study
([277]Han et al, 2013) and converted to human orthologues using the
Mouse Genome Informatics database ([278]http://www.informatics.jax.org,
retrieved May 2022).
Hierarchical clustering
Hierarchical clustering and heatmap generation was performed using the
ComplexHeatmap ([279]Gu et al, 2016) package using Euclidean distance
or one minus Pearson correlation distance, and Ward’s linkage.
Clustering was performed in a supervised manner using statistically
differentially expressed genes or proteins belonging to a particular
curated pathway, for example, HALLMARK_MYC_TARGETS_V1.
Proteomics
Corrected reporter intensities were extracted from the proteinGroups
table and further normalised in the same manner as the method
underlying the RNA-seq DE analysis ([280]Love et al, 2014). Briefly,
for each gene i, its expression in a pseudo-reference sample was
constructed as the geometric mean intensity across samples:
[MATH: Ri=exp(1n∑jlog(I
ij))=(∏<
mi>jIij)1/n<
/msup>, :MATH]
and size factors were computed for each sample j as its median
intensity:
[MATH:
Sj=median<
/mtext>iIij<
/mi>Ri. :MATH]
The normalised intensity for each gene i in sample j was then
calculated as
[MATH:
Nij=Iij<
mi>Sj. :MATH]
Protein differential expression (DE) analysis between GCN2-inhibited
cultures and the DMSO control at 48 h after treatment was performed on
protein groups detected in all 4 DMSO and all 3 GCN2iB samples. Welch’s
two-sided t tests were performed on the log[2]-transformed normalised
intensities; P-values were adjusted using the Benjamini–Hochberg
multiple testing correction.
Overrepresentation analysis
For the purpose of overrepresentation analyses, genes were classified
as differentially expressed if adjusted P < 0.05; unless otherwise
noted, no effect size (log fold change) threshold was applied.
Gene Ontology (GO) overrepresentation analyses were performed using the
enrichGO function from the clusterProfiler package (v.3.18.1 [[281]Yu
et al, 2012]). Genes were indexed by their Ensembl identifier, and
annotations were taken from the org.Hs.eg.db database in Bioconductor
(v.3.12.0). Genes detected in each data set were used as background.
Analyses were restricted to biological_process GO terms with between 10
and 500 constituents; adjusted P-values were corrected with the
Benjamini–Hochberg procedure, and gene sets were reported with P- and
q-value cut-offs of 0.05.
Similarly, KEGG overrepresentation analyses were performed using the
enrichKEGG function from the clusterProfiler package (v.3.18.1 [[282]Yu
et al, 2012]). Genes were indexed by their NCBI gene ID; the conversion
was performed using the biomaRt package (v.2.46.3 [[283]Durinck et al,
2009]) with the default “Ensembl” mart. Genes for which the conversion
succeeded and that were detected in each data set were used as
background. Local annotations were provided using the Bioconductor
KEGG.db package (v.3.2.4). Analyses were restricted to KEGG pathways
with between 10 and 500 constituents; adjusted P-values were corrected
with the Benjamini–Hochberg procedure, and pathways were reported with
P- and q-value cut-offs of 0.05.
GSEA
GSEA was performed using the fgsea package (v.1.16.0 [[284]Korotkevich
et al, 2021 Preprint]). Annotations to MSigDB Hallmark categories
([285]Liberzon et al, 2015) were provided by the msigdbr package
(v.7.5.1). Genes were ordered by their log2 fold change values after
shrinkage was applied (see section RNA sequencing). The P-value
boundary was set to 0 during the GSEA calculations, but adjusted
P-values were truncated in downstream plots.
Identification of mRNA and protein groups
We used test statistics for GCN2iB versus DMSO at 48 h for both
mRNA-seq and proteomics. mRNA and proteins were compared and stratified
into 5 groups.
* Group 1
+ mRNA NS and protein NS
* Group 2
+ mRNA SS up-regulated and protein SS up-regulated
+ mRNA SS up-regulated and protein NS, but up-regulated
+ mRNA NS, but up-regulated and protein SS up-regulated
* Group 3
+ mRNA SS up-regulated and protein SS down-regulated
+ mRNA SS up-regulated and protein NS, but down-regulated
+ mRNA NS, but up-regulated and protein SS down-regulated
* Group 4
+ mRNA SS down-regulated and protein SS down-regulated
+ mRNA SS down-regulated and protein NS, but down-regulated
+ mRNA NS, but down-regulated and protein SS down-regulated
* Group 5
+ mRNA SS down-regulated and protein SS up-regulated
+ mRNA NS, but down-regulated and protein SS up-regulated
+ mRNA SS down-regulated and protein NS, but up-regulated
Note 1: log[2]FC, log[2] fold change.
Note 2: NS, not statistically significant.
Note 3: SS, statistically significant.
Note 4: up-regulated, log[2]FC > 0.
Note 5: down-regulated, log[2]FC < 0.
Note 6: SS is defined as adjusted P < 0.05.
For each group, 1 to 5, mRNA and proteins were further categorised
based on protein designations from [286]Uhlén et al (2015), namely: SP,
secreted; TM, transmembrane; IC, intracellular; SPTM, SP and TM. The
presence of each mRNA/protein from each category (1–5) and each
subcategory (SP, TM, IC, SPTM) was checked in different Gene Ontology
terms:
* GO:0042254 Ribosome biogenesis
* GO:0022613 Ribonucleoprotein complex biogenesis
* GO:0000502 Proteasome complex
* GO:0005839 Proteasome core complex
* GO:0030198 Extracellular matrix organization
* GO:0043062 Extracellular structure organization
Reactome enrichment analysis
Pathway enrichment analysis was performed on genes passing adjusted P <
0.05 via the ReactomeF1 plugin in Cytoscape. Selected statistically
significantly enriched pathways were then plotted in R as networks via
the enrichplot and ReactomePA packages.
Identification of druggable targets
Significantly (adjusted P < 0.05) deregulated proteins were
cross-checked against potentially druggable proteins described in the
Human Protein Atlas
([287]https://www.proteinatlas.org/humanproteome/tissue/druggable).
Statistical analysis
Statistical analysis and graphical representation were conducted with
GraphPad Prism v.10 software. The statistical test employed for each
experiment is stated in the corresponding figure legend. For all
experiments, P < 0.05 was considered statistically significant.
Supplementary Material
Reviewer comments
[288]LSA-2024-03014_review_history.pdf^ (800.8KB, pdf)
Acknowledgements