Abstract
Cellular senescence affects many physiological and pathological
processes and is characterized by durable cell cycle arrest, an
inflammatory secretory phenotype and metabolic reprogramming. Here, by
using dynamic transcriptome and metabolome profiling in human
fibroblasts with different subtypes of senescence, we show that a
homoeostatic switch that results in glycerol-3-phosphate (G3P) and
phosphoethanolamine (pEtN) accumulation links lipid metabolism to the
senescence gene expression programme. Mechanistically, p53-dependent
glycerol kinase activation and post-translational inactivation of
phosphate cytidylyltransferase 2, ethanolamine regulate this metabolic
switch, which promotes triglyceride accumulation in lipid droplets and
induces the senescence gene expression programme. Conversely, G3P
phosphatase and ethanolamine-phosphate phospho-lyase-based scavenging
of G3P and pEtN acts in a senomorphic way by reducing G3P and pEtN
accumulation. Collectively, our study ties G3P and pEtN accumulation to
controlling lipid droplet biogenesis and phospholipid flux in senescent
cells, providing a potential therapeutic avenue for targeting
senescence and related pathophysiology.
Subject terms: Metabolomics, Senescence, Metabolism, Fat metabolism,
Lipids
__________________________________________________________________
Tighanimine et al. perform integrative time-resolved transcriptome and
metabolome analysis in senescent cells and find that
glycerol-3-phosphate and phosphoethanolamine accumulate and rewire
lipid metabolism to promote senescence.
Main
Senescence is a stable cell cycle arrest in response to diverse forms
of non-lethal cellular stress^[62]1,[63]2. It involves many
physiological and pathophysiological processes, including wound
healing, embryonic development, age-related diseases and aging. Two
potent tumour suppressors, p53 and retinoblastoma (RB), orchestrate
establishing and maintaining the senescence phenotype. During
senescence, p53 becomes stabilized, inducing the expression of the
cyclin-dependent kinase inhibitor CDKN1A (alias p21). RB is
instrumental in assembling a co-repressor complex in senescence that
inactivates the pro-proliferative transcription factor E2F. RB activity
is negatively regulated by CDK-dependent phosphorylation and positively
by CDKN2A (alias p16), the expression of which is strongly upregulated
in senescence. Unperturbed cell mass accumulation during senescence
cell cycle arrest may result in enlarged morphology with a high
cytoplasm-to-nucleus ratio^[64]3. Intracellular compartments are also
affected during senescence, with an increase in lysosomal mass and
activity reflected in the rise of the senescence biomarker
senescence-associated β galactosidase (SABG)^[65]4.
Senescent cells functionally interact with immune cells via the
senescent-associated secretory phenotype (SASP), characterized by the
production of inflammatory cytokines (such as interleukin (IL)-6, IL-1α
and IL-1β), chemokines (such as CCL2 and CXCL8), immune modulators
(prostaglandins) and matrix-remodelling factors (such as MMP, serpin,
PAI1 and TIMP)^[66]1. The SASP may also spread senescence to its
cellular environment in a paracrine fashion. Acute responses mostly
resolve with the clearing of senescent cells by the immune cells and
may represent an important tumour-suppressor mechanism; however,
chronic responses maintain an environment promoting tissue inflammation
and fibrosis that favours tumour development and other age-related
diseases. Therefore, therapeutic strategies aiming to eliminate
senescence (senolytic drugs) or offset the SASP (senomorphic drugs)
present interesting inroads for treating several age-related
pathological conditions^[67]5.
Cellular metabolism is largely impacted during senescence^[68]6–[69]12
and the rewiring of these metabolic adaptations may represent a
powerful avenue for therapeutic interventions^[70]2. In response to
oncogenic insults and telomere erosion, the mitochondria of senescent
cells tend to decrease ATP production and increase reactive oxygen
species (ROS)^[71]13. Hence, energy metabolism is shifted to
glycolysis^[72]10. To regenerate NAD^+ levels for glycolytic flux and
maintain redox status, lactate dehydrogenase (LDH) reduces cytosolic
pyruvate to lactate, while malate dehydrogenase 1 (MDH1) reduces
oxaloacetate to malate. Another common metabolic hallmark of cellular
senescence is the accumulation of lipid droplets^[73]8,[74]12,[75]14.
Profound alterations of triacylglycerol (TAG) metabolism reflect and
result from the complex interplay between lipid uptake, synthesis and
fatty acid oxidation (FAO)^[76]2. CD36-mediated free fatty acid (FFA)
uptake is upregulated in senescent cells^[77]15. This effect and
increased enzyme activity in fatty acid synthesis may account for TAG
accumulation; however, the involvement of fatty acid synthase (FASN)
and acetyl-CoA carboxylase (ACC) is variable depending on the cell type
and senescence insults, as their activity is rather downregulated
during oncogene-induced senescence (OIS)^[78]11,[79]16. In addition,
there is evidence of increased FAO in senescent cells^[80]2. It is
unclear whether FAO upregulation during senescence mainly serves
purposes of energy homoeostasis, lipodetoxification, SASP maintenance
or epigenetic modifications through histone acetylation^[81]2.
Similarly, the role of lipid droplet formation may be multi-faceted. It
has been proposed as a defence mechanism to cope with metabolic stress.
The formation of polyunsaturated fatty acids (PUFAs), enriched in the
lipid droplets of senescent cells, could also participate in NAD^+
recycling^[82]17. PUFAs are prone to lipid peroxidation in the context
of ROS production and may severely harm the integrity of cellular
membranes. Thus, lipid droplet formation may represent a protective
mechanism to sequester potentially toxic compounds such as
glycerolipids and PUFAs away from cell membranes.
On the other hand, lipid accumulation could also contribute to the
senescence programme, as the modulation of both CD36-dependent lipid
uptake and FASN activity affects the entry into senescence^[83]16.
Furthermore, structural features, such as the stiffness of the
extracellular matrix and cell geometry, have been shown to regulate
both senescence and lipid droplet formation through changes in the
endoplasmic reticulum (ER) and Golgi trafficking^[84]18,[85]19. In
addition, phospholipid (PL) synthesis may be essential to sustain cell
growth and organelle remodelling in senescent cells, though this needs
to be addressed. Finally, it remains to be established whether
strategies altering lipid metabolism and the neutral versus PL switch
in senescent cells may act as senolytics and senomorphics or be used as
a senogenic anticancer therapy.
In this study, we undertook a time-resolved, multi-layered, integrative
profiling approach to identify the clocks driving the senescence
programme triggered by distinct senescent stressors. Previously, the
combined analysis of transcriptome and epigenome profiles defined the
hierarchical organization of the transcription factor network acting on
the epigenetic state of enhancers to drive the senescent fate^[86]20.
Here, we used the same experimental setting on human fibroblasts to
interrogate the metabolic clock by metabolomic analysis of polar and
hydrophobic compounds. Our results identify G3P and pEtN metabolism as
potent regulators of the senescent programme at the nexus of TAG and PL
metabolism. Furthermore, we show that glycerol kinase (GK) and
phosphate cytidylyltransferase 2, ethanolamine (Pcyt2) activities,
which catalyse the regulatory steps in TAG and PL synthesis, impact G3P
and pEtN levels in a homoeostatic fashion controlling the senescence
programme. Finally, we provide evidence that pharmacological inhibitors
of GK activity act as senomorphics, thus suggesting a new therapeutic
target for interventions in age-related diseases and cancer.
Results
The metabolic landscape of senescent cell subtypes
To identify the metabolic signatures and potential metabolic
vulnerabilities of individual senescent cells, we performed targeted,
time-resolved metabolic profiling using mass spectrometry (MS)-based
analysis in normal, human diploid fibroblasts (strain WI38) exposed to
diverse forms of senescence-inducing stress, including hyper-active
oncogenes (RAS and RAF OIS), replicative senescence (RS) and DNA
damage-induced senescence (DDIS; induced by etoposide). For comparison,
we included cells undergoing quiescence by serum withdrawal, as well as
proliferating cells (time D00 in each condition) (Fig. [87]1a). To
track senescence dynamics and for downstream metabolome–transcriptome
integration, we performed time-series gene expression profiling. As
previously published^[88]20, senescence dynamics are inducer-specific,
spanning different time scales. Accordingly, we intermittently sampled
cells in line with inducer-specific senescence dynamics to adequately
cover the establishment and maintenance phases of senescence (Fig.
[89]1a).
Fig. 1. Untargeted metabolomics reveals a shared metabolic signature across
several senescence models.
[90]Fig. 1
[91]Open in a new tab
a, Experimental design for the integrative analysis of time-resolved
metabolome and transcriptome datasets obtained from WI38 fibroblasts
undergoing RAS and RAF OIS, etoposide-mediated DDIS, RS and quiescence
(Q). D, day. b–e, Heat maps showing modules of temporally coexpressed
metabolites in WI38 fibroblasts for the indicated senescence inducers
at indicated time points using a hierarchical clustering method
(WGCNA). Roman numerals refer to different metabolite modules. Data are
expressed as row z scores collected from three biologically independent
experiments per condition. GSSG, glutathione disulfide (oxidized
glutathione); UDP-Gal/Glc, uridine diphosphate galactose/glucose;
UDP-GlcNAc, uridine diphosphate N-acetylglucosamine. f, Integrated
dynamic metabolome PCA for cells undergoing RAS and RAF OIS, DDIS, RS
and Q as control. Metabolite levels were normalized by the ComBat tool.
Dashed lines depict the metabolome trajectory for each treatment. g,
Correlation circle for the percentage contribution of the indicated
metabolites to principal components PC1 and PC2 of f.
Congruent with our published data^[92]20, differentially expressed
genes for each senescent subtype were partitioned into various dynamic
gene expression modules (Extended Data Fig. [93]1a and Supplementary
Table [94]1) with distinct functional over-representation profiles in
line with the senescence phenotype (Extended Data Fig. [95]1b and
Supplementary Table [96]2). In particular, cell-cycle-related (RAS,
module V; RAF, module II; DDIS, module IV; and RS, module I) and
SASP-related inflammatory transcriptional modules (RAS, modules III and
IV; RAF, module III; DDIS, module I; and RS, modules II) were similar
across all senescence models, which we further corroborated by
expression profiling of a core senescence gene signature (Extended Data
Fig. [97]1c)^[98]21,[99]22. This analysis confirmed that the cells in
our time courses display classic senescence transcriptome features.
Extended Data Fig. 1. Transcriptome Evolution of Senescence Inducers.
[100]Extended Data Fig. 1
[101]Open in a new tab
A: Heat maps showing modules of temporally coexpressed genes for the
indicated senescence inducers and quiescence (Q) at the indicated time
points using an unsupervised weighted clustering network analysis
(WGCNA) approach. Roman numerals refer to different gene clusters. Data
are expressed as row Z scores collected from two biologically
independent experiments per condition. B: Functional
over-representation map depicting Molecular Signaling Database(MSigDB)
hallmark (H.) gene sets associated with each transcriptomic cluster for
the indicated senescence inducers. Circles are colour-coded according
to the FDR-corrected p value based on the hypergeometric text comparing
the overlap between the set of genes in each cluster and the respective
list of genes in each MSigDB pathway. Size is proportional to the
percentage of genes in the MsigDB gene set belonging to the cluster.
N > 100 genes per transcriptomic module for each senescence inducer.
Exact values for raw p values, adjusted p values and overlap (absolute
and relative) between each pair of sets are reported in Supplementary
Table [102]S2. C: Expression heat map of core senescence genes for the
indicated senescence inducers and quiescence as control.
To characterize the metabolic evolution of the different senescence
subtypes, we identified metabolites by a targeted mode in the MS
analysis. We independently clustered their time courses using weighted
correlation network analysis (WGCNA), identifying metabolite modules
with highly correlated metabolite expression trajectories, thereby
revealing senescence state- and inducer-specific metabolite patterns
(Fig. [103]1b–e, Extended Data Fig. [104]2a and Supplementary Tables
[105]3 and [106]4). The levels of 115 metabolites in RAS OIS, 71 in RAF
OIS, 107 in DDIS, 118 in RS and 94 in quiescence were significantly
changed (adjusted P value < 5%). Consistent with previous studies in
various cell biology models of senescence^[107]6–[108]12, we found
increased fatty acid metabolism (such as acylcarnitines), increased
glucose shunts to lactate, pentose phosphate pathway (sedoheptulose-7
phosphate; S7P) and uridine diphosphate N-acetylglucosamine
(UDP-GlcNAc) and altered central carbon metabolism (such as
α-ketoglutarate (α-KG) and malate) and the Kennedy pathway (such as
pEtN).
Extended Data Fig. 2. Metabolome profile of quiescent cells and benchmarking
batch-effect correction using ComBat.
[109]Extended Data Fig. 2
[110]Open in a new tab
A: Heat map showing modules of temporally coexpressed metabolites in
WI38 fibroblasts for quiescence at indicated time points using a
hierarchical clustering method (WGCNA). Roman numerals refer to
different metabolite clusters. Shown are the top forty metabolites
based on the most significant adjusted p values. Data are expressed as
row Z scores collected from three biologically independent experiments
per condition.B: Experimental design for batch correction validation.
DNA damage-induced senescence (DDIS) and quiescence (Q) samples
correspond to the same, as shown in Fig. [111]1a. The validation
dataset included technical replicates of a subset of those samples and
was measured in the same mass spectrometry run. C-E: Visualization of
the average computed values for (C) relative standard deviation (RSD),
(D) repeatability, and (E) Bhattacharyya distance for each approach.
F-H: Comparison between the obtained values for the ComBat approach
following quantile normalization (QN) and the other approaches for each
measured peak (or sample) based on three metrics: (F) RSD, (G)
repeatability and (H) Bhattacharyya distance. Black lines show the
identity function. I- J: PCA plots depicting the average of each sample
used for batch correction validation (I) before and (J) after quantile
normalization and ComBat.
Next, we visualized the metabolome dynamics of each senescence inducer
to identify commonalities and specificities of individual senescent
subtypes, performing an integrated dynamic metabolome
principal-component analysis (PCA) (Fig. [112]1f,g). Because MS-based
metabolome analysis is prone to technical variations, making an
accurate integration of disparate datasets challenging, we first
normalized all metabolome datasets using ComBat (Extended Data Fig.
[113]2b–j)^[114]23. The PCA illustrated three key points. First,
metabolic landscapes of senescence and quiescence are diametrically
opposed (PC1, 31.8%). Second, the overall temporal trajectories between
senescence subtype metabolomes correspond to neighbouring states,
finishing at the top right PCA quadrant (Fig. [115]1f). Third, plotting
the correlation between metabolites and the principal components (Fig.
[116]1g) identifies metabolites that contribute significantly to the
quiescence-associated metabolic shifts (top left quadrant of the
correlation circle) and senescence-associated metabolic shifts (SAMS)
(top right quadrant of the correlation circle), notably α-KG, G3P,
pEtN, UDP-GlcNAc, inosine, S7P, oxypurinol, acylcarnitines and lactate.
We consolidated the SAMS by calculating, for each senescence subtype,
the ratios between metabolites and their immediate precursors or
end-products in the same metabolic pathway, using as denominator start
(proliferation) and as numerator end point (senescence) metabolite
levels of the individual time courses (Fig. [117]2a–d). Irrespective of
the inducer, senescent cells significantly increased lactate:pyruvate,
α-KG:succinate, G3P:glycerol, G3P:di-hydroxy acetone phosphate (DHAP)
and pEtN:CDP-ethanolamine (Etn) ratios. To underscore the kinetics of
these metabolites, we visualized the curve of their fold changes over
time compared to day 0 in RAS OIS and DDIS. In both instances, starting
2 days after stress induction, the metabolite shift increased almost
linearly before reaching a plateau at 10–14 days of treatment (Extended
Data Fig. [118]3a,b). An increased lactate:pyruvate ratio is consistent
with the glycolytic shift and mitochondrial activity decrease observed
in senescence^[119]10,[120]13. In addition, a high proportion of the
two oncometabolites, α-KG and succinate, was previously observed as a
p53-dependent senescence response of KRAS-mutant cancer cells, leading
to the modulation of α-KG-dependent dioxygenases and tumour
suppression^[121]23; however, the mechanistic underpinnings and
functional implications of altered G3P:DHAP, G3P:glycerol and
pEtN:CDP-Etn ratios in senescence regulation are unknown.
Fig. 2. Identification of common SAMS.
[122]Fig. 2
[123]Open in a new tab
a–d, Fold change of the ratios between the indicated metabolites in
WI38 fibroblasts undergoing RAS OIS (n = 3), RAF OIS (n = 3), DDIS
(n = 3) and RS (n = 6) measured for each treatment at the last time
point of the kinetics and relative to the values of proliferating
cells. The pEtN:CDP-Etn ratio in RS was calculated using D11 as a
proliferative control. Bars represent the means of biological
replicates ± s.d. Indicated P values were calculated using an unpaired
two-sided Student’s t-test.
[124]Source data
Extended Data Fig. 3. SAMS induction kinetics and specific metabolic response
to distinct senescence inducers.
[125]Extended Data Fig. 3
[126]Open in a new tab
Fold changes kinetics of the indicated metabolites belonging to the
SAMS in WI38 fibroblasts undergoing DDIS (A) or RAS-OIS (B). n = 3
biologically independent experiments for each time point and condition.
Indicated p values were calculated using an unpaired two-sided
Student’s t-test. Data are presented as mean values +/- SD. C: Sparse
Least Squares Regression – Discriminant Analysis (sPLS-DA) depicting
the two orthogonal components that maximize the separation of samples
treated with different CS inducers. Metabolite levels were normalized
by the Combat tool considering fibroblasts undergoing RAS- and RAF-OIS,
DDIS, and RS. Dashed lines depict the metabolome trajectory for each
treatment. The background colour depicts the predicted CS inducer given
a sample metabolic state. D: Correlation circle depicting the
projection of the sPLS-DA selected metabolites in each component. E:
Normalized levels for the metabolites which activity best discriminate
experimental treatment.
[127]Source data
Next, we performed a sparse partial least squares discriminant analysis
(sPLS-DA) to identify metabolite signatures that could discriminate
between the different senescence inducers (Extended Data Fig.
[128]3c–e). sPLS-DA separated samples according to their treatment in
three sectors (Extended Data Fig. [129]3c), with cells undergoing RS
(purple sector), RAS OIS (blue sector) and RAF OIS (green sector)
following distinct dynamics. In comparison, DDIS was intercalated
between the three sectors. We then associated each sPLS-DA component
with its respective metabolite(s) and its/their corresponding median
level (Extended Data Fig. [130]3d,e). We observed that metabolites
positively related to the first component (palmitoyl-carnitine and
ribose phosphate; Extended Data Fig. [131]3d) were produced at
exceptionally high levels in RS, thus distinguishing it from the other
senescence inducers (Extended Data Fig. [132]3e). Conversely,
butyryl-carnitine was negatively associated with the first component
(Extended Data Fig. [133]3d), presenting higher levels, especially in
RAS OIS and DDIS (Extended Data Fig. [134]3e). Finally, GLN was
positively associated with the second sPLS-DA component (Extended Data
Fig. [135]3d) and its lower levels specified RAF OIS (Extended Data
Fig. [136]3e).
We confirmed these results in additional senescence models: human
primary myoblasts undergoing RAS OIS and RS (Extended Data Fig.
[137]4a). In particular, cell-cycle- and SASP-related transcriptional
modules (Extended Data Fig. [138]4a,b), senescence index (Extended Data
Fig. [139]4c), SAMS (Extended Data Fig. [140]4d–f) and its kinetics
(Extended Data Fig. [141]4g–j) were highly similar.
Extended Data Fig. 4. Transcriptome and metabolome profiles of senescent
myoblasts.
[142]Extended Data Fig. 4
[143]Open in a new tab
A,D: Heat maps showing modules of temporally coexpressed (A) genes and
(D) metabolites for myoblasts undergoing RAS-OIS using an unsupervised
weighted clustering network analysis (WGCNA) approach. Roman numerals
refer to different coexpression clusters. Data are expressed as row Z
scores collected from (A) two and (D) three biologically independent
experiments per condition. B: Functional over-representation map
depicting Molecular Signaling Database(MSigDB) hallmark (H.) gene sets
associated with each transcriptomic cluster for the indicated
senescence inducers. Circles are colour-coded according to the
FDR-corrected p value based on the hypergeometric text comparing the
overlap between the set of genes in each cluster and the respective
list of genes in each MSigDB pathway. Size is proportional to the
percentage of genes in the MsigDB gene set belonging to the cluster.
N > 100 genes per transcriptomic module for each senescence inducer.
Exact values for raw p values, adjusted p values and overlap (absolute
and relative) between each pair of sets are reported in Supplementary
Table [144]S2. C: Expression heat map of core senescence genes for
myoblasts undergoing RAS-OIS. E: Fold change of the ratios between the
indicated metabolites or of metabolite levels in myoblasts undergoing
RAS-OIS (n = 3), measured at the indicated time point of the kinetics
and relative to the values of proliferating cells. Data are presented
as mean values +/- SD. Indicated p values were calculated using an
unpaired two-sided Student’s t-test. F: Fold change of the ratios
between the indicated metabolites or of metabolite levels in myoblasts
undergoing replicative senescence (n = 6) and relative to the values of
proliferating cells. Data are presented as mean values +/- SD.
Indicated p values were calculated using an unpaired two-sided
Student’s t-test. G-J: Fold changes kinetics of the indicated
metabolites belonging to the SAMS in myoblasts undergoing RAS-OIS.
n = 3 biologically independent experiments for each time point.
Indicated p values were calculated using an unpaired two-sided
Student’s t-test. Data are presented as mean values +/- SD. (The panel
C contains small letters and numbers that overlap with the heatmap and
the X axis. They need to be removed).
[145]Source data
Altogether, our investigation of metabolome dynamics in different cell
biology models of senescence defined a dynamic inducer-specific modular
organization of the senescence metabolic programme with a shared and
pronounced metabolic shift, particularly in central carbon metabolism,
toward G3P and pEtN accumulation.
Inhibition of mTOR and α-KG links SAMS and SASP expression
To test whether SAMS was predictive of senescence status, we
administered the mTOR inhibitor rapamycin, an established
senomorphic^[146]24–[147]26, to WI38 cells undergoing DDIS and
dimethyloxalylglycine (DMOG), a hypoxia-mimetic and competitive α-KG
antagonist^[148]24,[149]27, to cells undergoing RAS OIS.
Hypoxia-mimetic compounds were recently shown to suppress SASP
expression in vitro and in vivo^[150]28.
Rapamycin and DMOG significantly reduced the number of SABG-positive
cells by 2.5-fold and fourfold, respectively (Fig. [151]3a,b). Next, we
performed time-resolved gene expression and metabolic profiling to
measure rapamycin- and DMOG-mediated changes in the senescence
transcriptome and metabolome. Gene clustering, pathway enrichment and
gene set enrichment analysis (GSEA) revealed that rapamycin and DMOG
shift the transcriptional landscape closer to proliferative control
cells, markedly perturbing SASP expression and downregulating the
cyclin-dependent kinase inhibitor CDKN1A/p21 (Fig. [152]3c,d, Extended
Data Fig. [153]5a–d and Supplementary Tables [154]5–[155]8). We applied
a river plot analysis to visualize and quantify the relationship
between the two pharmacological treatments. This analysis pinpointed
genes in DMOG-treated RAS OIS cells that trend similarly to genes in
rapamycin-treated DDIS cells (Extended Data Fig. [156]5e; blue waves
connecting clusters II and III (RAS OIS DMOG) to clusters III and V
(DDIS rapamycin), repressed genes; red waves connecting modules I and V
(RAS OIS DMOG) to modules IV and VI (DDIS rapamycin), activated genes).
In addition, DMOG also provoked a hypoxia-related gene expression
programme, which is consistent with its known antagonistic effect on
Fe(II)/α-KG-dependent dioxygenases, including hypoxia-inducible factor
(HIF) hydroxylases (EGLNs), ribosomal protein hydroxylases (OGFOD),
ten-eleven translocation DNA (TETs) and JmjC histone lysine
demethylases (KDMs)^[157]23,[158]27,[159]29–[160]32 (Extended Data Fig.
[161]5c,d; module I).
Fig. 3. Senescence repression correlates with SAMS repression.
[162]Fig. 3
[163]Open in a new tab
a, Percentage of SABG-positive cells of cultures of WI38 fibroblasts
non-treated (Prolif.) undergoing DDIS in the presence or the absence of
rapamycin for 7 days. b, Percentage of SABG-positive cells of WI38
fibroblasts non-treated (Prolif.) or undergoing RAS-induced senescence
(RAS OIS) treated or not with DMOG for 7 days. c,d, GSEA enrichment of
SASP genes of DDIS rapamycin-treated cells (14 days) (c) and RAS OIS
DMOG-treated cells (7 days) (d). NES, normalized enrichment score. e,f,
Fold changes of SAMS in WI38 fibroblasts undergoing DDIS ± rapamycin
for 14 days (e) and RAS OIS ± DMOG for 7 days (f). For a,b,e,f, bars
represent the means of three biological replicates ± s.d. Indicated P
values were calculated using an unpaired two-sided Student’s t-test.
[164]Source data
Extended Data Fig. 5. Rapamycin and DMOG revert transcriptomic features of
senescence.
[165]Extended Data Fig. 5
[166]Open in a new tab
A: Heat map showing modules of temporally coexpressed genes for WI38
cells undergoing etoposide-mediated DDIS in the presence (+) or the
absence (-) of rapamycin (Rapa) at the indicated time points using an
unsupervised weighted clustering network analysis (WGCNA) approach. B:
Functional over-representation map depicting Molecular Signaling
Database (MSigDB) hallmark gene sets associated with each
transcriptomic module (Fig. [167]3c) for cells undergoing
etoposide-mediated DDIS in the presence or the absence of rapamycin. C:
Heat map showing modules of temporally coexpressed genes for WI38
fibroblasts undergoing RAS-OIS and treated or not with DMOG at the
indicated time points using an unsupervised weighted clustering network
analysis (WGCNA) approach. For panels A, C, roman numerals refer to
different modules. Data are expressed as row Z scores collected from
two biologically independent experiments per condition. D: Functional
over-representation map depicting Molecular Signaling Database (MSigDB)
hallmark gene sets associated with each transcriptomic module (Fig.
[168]3d) for cells undergoing RAS-OIS in the presence or the absence of
DMOG. For panels B,D, circles are colour-coded according to the
FDR-corrected p value based on the hypergeometric text comparing the
overlap between the set of genes in each cluster and the respective
list of genes in each MSigDB pathway. Size is proportional to the
percentage of genes in the MsigDB gene set belonging to the cluster.
N > 100 genes per transcriptomic module for each senescence inducer.
Exact values for raw p values, adjusted p values and overlap (absolute
and relative) between each pair of sets are reported in Supplementary
Table [169]S6 E: River plot depicting the overlaps between gene
expression modules in cells undergoing RAS-OIS in the absence or
presence of DMOG and cells undergoing DDIS in the absence or presence
of rapamycin (Rapa). Blue and red tracks highlight genes down- and
upregulated in both senescence perturbation experiments. F: Heat map
showing modules of temporally coexpressed metabolites in WI38
fibroblasts undergoing etoposide-mediated DDIS in the presence or the
absence of rapamycin for the indicated times using a hierarchical
clustering approach. G: Heat map showing modules of temporally
coexpressed metabolites for RAS-OIS cells in the presence (+) and
absence (-) of DMOG at the indicated time points using a hierarchical
clustering approach. For panels F, G, data are expressed as row Z
scores collected from three biologically independent experiments per
condition and time point.
Congruent with the senomorphic effects on the senescence transcriptome,
metabolic profiling demonstrated that rapamycin and DMOG also markedly
attenuated SAMS (Fig. [170]3e,f and Extended Data Fig. [171]5f,g). In
essence, these results highlight that the identified metabolic
alterations correlate with the senescence status rather than the nature
of the stress inducer and suggest a functional intersection between
SAMS and the senescence gene expression programme.
Glycerol shunt intersects with the senescence programme
Measurement and integration of the transcriptome and metabolome in the
same cells are increasingly applied to elucidate mechanisms that drive
diseases and uncover putative biomarkers (metabolites) and targets
(genes). Previous studies have revealed that functionally related genes
and metabolites show coherent co-regulation patterns^[172]33,[173]34.
Accordingly, we computed the Spearman correlation between all
differentially expressed genes and metabolites, accounting for possible
non-linear molecular interactions for the individual senescence
subtypes and quiescence. We combined the results obtained from each
experiment by selecting gene–metabolite pairs with an absolute
correlation higher than 0.5 for all datasets (Supplementary Table
[174]9). Then, we joined these pairs into a gene–metabolite network
that connects all molecules with similar profiles over time, regardless
of the senescence inducer. The latter allowed us to compute the
betweenness centrality, a measure that detects the amount of influence
a node has over the flow of information in a graph. Figure [175]4a
shows that S7P and G3P have the highest betweenness centrality in the
gene–metabolite network (Supplementary Table [176]10). This result
remains unchanged in the presence of myoblast RAS OIS gene–metabolite
network data (Extended Data Fig. [177]6a). Reactome analysis of
G3P-correlated genes (Fig. [178]4b, Extended Data Fig. [179]6b and
Supplementary Table [180]11) revealed an association with inflammation
and epigenetic regulation of cell cycle genes, thus raising the
possibility that G3P acts as a new core ‘hub’ metabolite central to
regulating the senescence gene expression programme.
Fig. 4. Glycerol-3-P accumulation at the onset of senescence metabolic
reprogramming.
[181]Fig. 4
[182]Open in a new tab
a, Nodes with the highest betweenness values (top 20; Supplementary
Table [183]9) in the gene–metabolite correlation network connecting
genes and metabolites presenting a correlation with an absolute value
>0.5 for RAS OIS and RAF OIS, etoposide-mediated DDIS, RS and Q. MYO9A,
myosin 9-A; SCN9A, sodium voltage-gated channel α subunit 9; HMGN2,
high mobility group nucleosomal binding domain 2; DARS2, aspartyl-tRNA
synthetase 2, mitochondrial; FAM43A, family with sequence similarity 43
member A; HEG1, heart development protein with EGF-like domains 1;
PAPPA, pappalysin 1; GCC2, GRIP and coiled-coil domain containing 2. b,
Reactome analysis of genes correlating either positively or negatively
with G3P accumulation during senescence in WI38 fibroblasts. c,
Simplified scheme of the metabolic pathways involving G3P. d, Heat maps
representing levels of indicated lipid species in WI38 fibroblasts
proliferating (n = 5), undergoing DDIS (n = 5) or RS (n = 4). PC,
phosphatidylcholine; PE, phosphatidylethanolamine; PI,
phosphatidylinositol; PS, phosphatidylserine; PG, phosphatidylglycerol;
FC, fold change. e, Ratio of total PL to TAG levels normalized to
protein content in proliferative WI38 fibroblasts compared to DDIS
(n = 5) or RS (n = 4) cells. f, Immunoblots showing indicated protein
levels in WI38 fibroblasts undergoing RAS OIS (left) or DDIS (right).
Sample processing controls (actin) were migrated into different gels
from those of GK. g, Densitometric quantification of GK and p21 protein
levels relative to actin from three experiments, including the one
shown in panel (f), in RAS OIS (day 7) and DDIS (day 6). h, Western
blots showing indicated protein levels in extracts from WI38
fibroblasts proliferating or undergoing DDIS and non-transfected (−) or
transfected with a control non-silencing siRNA (siCtrl) or an siRNA
targeting the p53 mRNA (si p53) for 4 days. The experiment was repeated
independently twice with similar results. In e,g, data are presented as
mean ± s.d. Indicated P values were calculated using an unpaired
two-sided Student’s t-test.
[184]Source data
Extended Data Fig. 6. The G3P shuttle is not involved in the establishment of
senescence.
[185]Extended Data Fig. 6
[186]Open in a new tab
A: Nodes with the highest betweenness values (top 20) in the overlap
network produced by intersecting a Myoblast-only Gene–Metabolite
Correlation Network, which considers genes and metabolites presenting a
correlation with absolute value higher than 0.5 in myoblast samples,
and the Fibroblast Gene-Metabolite Correlation Network, considering all
senescence inducers (RAS- and RAF-OIS, DDIS, and RS) and quiescence
(Q). B: Genes (green squares) whose expression correlates either
positively or negatively with G3P accumulation during senescence in
WI38 fibroblasts. The size of green squares is proportional to node
betweenness for each target. C: Representative Western blots showing
indicated protein levels in extracts of WI38 fibroblasts proliferating
or undergoing DDIS for 7 days. D: Activity of mitochondrial
Glycerol-3-phosphate dehydrogenase calculated from the measurement of
glycerol-3-phosphate cytochrome c reductase in WI38 fibroblasts
undergoing DDIS or RAS-OIS during 7 days. n = 2 biological replicates.
E: Representative Western blots showing indicated protein levels in
WI38 fibroblasts proliferating (Prolif.) or undergoing RAS-OIS
induction and not infected (-) or infected with an adenovirus
overexpressing GFP or GPD1 for 7 days. F: Densitometric quantification
of p16 and p21 protein levels relative to actin from three experiments,
including the one of the panel, in proliferative, RAS-OIS cells
infected with GFP- or GPD1-overexpressing adenoviruses for 7 days. G:
mRNA levels scored by RT–qPCR in WI38 fibroblasts proliferating
(Prolif.) or undergoing RAS-OIS and infected with an adenovirus
carrying a control scramble shRNA (shCtrl) or an shRNA targeting GPD1
(shGPD1) for 7 days. H: Maximal fold change of the GK mRNA in WI38
fibroblasts and myoblasts induced to senesce under the indicated
conditions relative to proliferating cells as measured by Affymetrix
microarrays (n = 2 for all the samples, except n = 4 for the senescence
sample of RS and n = 3 for proliferative samples of DDIS fibroblasts
and RAS-OIS myoblasts). Data are presented as mean values. I:
Measurement of glycerol uptake in WI38 fibroblast proliferating,
undergoing RAS-OIS (7 days) or DDIS (10 days). The reported values are
relative to those of proliferating cells. For panels F, G, E, bars
represent the means of 3 biological replicates +/- s.d. Indicated p
values were calculated using an unpaired two-sided Student’s t-test.
[187]Source data
G3P is situated at the crossroads of multiple metabolic pathways.
Through the redox conversion to DHAP, G3P can enter glycolysis and
gluconeogenesis (Fig. [188]4c). DHAP reduction to G3P by the G3P
dehydrogenase GPD1 regenerates NAD^+ levels in the cytosolic side of
the G3P shuttle, while the mitochondrial side catalysed by GPD2 leads
to the formation of DHAP and FADH2 that feeds the electron transport
chain. Finally, G3P and FFAs are the critical intermediates for
lipogenesis, TAG and PL synthesis. We hypothesized a crucial role of
the G3P shuttle in the senescence programme, similar to the
malate–aspartate shuttle^[189]35; however, GPD1 and GPD2 protein levels
and GPD2 mitochondrial activity remained unchanged, as exemplified for
DDIS and RAS OIS cells (Extended Data Fig. [190]6c–e). Moreover,
adenoviral-mediated GPD1 overexpression or its shRNA-mediated knockdown
failed to impact the expression of senescence biomarkers, including
CDKN1A/p21, CDKN2A/p16 and IL-6 (Extended Data Fig. [191]6e–g). These
findings thus rule out an involvement of the G3P shuttle in regulating
senescence.
Given its role in lipid synthesis, we tested the implication of G3P in
the lipid metabolism of senescent cells. Lipidomic analysis revealed a
substantial increase of diacylglycerol (DAG) in DDIS compared to
control cells. DAG serves as a precursor of neutral lipids (NLs) TAG
and PL. Notably, both RS and DDIS also led to the accumulation of TAG.
In contrast, the levels of different PL species were not affected
substantially compared to control cells, except for decreased
phosphatidylglycerol (PG) levels (Fig. [192]4d). Consequently, the
amount of total PL relative to total TAG (NL) was reduced in senescent
cells (Fig. [193]4e). Our data support a view in which senescent cells
divert available resources toward converting fatty acids to TAGs stored
in lipid droplets^[194]9,[195]12[.]
GK expression levels were upregulated in all senescence models
(Extended Data Fig. [196]6h), an effect that was confirmed at the
protein level in RAS OIS and DDIS (Fig. [197]4f,g). In contrast, the
invariable increase in GK expression was not mirrored by consistent
glycerol uptake changes that were downregulated in RAS OIS and
increased in DDIS (Extended Data Fig. [198]6i). The tumour suppressor
p53 regulates GK expression, as demonstrated by siRNA- or
shRNA-mediated depletion of p53 in DDIS cells (Fig. [199]4h and
Extended Data Fig. [200]7a). GK catalyses the phosphate transfer from
ATP to glycerol to form G3P, controlling whether G3P, a critical
intermediate at the crossroad of carbohydrate, lipid and energy
metabolism, leaves the cell as glycerol upon its direct
dephosphorylation or enters intracellular metabolic pathways. To
corroborate the link between p53, GK and G3P accumulation, we analysed
the effects of pharmacological activation of p53 by the small-molecule
MDM2 antagonist Nutlin-3 (ref. ^[201]36). This treatment was sufficient
to activate p21 expression and upregulate GK levels (Extended Data Fig.
[202]7b,c). Consistent with published data^[203]37–[204]39, p53
activation suppressed SASP biomarker IL-1α, IL-6 and CXCL8 expression
(Extended Data Fig. [205]7c). In contrast and congruent with other
senescence inducers, p53 activation led to a SAMS (Extended Data Fig.
[206]7d), including a rise in G3P levels (Extended Data Fig. [207]7e)
and neutral lipid droplet accumulation (Extended Data Fig. [208]7f). We
conclude that p53 controls a senescence programme involving a
p21-dependent cell cycle arrest, GK upregulation, concomitant G3P
accumulation and a SAMS independently of its suppressive effect on the
SASP.
Extended Data Fig. 7. P53 activation recapitulates part of the SAMS.
[209]Extended Data Fig. 7
[210]Open in a new tab
A: Immunoblots showing the levels of the indicated proteins in WI38
fibroblasts proliferating (Prolif.), or undergoing DDIS and infected
with an adenovirus driving the expression of a control shRNA (shCtrl)
or an shRNA targeting p53 for 7 days. The numbers below the top panel
are the quantification of GK levels relative to those of α-tubulin. The
experiment was repeated independently twice. B: Immunoblots blots
showing the indicated protein levels in WI38 fibroblasts proliferating
and treated with vehicle or Nutlin-3 for 3 or 6 days. The numbers below
the top panel are the quantification of GK levels relative to those of
Actin. C: Levels of the indicated mRNAs as measured by RT–qPCR in WI38
fibroblasts proliferating and treated with vehicle or Nutlin-3 for 6
and 10 days. The reported values are relative to those of proliferating
cells. Bars represent the means of 3 biological replicates +/- s.d.
Indicated p values were calculated using an unpaired two-sided
Student’s t-test. D: Representative heat map using hierarchical
clustering showing metabolites in WI38 fibroblasts treated with DMSO or
nutliln-3 for 7 days. Data are expressed as row Z scores collected from
three biologically independent experiments per condition. E: Fold
change of G3P levels in WI38 fibroblast treated with DMSO or nutliln-3
for 7 days, relative to the value of DMSO-treated cells. Bars represent
the means of 3 biological replicates +/- s.d. Indicated p values were
calculated using an unpaired two-sided Student’s t-test. F:
Representative images of DAPI and LipidTox staining of WI38 fibroblasts
treated with DMSO or nutlin-3 for 7 days. The experiment was repeated
independently twice with similar results. Scale bars represent 20 µm.
[211]Source data
To evaluate the functional role of GK in the senescence programme, we
first transduced proliferating fibroblasts with an adenovirus
overexpressing GK (GK-OE). GK-OE was sufficient to trigger a
senescence-like state, as evidenced by the dramatic increase in the
percentage of SABG-positive cells and expression of SASP genes CXCL8
and IL-1α (Fig. [212]5a,b). These effects were accompanied by a
considerable accumulation of NLs, consistent with the utilization of
G3P in TAG synthesis (Fig. [213]5c) and a senescence-like metabolic
shift, notably of G3P and pEtN levels (Extended Data Fig. [214]8a).
Thus, GK-OE acts senogenic. Conversely, GK knockdown repressed SASP
genes in RAS OIS cells. At the same time, p21 and p16 expression were
unaffected or minorly affected, respectively (Fig. [215]5d). Thus, a
reduction in GK activity acts as a senomorphic, uncoupling SASP
expression and senescence arrest. In line with our above findings,
scavenging G3P by overexpressing G3P phosphatase (G3PP-OE)^[216]40
(forcing G3P conversion to glycerol) had similar effects to GK
depletion, reversing G3P accumulation in RAS OIS cells (Fig. [217]5e
and Extended Data Fig. [218]8b), concomitant with a downregulation of a
select number of SASP genes (Fig. [219]5f). Finally, pharmacological
treatment with thioglycerol, a competitive inhibitor of GK^[220]41,
also reduced SASP factors such as IL-1α, CXCL8 and IL-6 in RAS OIS
(Fig. [221]5g). Together, these results reinforce the crucial role of
G3P metabolism in senescence regulation.
Fig. 5. Glycerol-3-P accumulation drives metabolic senescence programme and
SASP induction.
[222]Fig. 5
[223]Open in a new tab
a, Representative images (left) and percentage (right) of SABG-positive
WI38 fibroblasts infected with GFP-OE or GK-OE adenoviruses for 7 days.
The percentage is also reported for control, non-infected proliferating
cells. n = 3 biologically independent experiments. Data are presented
as mean ± s.d. Indicated P values were calculated using an unpaired
two-sided Student’s t-test. Scale bars, 50 µm. b, mRNA levels of the
indicated SASP markers as measured by RT–qPCR in WI38 fibroblasts
treated as in a, relative to the value of non-infected cells (Prolif.).
n = 3 biologically independent experiments. Data are presented as
mean ± s.d. Indicated P values were calculated using an unpaired
two-sided Student’s t-test. c, Representative images of
4,6-diamidino-2-phenylindole (DAPI) and LipidTox staining of WI38
fibroblasts infected with GFP-OE or GK-OE adenovirus for 7 days. The
experiment was repeated independently three times with similar results.
Scale bars, 20 µm. d, Heat map of the indicated mRNA levels as measured
by RT–qPCR in WI38 fibroblasts proliferating or undergoing RAS OIS and
infected with an adenovirus driving the expression of a control
scramble shRNA (shCtrl) or an shRNA targeting GK mRNA (shGK) for 7 days
(n = 3). Indicated P values were calculated using an unpaired two-sided
Student’s t-test between shCtrl and shGK conditions. e, FC of G3P
levels in WI38 fibroblasts undergoing RAS OIS and infected with GFP-OE
or G3PP-OE adenoviruses for 7 days, relative to the value of
non-infected cells (Prolif.). n = 3 biologically independent
experiments. Data are presented as mean ± s.d. Indicated P values were
calculated using an unpaired two-sided Student’s t-test. f, Heat map of
the indicated mRNA levels as measured by RT–qPCR in WI38 fibroblasts
treated as in e (n = 3). P values (unpaired two-sided Student’s t-test)
in gene expression between GFP and G3PP are indicated. g, Heat map of
the indicated mRNA levels as measured by RT–qPCR in WI38 fibroblasts
subjected to RAS OIS and treated with dimethylsulfoxide (DMSO) or
1-thioglycerol (1 mM) for 7 days, relative to the value of non-infected
cells (Prolif.) (n = 3). Indicated P values were calculated using an
unpaired two-sided bilateral Student’s t-test between DMSO and
1-thioglycerol conditions.
[224]Source data
Extended Data Fig. 8. Effects of perturbing G3P levels on metabolome of
senescent cells and characterization of the pEtN synthesis pathway.
[225]Extended Data Fig. 8
[226]Open in a new tab
A: Heat map showing modules of metabolites in WI38 fibroblast
proliferating or infected with a GFP- or GK-overexpressing adenovirus
for 7 days. B: Heat map showing modules of metabolites in WI38
fibroblast proliferating or undergoing RAS-OIS and infected with a GFP-
or G3PP-overexpressing adenovirus for 7 days. For both panels, a
hierarchical clustering approach was used. Data are expressed as row Z
scores collected from three biologically independent experiments per
condition. C: Measurement of labelled Etn uptake in WI38 fibroblasts
proliferating, undergoing DDIS or RAS-OIS for 7 days after a pulse of
1 hour. The reported values are relative to those of proliferating
cells. Bars represent the mean of 3 biological replicates +/- s.d.
Indicated p values were calculated using an unpaired two-sided
Student’s t-test. D: Curves of decay of labelled Etn in WI38
fibroblasts proliferating or undergoing DDIS, after a pulse of 1 hr
followed by a chase for the indicated times. For each treatment the
values are normalized to those of the 0 hr chase time. Each point
represents the mean of 3 biological replicates +/- s.d. Indicated p
values were calculated using an unpaired two-sided Student’s t-test. E:
Maximal fold change of PCYT2 mRNA in cells induced to senesce under the
indicated conditions relative to proliferating cells as measured by
Affymetrix microarrays (n = 2 for each sample). F: Representative
Western blots showing the indicated protein levels in WI38 fibroblasts
proliferating (Prolif.), undergoing RAS-OIS or DDIS for 7 days. The
experiment was repeated independently 3 times with similar results.
[227]Source data
G3P and pEtN homoeostatic switch regulates senescence
G3P and pEtN are building blocks for TAG and PL synthesis and accrue in
senescent cells. pEtN is utilized in the Kennedy pathway for the
biosynthesis of phosphatidylethanolamine (PE), a major component of
cell membranes accounting for 25–35% of total PL. Ethanolamine (Etn) is
first taken up by cells, subsequently phosphorylated to pEtN by
ethanolamine kinase 1 (ETNK1) and finally conjugated to CDP by
phosphate cytidylyltransferase 2, ethanolamine (PCYT2) to react with
DAG to generate PE by ethanolaminephosphotransferases (EPT and CEPT)
(Fig. [228]6a)^[229]42.
Fig. 6. PCYT2 is less active and dephosphorylated in senescent cells.
[230]Fig. 6
[231]Open in a new tab
a, Schematic overview of the phosphatidylethanolamine pathway
highlighting pEtN and the enzymes involved in the pathway. b, Curves of
decay of labelled pEtN or CDP-Etn in WI38 fibroblasts proliferating or
undergoing DDIS (day 10), after a pulse of 1 h followed by a chase for
the indicated times. n = 3 biological replicates. c, Representative
western blots of a Phos-tag gel (top) and a conventional gel (bottom)
showing the indicated protein levels in extracts from WI38 fibroblasts
proliferating, undergoing DDIS (14 days) or RAS OIS (7 days). The
phosphatase-treated extract is from proliferating cells. Loading
control (actin) was migrated into the same gel as RB. d, Representative
western blots of a Phos-tag gel (top) and a conventional gel (bottom)
showing the indicated protein levels in extracts from WI38 fibroblasts
proliferating or undergoing RAS OIS and non-transfected (-) or
transfected with a non-silencing siRNA or an siRNA targeting the p53
mRNA for 3 days. The phosphatase-treated extract is from proliferating
cells. Dashed lines indicate the cropping of two lanes. Sample
processing control (actin) was migrated into a different gel than p21.
The experiment was repeated independently twice with similar results.
e, Fold change of pEtN:CDP-Etn ratio in WI38 fibroblasts undergoing RAS
OIS and infected with shCtrl-OE or shp53-OE adenoviruses for 7 days,
relative to the value of non-infected cells (Prolif.). n = 3
biologically independent experiments. f, Representative western blots
of a Phos-tag gel (top) and a conventional gel (bottom) showing the
indicated protein levels in extracts from WI38 fibroblasts treated by
Nutlin-3 (10 µM) for the indicated times. The phosphatase-treated
extract is from proliferating cells. The experiment was repeated
independently twice with similar results. g, FC of pEtN:CDP-Etn ratio
in WI38 fibroblasts treated by Nutlin-3 (10 µM) for 7 days. n = 3
biologically independent experiments. h, Representative western blots
of a Phos-tag gel (top) and a conventional gel (bottom) showing the
indicated protein levels in extracts from WI38 fibroblasts treated with
BisIndo.I at the indicated concentrations for 16 h. Dashed lines
indicate the cropping of one lane. For b,e,g, data are presented as
mean ± s.d. All the indicated P values were calculated using an
unpaired two-sided Student’s t-test. For c,h, the experiment was
repeated independently three times with similar results.
[232]Source data
To probe the role of Etn metabolism in senescence, we first performed
flux experiments with exogenous ^13C-Etn (Etn MW + 2). We did not
detect differences in Etn uptake and Etn conversion between
proliferating and senescent cells (Extended Data Fig. [233]8c,d);
however, the pEtN and CDP-Etn (MW + 2) conversion were less effective
in senescent cells compared to proliferative cells, pointing to
decreased Pcyt2 activity (Fig. [234]6b). Accordingly, we measured the
Pcyt2 transcript and protein levels, which were unchanged between
proliferating and senescent cells (Extended Data Fig. [235]8e,f).
Previous studies demonstrated that Pcyt2 activity is positively
regulated by phosphorylation on several serine and threonine
residues^[236]43. We therefore, determined the Pcyt2 phosphorylation
status by Phos-tag analysis. Pcyt2 displayed several band shifts in
extracts from proliferating cells, consistent with its phosphorylation
on multiple sites (Fig. [237]6c). Notably, DDIS and RAS OIS cells
exhibited a dramatic reduction in Pcyt2 band shifts, indicative of
reduced protein phosphorylation compared to control cells. This effect
was p53-dependent because (1) p53 silencing (siRNA- or shRNA-mediated)
rescued Pcyt2 phosphorylation (Fig. [238]6d), while reducing the
pEtN:CDP-Etn ratio in RAS OIS cells (Figs. [239]6e) and (2) p53
activation by Nutlin-3 reduced Pcyt2 phosphorylation (Fig. [240]6f) and
increased the pEtN:CDP-Etn ratio in cells (Fig. [241]6g)^[242]36. Thus,
p53 negatively regulates Pcyt2 phosphorylation and activity in
senescence, resulting in pEtN accumulation. PCYT2 phosphorylation was
sensitive to treatment with the selective PKC inhibitor
bisindolylmaleimide (BisIndo.I) (Fig. [243]6h). Whether p53 controls
PCYT2 phosphorylation by affecting PKC activity or an uncharacterized
PCYT2 phosphatase remains to be determined.
To evaluate the functional consequences of pEtN level alterations, we
performed Pcyt2 knockdown experiments in proliferating cells, thus
blocking pEtN conversion and resulting in an increased pEtN:CDP-Etn
ratio (Fig. [244]7a and Extended Data Fig. [245]9a,b). Analogous to
GK-OE, Pcyt2 knockdown was sufficient to elicit a senescence-like
phenotype, as evidenced by an increase in cells staining positive for
SABG and in the expression of canonical senescence biomarkers CDKN1A,
CDKN2A and IL-1α (Fig. [246]7b,c). In addition, Pcyt2 knockdown also
triggered neutral lipid droplet accumulation (Fig. [247]7d). By
contrast, Pcyt2 OE in RAS OIS cells reduced the expression of SASP
factors IL-1α, IL-1β, IL-6 and CXCL8 (Fig. [248]7e), while
re-establishing a pEtN:CDP-Etn ratio similar to that of proliferating
control cells (Fig. [249]7f). To further corroborate that pEtN
accumulation in cells directs the senescence fate, we overexpressed
ethanolamine-phosphate phospho-lyase (ETNPPL), an enzyme promoting the
breakdown of pEtN to ammonia, inorganic phosphate and
acetaldehyde^[250]44. In line with the above results, ETNPPL-OE lowered
the pEtN:CDP-Etn ratio (Fig. [251]7f and Extended Data Fig. [252]9c),
repressing the SASP biomarkers IL-1α, IL-1β, IL-6 and CXCL8 in RAS OIS
cells (Fig. [253]7e). We thus conclude that the senescence phenotype is
intricately linked to pEtN homoeostasis.
Fig. 7. PCYT2 and pEtN modulation regulate the senescence metabolic
reprogramming.
[254]Fig. 7
[255]Open in a new tab
a, FC of pEtN:CDP-Etn ratio in WI38 fibroblasts infected with an
adenovirus driving the expression of a control shRNA (shCtrl) or an
shRNA targeting the PCYT2 mRNA (shPCYT2) for 7 days, relative to the
value of non-infected cells (Prolif.) b, Representative images (left)
and percentage (right) of SABG-positive WI38 fibroblasts treated as in
a. The percentage is also reported for non-infected proliferating
cells. Scale bars, 20 µm. c, mRNA levels of senescence markers scored
by RT–qPCR in WI38 fibroblasts treated as in a. n = 6 biologically
independent experiments. Data are presented as mean ± s.d. Indicated P
values were calculated using an unpaired two-sided Student’s t-test. d,
Representative images of DAPI and LipidTox staining of WI38 fibroblasts
infected with an adenovirus driving the expression of a control shRNA
(shCtrl) or an shRNA targeting the PCYT2 mRNA (shPCYT2) for 7 days. The
experiment was repeated independently three times with similar results.
e, Heat map of the indicated mRNA levels as measured by RT–qPCR in WI38
fibroblasts proliferating (Prolif.) or subjected to Ras induction and
infected with adenoviruses overexpressing GFP, PCYT2 or ETNPPL for 7
days (n = 3). P values (unpaired two-sided Student’s t-test) in gene
expression between RAS OIS + GFP and RAS OIS + PCYT2 or between RAS
OIS + GFP and RAS OIS + ETNPPL are indicated. f, FC of pEtN:CDP-Etn
ratio in WI38 fibroblasts treated as in e, normalized to the value of
non-infected cells (Prolif.). For a,b,f, bars represent the means of
three biological replicates ± s.d. Indicated P values were calculated
using an unpaired two-sided Student’s t-test.
[256]Source data
Extended Data Fig. 9. Effects of perturbating pEtN levels on metabolome of
senescent cells and upregulation of AKT activity in GK overexpressing cells.
[257]Extended Data Fig. 9
[258]Open in a new tab
A: Levels of the PCYT2 mRNAs normalized to those of RPS14 as measured
by RT–qPCR in WI38 fibroblasts proliferating or infected with an
adenovirus driving the expression of a control shRNA (shCtrl) or an
shRNA targeting the PCYT2 mRNA (shPCYT2) for 7 days. The reported
values are relative to those of proliferating cells. Bars represent the
means of 3 biological replicates +/- s.d. Indicated p values were
calculated using an unpaired two-sided Student’s t-test. B:
Representative heat map using hierarchical clustering showing
metabolites in WI38 fibroblasts proliferating (Prolif.) and
shControl-(shCtrl) or shPCYT2-expressing WI38 fibroblasts at day 7.
Data are expressed as row Z scores collected from three biologically
independent experiments per condition. C: Representative heat map using
hierarchical clustering showing metabolites in WI38 fibroblasts
proliferating (Prolif.) or undergoing RAS-OIS and infected with
GFP-PCYT2- or ETNPPL overexpressing adenoviruses for 7 days. Data are
expressed as row Z scores collected from three biologically independent
experiments per condition. D: Representative western blots showing the
levels of the indicated proteins in WI38 fibroblasts proliferating or
infected with GFP- or GK-overexpressing adenovirus for the indicated
times. The experiment was repeated independently 3 times.
[259]Source data
To decipher how senescence rewired PL metabolism, we revisited our
lipidomic analysis. Despite decreased Pcyt2 activity, the
phosphatidylethanolamine (PE) level in senescent cells was not altered
(Fig. [260]4d). A similar observation was recently made in Pcyt2^+/−
cells and could be explained by decreased PE degradation and increased
conversion of other PL to PE^[261]45. Notably, Pcyt2 knockdown
recapitulated the alterations of glycerolipid metabolism by senescence
inducers, as evidenced by the accumulation of G3P (Fig. [262]8a) and
lipid droplets (Fig. [263]7d). Similarly, the modulation of G3P levels
by GK or G3PP OE affected pEtN levels (Fig. [264]8b,c), indicating a
homoeostatic interconnection between G3P and pEtN in the cell. At the
molecular level, the increase in G3P and pEtN promoted CDKN2A/p16
accumulation, hypo-phosphorylation (activation) of RB and the
consequent repression of the pro-proliferative RB/E2F target gene
Cyclin A2 (CCNA2) (Fig. [265]8d,e), with no sign of ER stress. The
inhibitory effect of GK overexpression on RB phosphorylation was rapid,
already detectable 2 days after viral transduction, indicating a role
of the G3P–pEtN switch in the induction of senescence (Extended Data
Fig. [266]9d). Of note, p16 transcriptional upregulation was
accompanied by substantial downregulation of the Id1 transcription
factor and Polycomb Group complex 1 and 2 (PRC1 and PRC2) components
Bmi1, Ezh2 and SUZ12 (Fig. [267]8d,e) implicated in p16
repression^[268]46. GK rapidly activated Akt phosphorylation (Extended
Data Fig. [269]9d), a known negative regulator of PRCs^[270]47,[271]48.
Mechanistically, these data suggest that the G3P–pEtN switch remodels
the epigenetic and transcriptional landscape to allow p16
transcriptional activation.
Fig. 8. Phosphoethanolamine and G3P accumulation are interconnected and
regulate RB phosphorylation during senescence.
[272]Fig. 8
[273]Open in a new tab
a, FC of G3P levels in WI38 fibroblasts infected with an adenovirus
overexpressing a control shRNA (shCtrl) or an shRNA targeting the PCYT2
mRNA (shPCYT2) for 7 days, relative to the value of non-infected cells
(Prolif.). n = 3 biologically independent experiments. Data are
presented as mean ± s.d. Indicated P values were calculated using an
unpaired two-sided Student’s t-test. b, FC of G3P levels and
pEtN:CDP-Etn ratio in WI38 fibroblasts infected with GFP-OE or GK-OE
adenovirus for 7 days, relative to the value of non-infected cells
(Prolif.). n = 3 biologically independent experiments. Data are
presented as mean ± s.d. Indicated P values were calculated using an
unpaired two-sided Student’s t-test. c, FC of pEtN:CDP-Etn ratio in
WI38 fibroblasts infected with GFP-OE or G3PP-OE adenovirus for 7 days,
relative to the value of non-infected cells (Prolif.). n = 3
biologically independent experiments. Data are presented as mean ± s.d.
Indicated P values were calculated using an unpaired two-sided
Student’s t-test. d, Representative western blots showing indicated
protein levels in WI38 fibroblasts not infected (Prolif.) or infected
with GFP-OE or GK-OE adenoviruses for 4 or 7 days. Loading control
(actin) was migrated into the same gel than p16, CCNA2 and GK. The
experiment was repeated independently three times with similar results.
e, Representative western blots showing indicated protein levels in
WI38 fibroblasts not infected (Prolif.) or infected with an adenovirus
overexpressing a control shRNA (shCtrl) or an shRNA targeting the PCYT2
mRNA (shPCYT2) for 7 days. The arrowhead indicates the position of the
band corresponding to the PCYT2 protein. Loading control (actin) was
migrated into the same gel as CCNA2 and RB. The experiment was repeated
independently three times with similar results. f, Representation of
G3P and pEtN metabolic interconnections leading to TAG accumulation and
senescence.
[274]Source data
To extend these observations to pathophysiological conditions, we
measured GK expression in senescence-prone mouse models (Extended Data
Fig. [275]10). These included PIK3CA^Adipo-CreER mice that display
white adipose tissue (WAT) hypertrophy upon tamoxifen-induced
expression of constitutively active PIK3CA/AKT in WAT^[276]49 and
LSL-KrasG12D^Ptf1a-Cre transgenic mice, which develop spontaneous
pancreatic premalignant lesions containing senescent cells^[277]50.
PIK3CA mutant transgenic mice showed increased GK expression in their
WAT coinciding with upregulated senescence biomarkers p16, p21, IL-1α
and tumour necrosis factor α (Extended Data Fig. [278]10a,b).
Extended Data Fig. 10. GK upregulation in in-vivo models of senescence.
[279]Extended Data Fig. 10
[280]Open in a new tab
A: Levels of the indicated mRNAs normalized to those of Pinin as
measured by RT–qPCR in mouse adipose tissue not expressing (PIK3CAWT)
or expressing (PIK3CAAdipo-CreER) the constitutively active PI3KCA
mutant. The reported values are relative to those of the PIK3CAWT
genotype. Bars represent the means of n = 7 biological replicates (9
males and 5 females) +/- s.d. Indicated p values were calculated using
an unpaired two-sided Student’s t-test. B: Left panel: Representative
western blot showing the levels of the indicated proteins in mouse
adipose tissue not expressing (PIK3CAWT) or expressing
(PIK3CAAdipo-CreER) the constitutively active PI3KCA mutant. Right
panel: Levels of the GK protein normalized to those of α-tubulin as
measured on western blots on total protein extracts of mouse adipose
tissue not expressing (PIK3CAWT) or expressing (PIK3CAAdipo-CreER) the
constitutively active PI3KCA mutant. Values are normalized to those of
the PIK3CAWT sample. Bars represent the means of 3 biological
replicates +/- s.d (4 males and 2 females). Indicated p values were
calculated using an unpaired two-sided Student’s t-test. C:
Immunostaining with an anti-p21 and an anti-GK antibody on sections of
the pancreas of WT mice (male) and of mice overexpressing in the
pancreas the constitutively active G12D KRas-mutant (male). Asterisks
indicate Langerhans islets. Scale bars represent 20 µm. The experiment
was repeated independently twice with similar results.
[281]Source data
In LSL-KrasG12D^Ptf1a-Cre transgenic mice, GK and p21 senescence
biomarkers were detectable in the pancreatic intraepithelial neoplasia
(PanIN) of KrasG12D-expressing mice but not in control wild-type mice
(Extended Data Fig. [282]10c). While these data do not establish a
causal relationship between GK-OE and senescence in vivo, they indicate
that a GK-dependent switch may operate in tissues undergoing
senescence.
Discussion
In this study, we performed a multi-layered kinetic analysis combining
transcriptomics and metabolomics of human fibroblasts senescent cells
to reveal common metabolic adaptations and the underlying gene
expression mechanisms across various stresses that promote senescence.
We identified a universal signature of SAMS, pointing to the
accumulation of lactate versus pyruvate, α-KG versus succinate, G3P and
pEtN. We focused on G3P and pEtN, which led us to reveal two newly
observed mechanisms responsible for their accumulation in senescent
cells: the upregulation of GK levels and the post-translational
downregulation of Pcyt2, respectively. We found that G3P and pEtN are
not only biomarkers of the senescent state but are also potent
inducers. Promoting or scavenging G3P and pEtN accumulation by
modulating the expression of GK and G3PP and Pcyt2 and ETNPPL, is
necessary and sufficient to impact the senescent fate decision. G3P and
pEtN levels are homeostatically interdependent and coordinated,
suggesting that they represent a core hub node in the balance between
NLs and PLs (Fig. [283]8f).
Our identified metabolic biomarkers are associated with senescence
rather than the induction of cellular stress. Of note, exposing cells
to cellular stress in the presence of drugs blunting the senescence
programme is sufficient to curtail the rise in the level of these
biomarkers. The mTOR inhibitor rapamycin is a well-known
anti-senescence treatment in vitro^[284]24,[285]26, promoting longevity
in several experimental in vivo models^[286]51. Here, we report that
DMOG also has decisive effects on senescence and the SAMS. The
rationale for testing DMOG is the α-KG accumulation in senescent cells,
previously observed in cancer cells from KRAS-mutant mouse models of
pancreatic cancer upon restoring p53 expression^[287]23. DMOG acts as
an antagonist of α-KG on Fe(II)/α-KG-dependent dioxygenases^[288]27, a
large family of enzymes regulating cell fate through the control of
metabolism and epigenetics. DMOG is a potent inducer of HIF-dependent
transcription by inhibiting the Fe(II)/α-KG-dependent dioxygenases
involved in HIF degradation. Our transcriptomics analysis identified
major changes in the hypoxia response during senescence; however, at
this stage, we cannot rule out additional targets explaining the
effects of DMOG on senescence. Moreover, DMOG has been reported to have
HIF-independent effects on mitochondrial metabolism^[289]52. Future
studies should address whether the mechanism of action of DMOG requires
HIF and/or other cellular components.
One rationale for this study was that comparing metabolomics and
transcriptomics data across various senescent inducers would reveal key
steps for senescence initiation. For instance, lipid droplet
accumulation is a common feature observed in a wide range of senescence
conditions, both in vitro and in vivo; however, the routes leading to
lipid droplet accumulation may differ depending on the cellular
insults. Increased lipid uptake, de novo lipogenesis, decreased fatty
acid oxidation and lipid remodelling by lysosomal PL degradation all
trigger lipid droplet formation. Indeed, we identified distinct
acylcarnitine derivatives as molecules that lead to the highest
discrimination between CS inducers; however, their contribution has
been shown to vary in different experimental
models^[290]8,[291]12,[292]15. Oncogenic Ras and hypoxic cells mainly
rely on lipid uptake and fatty acid scavenging, whereas PI3K and Akt
actively turn on de novo lipid biosynthesis and mitochondrial mutations
somewhat impair FAO^[293]11,[294]53,[295]54. Thus, the modulation of de
novo lipogenesis by FAS, SCD1 and ACC activities may have different
outcomes when the driver of senescence is oncogenic Ras as opposed to
replicative or oxidative stresses^[296]11,[297]16. Here, we find that
the G3P and pEtN accumulation is invariably detected in all the
senescence settings that we tested. Moreover, their levels affect lipid
droplet formation, suggesting that they represent obligatory steps in
this phenotype. These observations can be extended to other systems and
cell types in which pEtN and G3P alterations were also
reported^[298]10,[299]15,[300]55. Considering this robust response, we
addressed the origins and functional consequences of pEtN and G3P
status, as discussed below.
G3P and pEtN are the products of the activation step of glycerol and
ethanolamine initiating TAG and PE synthesis. Glycerol is produced
intracellularly by lipolysis or via the newly discovered enzyme
G3PP^[301]40 and can also cross the membrane through aquaglyceroporins
channels, including AQP3, AQP7, AQP9 and AQP10 in mammals^[302]56. Etn
is a nutrient present in food sources from plants, where it is derived
from PE lipolysis or decarboxylation of serine^[303]42. Etn, like
choline, permeates the CTL1 and CTL2 membrane channels^[304]57. While
pEtN specifically enters the Kennedy pathway for PE biosynthesis, G3P
is involved in several metabolic pathways, such as lipid synthesis,
glycolysis, gluconeogenesis and the electron transport chain. Although
we cannot exclude the contribution of multiple pathways, our data
suggest that the increase of GK activity and TAG synthesis is a major
cause and consequence of G3P accumulation in senescent cells.
Several lines of evidence in the literature suggest that the modulation
of G3P and pEtN in vivo affects age-related diseases, including
metabolic syndromes. Thus, Pcyt2 expression negatively correlates with
obesity^[305]45. The heterozygous deletion of Pcyt2 in mice leads to
hepatic DAG and TAG accumulation, providing a model of non-alcoholic
steatohepatitis. Pcyt2 activity declines in aging muscles of mice and
humans, consistent with Pcyt2 deficiency causing muscle weakness and
aging^[306]58. Similarly, G3PP suppression increases lipid synthesis,
reduces FAO and lowers ATP levels in liver and pancreatic β
cells^[307]40,[308]59,[309]60. In Caenorhabditis elegans, three
phosphoglycolate phosphatase homologue (PGPH) enzymes have been
proposed as G3PP orthologues^[310]61. Their combined deletion increases
G3P levels without affecting other proposed PGPH substrates, such as
2-phosphoglycolate, 2-phospholactate and 4-phosphoerythronate. This
leads to increased fat deposition and lethality in hyperosmotic stress
conditions and high glucose exposure, in which the mutant worms cannot
produce glycerol and become hypersensitive to these stresses.
Conversely, OE of PGPH-2 triggers a mild increase in C. elegans
lifespan, accompanied by decreased fat content. Notably, the glycerol
channel aqp1 in C. elegans is implicated in the lifespan-shortening
effect of a glucose diet^[311]62. Although senescence has not been
characterized in C. elegans, some of the pathways regulating lifespan
in worms have been linked to a senescence phenotype in higher
organisms. In mammals, insulin represses the expression of
aquaglyceroporin channels and AQP7-deficient mice display obesity and
insulin resistance because their glycerol permeability is
affected^[312]63. Our study linking G3P and pEtN accumulation to the
senescence programme in human cells may explain this age-related
pathophysiology in vivo.
We propose that G3P and pEtN are central to the homoeostatic switches
orchestrating the senescence programme by modifying the balance between
TAG and PLs. The high level of coordination and interdependence between
these two metabolites is demonstrated by the findings that the GK and
G3PP treatments affecting G3P similarly influence pEtN levels, while
the Pcyt2 treatment affecting pEtN levels influences G3P levels. Our
lipidomic analyses show that the balance shifts toward NLs in senescent
cells compared to PLs^[313]15. In addition, both GK overexpression and
Pcyt2 downregulation lead to TAG accumulation. The impairment of Pcyt2
activity in senescent cells has mild effects on the composition of
membrane PLs, while promoting TAG accumulation. The latter is
consistent with the analysis of Pcyt2 heterozygous mutant mice,
displaying TAG accumulation with constant PE levels^[314]45,[315]64.
The reduced flux in the Kennedy pathway for PE biosynthesis is
compensated by PS decarboxylation and the reduced turnover of the
membrane phospholipids. Of note, the sole PL to be significantly
downregulated in senescent cells is PG, again pointing to the
interconnection with G3P metabolism.
The coordination between G3P and pEtN is also achieved by interacting
with the two master regulators of the senescence programme, p53 and
p16/RB. We find that p53 controls GK and Pcyt2 activities, leading to
G3P and pEtN accumulation. In turn, these two metabolites implement a
downstream senescent response characterized by sharp p16/RB changes in
the face of a relatively constant p53 activity. In addition, p53
upregulates GK messenger RNA levels, consistent with previous studies
in HepG2 cells expressing shRNA against p53 or treated with
Nutlin^[316]65. Notably, GK expression in HepG2 cells is accompanied by
the upregulation of AQP3 and AQP9 in glycerol uptake. The
transcriptional effect of p53 may be indirect, as chromatin
immunoprecipitation experiments failed to identify p53 response
elements in the promoter of these genes^[317]65. We also cannot exclude
the increased stability of the GK transcript in senescent cells. The
control of Pcyt2 activity by p53 is not transcriptional but
post-translational and is accompanied by the dephosphorylation of the
enzyme in senescent cells. Pcyt2 activity is regulated by
phosphorylation on several residues, including two putative PKC sites,
Ser-197 and Ser-205 (ref. ^[318]43). Mutation of both serine residues
to alanine decreases enzymatic activity, whereas phorbol ester
treatment upregulates Pcyt2 activity. Future studies should determine
by what mechanism p53 leads to Pcyt2 dephosphorylation, for example,
whether it involves kinase inhibition or phosphatase activation. Some
PKC isoforms are DAG-dependent, suggesting another possible link with
lipid homoeostasis.
Ectopically increasing G3P levels alone can elicit a senescence-like
response without additional stresses. Few metabolic adaptations have
such a potent effect. Electron transport chain inhibition, malate
dehydrogenase knockdown and inhibition of malic enzymes ME1/ME2 can
drive a senescence programme directly related to mitochondrial
activity^[319]13; however, GK and Pcyt2 are cytosolic enzymes mainly
controlling lipid synthesis in this setting. pEtN, G3P and the
metabolites in lipid droplet biosynthesis may impact mitochondrial
activity, though this is an indirect response to a cytosolic
modification. pEtN has been proposed to inhibit mitochondrial activity
through competition with succinate at complex II (or succinate
dehydrogenase) of the mitochondrial respiratory chain^[320]66. Lipid
droplets could also alter ER–mitochondrial contact sites, though we did
not detect signs of ER stress upon GK and Pcyt2 modulation. The pathway
linking G3P and pEtN to the transcriptional and epigenetic machinery
regulating senescence remains unknown.
The universal metabolic adaptations across various senescence inducers
described in our paper suggest new avenues of therapeutic
interventions. GK, Pcyt2 and G3PP have enzymatic activities that are
druggable. Meclizine has been reported as a Pcyt2 inhibitor^[321]67.
Thioglycerol, used in our study and
(+/-)-2,3-dihydroxypropyl-dichloroacetate act as GK inhibitors^[322]41.
Although their chemistry is not compatible with in vivo treatments,
they could serve to model further drug development. GK inhibition is
predicted to increase the level of intracellular glycerol, which is
less toxic than G3P. Their efflux through aquaglyceroporins would
reduce carbon sources for oxidation and lipogenesis, potentially
blunting senescence response, inflammatory cytokine production and
age-related disorders^[323]56.
Limitations of the study
Although we provide compelling evidence that preventing G3P and pEtN
accumulation exerts senomorphic effects in cells exposed to
senescence-inducing agents, we did not address whether reducing G3P and
pEtN levels in cells that are blatantly senescent would result in a
similar outcome. Our study does not address the mechanism underlying
the effects of TP53 on GK gene expression and PCYT2 phosphorylation
that we observe in senescent cells. Although suggested by our data, we
lack definitive evidence that p16 has a dominant role in the
establishment of senescence when we drive G3P accumulation. We
identified the pro-senescent role of G3P and pEtN. The direct target
remains to be established. Our in vivo analysis is preliminary and
limited to the observation of increased levels of GK protein in
senescent tissues. We did not determine whether those changes are
associated with an equivalent effect on G3P levels.
Methods
Cell culture
WI38 fibroblasts (ATCC; CCL-75) were cultured in DMEM GLUTAMAX,
high-glucose (Gibco) containing 10% fetal bovine serum (FBS) and 1×
PenStrep (Thermo Fisher) at 37 °C with a 5% atmospheric concentration
of O[2] and CO[2]. The medium was changed every 2 d. Cells were split
when they reached a confluency of 70–80%. Experiments were performed on
cells at a population-doubling level inferior to 45 divisions (except
for the RS model). WI38-ER:RASV12 fibroblasts were generated by
retroviral transduction as previously described^[324]20. RAS OIS was
induced by adding 400 nM 4-hydroxytamoxifen (4OHT) to the culture
medium. The doxycycline-inducible oncogenic BRAFV600E retroviral
construct was a gift from C. Mann (CEA, Gif-sur-Yvette, France). RAF
OIS was induced with 100 ng ml^−1 doxycycline. DDIS was triggered by
etoposide treatment at a concentration of 20 µM for 2 d. Cells were
washed and incubated with fresh medium without drug. RS was obtained
through proliferative exhaustion. For the induction of quiescence, WI38
fibroblasts were cultured in DMEM containing 0.2% FBS. Primary human
myoblasts (SkMC) were isolated from a skeletal muscle biopsy of a
healthy donor (PromoCell, C-12530, lot 414Z025.11) and were purified
with an immunomagnetic sorting system using CD56/NCAM magnetic beads
(Miltenyi Biotec, 130-050-401) following the manufacturer’s
specifications. The purified CD56-positive myoblasts were seeded in
dishes coated with type I collagen (Sigma-Aldrich, C8919) and cultured
in the proliferation medium (DMEM high glucose (Sigma, D6429), 20% FBS
(Life Technologies, 10270106), 50 µg ml^−1 gentamicin (Life
Technologies, 15750037), 0.5% Ultroser G (PALL, 15950-017)) at 37 °C
with 5% CO[2]. All experiments were conducted at Cumulative Population
Doubling (CPD)-11 and CPD-29 to avoid replicative senescence and
myoblasts were passaged at a cell confluency not exceeding 50% to avoid
myogenic differentiation. Retroviral infections were performed as
outlined for WI38 fibroblasts. For the indicated treatments of
fibroblasts and myoblasts, cells were collected and processed at the
indicated time points.
Reagents for cell culture
We used 4OHT (H7904, Sigma) and doxycycline (D3447, Sigma) as
described^[325]20. Etoposide (E1383, Sigma) was dissolved in 50 mM
DMSO, aliquoted and stored at −20 °C. Before use, etoposide was added
to fresh medium at a final concentration of 20 µM. Rapamycin (1292,
Tocris) was dissolved in 100% ethanol at a 27.3 mM concentration,
aliquoted and stored at −20 °C. Before use rapamycin was added to fresh
medium from an intermediate 27.3 μM solution at a final concentration
of 20 nM. DMOG (D3695, Sigma) was dissolved in water at a 150 mM
concentration, aliquoted and stored at −20 °C. Before use, DMOG was
added to fresh medium at a final concentration of 1 mM. 1-Thioglycerol
(M1753, Sigma) was stored at 4 °C. Before use, 1-thioglycerol was added
to fresh medium at a final concentration of 1 mM. Nutlin-3 (S1061,
Selleckchem) was dissolved in DMSO at a 10 mM concentration, aliquoted
and stored at −80 °C. Before use, Nutlin-3 was added to fresh medium at
a final concentration of 10 μM.
Viral transduction and transfection of siRNAs
Adenoviruses were transduced in cells incubated in an FBS-deprived
medium for 3 h. Subsequently, cells were gently washed and incubated
with fresh complete medium containing 4OHT, where required, to trigger
ER:RASV12 induction. Supplementary Table [326]12 lists the adenoviruses
used. For the transfection of siRNAs, cells plated in 6-cm dishes were
transfected with siRNAs at a final concentration of 25 nM using the
Transit-X2 Dynamic Delivery System (MIR6003, Mirus) according to the
manufacturer’s instructions.
Targeted LC–MS metabolomics analyses
For metabolomic analysis, the extraction solution was composed of 50%
methanol, 30% acetonitrile (ACN) and 20% water. The volume of the
extraction solution was adjusted to urea volume (1 ml per 1 × 10^6
cells). After adding the extraction solution, samples were vortexed for
5 min at 4 °C and centrifuged at 16,000g for 15 min at 4 °C. The
supernatants were collected and stored at −80 °C until analysis. LC–MS
analyses were conducted on a QExactive Plus Orbitrap mass spectrometer
equipped with an Ion Max source and a HESI II probe coupled to a Dionex
UltiMate 3000 uHPLC system (Thermo). External mass calibration was
performed using a standard calibration mixture every 7 d, as
recommended by the manufacturer. The 5-μl samples were injected into a
ZICpHILIC column (150 × 2.1 mm; internal diameter 5 μm) with a guard
column (20 × 2.1 mm; internal diameter 5 μm) (Millipore) for LC
separation. Buffer A was 20 mM ammonium carbonate and 0.1% ammonium
hydroxide (pH 9.2) and buffer B was ACN. The chromatographic gradient
was run at a flow rate of 0.200 μl min^−1 as follows: 0–20 min, linear
gradient from 80% to 20% of buffer B; 20–20.5 min, linear gradient from
20% to 80% of buffer B; and 20.5–28 min, 80% buffer B. The mass
spectrometer was operated in full-scan, polarity-switching mode with
the spray voltage set to 2.5 kV and the heated capillary held at
320 °C. The sheath gas flow was set to 20 units, the auxiliary gas flow
to 5 units and the sweep gas flow to 0 units. The metabolites were
detected across a mass range of 75–1,000 m/z at a resolution of 35,000
(at 200 m/z) with the automatic gain control target at 106 and the
maximum injection time at 250 ms. Lock masses were used to ensure mass
accuracy below 5 ppm. Data were acquired with Thermo Xcalibur software
(Thermo). The peak areas of metabolites were determined using Thermo
TraceFinder software (Thermo), identified by the exact mass of each
singly charged ion and by the known retention time on the HPLC column.
Targeted metabolomics analyses were focused on the small polar
compounds in central carbon metabolism. Established methods for sample
extraction and LC–MS analyses using a pHILIC HPLC column for polar
metabolite separation were used^[327]68. Additional details are in the
supplementary files.
Lipidomics
PL, TAG and DAG species in cells were analysed by Nano-Electrospray
Ionization Tandem MS (Nano-ESI-MS/MS) with direct infusion of the lipid
extract (Shotgun Lipidomics). A total of 5–10 × 10^6 cells were
homogenized in 500 µl of Milli-Q water using the Precellys 24
Homogenisator (Peqlab) at 6,500 r.p.m. for 30 s. The protein content of
the homogenate was determined using bicinchoninic acid. Then, 35 µl
(for PL analysis), 100 µl (for TAG analysis) or 20 µl (for DAG
analysis) homogenate were diluted to 500 µl with Milli-Q water. For PL
analysis, 1.875 ml methanol/chloroform 2:1 (v/v) and internal standards
(125 pmol PC 17:0–20:4, 132 pmol PE 17:0–20:4, 118 pmol PI 17:0–20:4,
131 pmol PS 17:0–20:4 and 62 pmol PG 17:0–20:4; Avanti Polar Lipids)
were added. For the analysis of TAG and DAG species, 1.875 ml
chloroform/methanol/37% hydrochloric acid 5:10:0.15 (v/v/v) and 20 µl
d5-TG Internal Standard Mixture I (for TAGs) or 30 µl each of d5-DG
Internal Standard Mixtures I and II (for DAGs) (Avanti Polar Lipids)
were used. Conditions of lipid extraction and Nano-ESI-MS/MS analysis
have been previously described^[328]69. PC analysis was performed by
scanning for precursors of m/z 184 Da at a collision energy (CE) of
35 eV. PE, PI, PS and PG measurements were conducted by scanning for
neutral losses of m/z 141, 277, 185 and 189 Da with a CE of 25 eV. The
value for the declustering potential was 100 V (ref. ^[329]70).
Scanning was performed in a mass range of m/z 650–900 Da. TAG and DAG
species were detected by scanning for the neutral losses of the
ammonium adducts of distinct fatty acids: 271 (16:1), 273 (16:0), 295
(18:3), 297 (18:2), 299 (18:1), 301 (18:0), 321 (20:4) and 345 Da
(22:6). For the analysis of TAG species, a mass range of m/z
750–1,100 Da was scanned with a CE of 40 eV, for DAG species the mass
range was m/z 500–750 Da and the CE 25 eV (ref. ^[330]70). All scans
were conducted in a positive-ion mode at a scan rate of 200 Da s^−1.
Mass spectra were processed by LipidView v.1.2 Software (SCIEX) to
identify and quantify lipids. Endogenous lipid species were quantified
by referring their peak areas to those of the internal standards. The
calculated lipid amounts were normalized to the protein content of the
cell homogenate. Additional details are in the supplementary files.
Isotope labelling of Kennedy and glycerol pathway metabolites
To assess ethanolamine and glycerol uptake, we incubated cells with
100 µg ml^−1 isotope-labelled Etn (MW + 2) (606294, Sigma) or 1.05 mM
labelled glycerol (MW + 3) (489476, Sigma) for 1 h. For pulse–chase
experiments, cells were washed with PBS twice after the pulse, then
incubated with fresh medium containing an excess of non-labelled Etn
(1 mM). Preparation of the extracts and measurement of labelled
metabolite levels were performed as described above for ‘Targeted LC–MS
metabolomics analyses’.
Mitochondrial glycerol-3-phosphate dehydrogenase activity
The mitochondrial glycerol-3-phosphate dehydrogenase (EC 1.1.5.3)
activity was estimated through the activity of G3P cytochrome c
reductase spectrophotometrically measured on cell pellets at 37 °C
(Cary 60 double wavelength spectrophotometer, Varian) according to
previous work^[331]71. Levels of protein were estimated by the Bradford
test.
RNA purification reverse transcription and RT–qPCR
Total RNA was isolated using Trizol (QIAGEN) following the
manufacturer’s instructions. RNA concentration was determined with a
NanoDrop 2000 apparatus (Thermo Fisher Scientific). Reverse
transcription was carried out on 100–150 ng of RNA using a SuperScript
II RT kit (Invitrogen). Then, 4 μl 1:50 dilutions of the cDNAs were
used in RT–qPCR reactions containing a SYBR Green PCR Master Mix
(Bio-Rad). The reactions were carried on in a Stratagene MX3005P
apparatus (Agilent Technologies). The thermal profile setup was 15 min
at 95 °C followed by 40 cycles of alternating steps of 15 s at 95 °C
and 30 s at 60 °C. Melt curve analysis was performed at the end of each
run. The relative quantification of gene expression was performed using
the 2^-ΔΔCT method, normalizing with RPS14 as a housekeeping
transcript. Supplementary Table [332]12 lists the oligonucleotides
used.
RNA microarrays
Total RNA was purified using the QIAGEN RNeasy Plus kit according to
the manufacturer’s instructions. Then, 100 ng RNA per sample were
analysed using Affymetrix Human Transcriptome Arrays v.2.0, according
to the manufacturer’s instructions.
Cellular staining
SABG activity was assessed using the SABG Staining kit (Cell Signaling
Technology) following the manufacturer’s instructions. Images were
taken using an optical microscope and analysed using ImageJ software.
Staining of lipid droplets was performed as previously
described^[333]72. In brief, cells seeded in 24-well plates were fixed
with 4% PFA for 15 min, then permeabilized and blocked for 45 min in
200 mM glycine, 3% BSA, 0.01% saponin and 1× PBS. After washing with
PBS 1×, cells were incubated for 30 min with LipidTox Red (Thermo
Fisher) diluted 1:200 in 0.1% BSA, 0.01% saponin and 1× PBS. All steps
were carried out at room temperature. The coverslips were mounted using
a mounting medium containing DAPI. Cells were imaged using a Spinning
Disk microscope (Zeiss, Zen software) and analysed using ImageJ
software.
Immunohistochemistry
Mice tissues were fixed in 4% PFA, embedded in paraffin and sectioned
at 4 μm. Sections were deparaffinized and hydrated before being boiled
for 10 min in citrate buffer (pH6). Sections were blocked for 1 h in
0.1% Triton-X100, 0.1% Tween-20, 3% BSA and 5% goat serum in
Tris-buffered saline (TBS) and then incubated with primary antibody
overnight at 4 °C. Next, sections were incubated with biotinylated
secondary antibodies and signal detected with the Vectastain Elite ABC
kit (PK-6100; Vector Laboratories) and DAB chromogen system (DAKO).
Pictures were acquired using a Nikon Eclipse Ti-S microscope (Nikon)
using a ×10 and ×20 magnification.
Immunoblotting
Cells were lysed in ice-cold lysis buffer (50 mM Tris-Cl, pH 7.4,
138 mM NaCl, 2.7 mM KCl, 5 mM EDTA, 20 mM NaF, 5% glycerol and 1% NP40)
supplemented with protease and phosphatase inhibitor mixes (Roche).
Protein concentrations were determined using the Bradford reagent
(Bio-Rad). Equal amounts of extracts were resolved by 8, 10 or 12%
SDS–PAGE and electro-transferred onto a polyvinylidene difluoride
(PVDF) membrane (Amersham Biosciences). The preparation of 6% Phos-tag
gels, migration and transfer were performed as described^[334]73. Blots
were blocked in 1× TBS supplemented with 5% milk and subsequently
incubated overnight at 4 °C in 3% BSA in 1× TBS supplemented with the
primary antibodies diluted 1:1,000. After washing in TBS-Tween 0.1%,
blots were incubated for 1 h at room temperature in TBS-milk 5%
supplemented with HRP-conjugated secondary antibodies (Cell Signaling
Technology; anti-mouse, 7076S; anti-rabbit, 7074S) diluted 1:5,000.
After washing in TBS-Tween 0.1%, blots were developed with the
Immobilon western chemiluminescence HRP substrate (Millipore). Images
were acquired with a ChemiDoc Imager from Bio-Rad. Supplementary Table
[335]12 lists the antibodies used.
Mice
The generation and genotyping of PIK3CA^Adipo-CreER mice and
LSL-KrasG12D^Ptf1a-Cre transgenic mice are described
elsewhere^[336]49,[337]50. The experiments were approved by the
Direction Départementale des Services Véterinaires (Prefecture de
Police, Paris; authorization no. 75-1313 and APAFIS, 34979). Mice were
housed in a 12-h light–dark cycle and fed a standard chow diet (2018
Teklad Global, 18% protein rodent diets; 3.1 kcal g^−1; Envigo). At the
age of 6 weeks, PIK3CA^WT and PIK3CA^Adipo-CreER mice received a daily
dose of tamoxifen (40 mg kg^−1) for 5 d and were killed 6 weeks later.
LSL-KrasG12D^Ptf1a-Cre mice were killed at 2 months of age together
with the control mice.
Analysis of metabolomics data
The data matrices were log-transformed for each batch and an analysis
of variance (ANOVA) was performed to determine the statistical
significance of metabolite differential accumulation. P values were
corrected using the false discovery rate (FDR) approach and metabolites
with a q value <0.05 were considered differentially accumulated. In
total, 137 molecules were identified as differentially accumulated, at
least in one sample and one experiment. Compound time profiles for each
dataset were clustered independently using the WGCNA package^[338]74,
with each sample being represented by the median of its replicates. The
‘soft threshold’ parameter was determined for each batch separately,
with the choice of the lowest value leading to a high scale-free
topology fit by applying the elbow method, as suggested by the tool
authors. We set the parameters ‘minimum cluster size’, ‘deepSplit’ and
‘correlation threshold for cluster merging’ to 3, 3 and 0.60,
respectively. We inspected signalling pathways enriched in each WGCNA
module for each dataset by performing a hypergeometric test with
pathways stored in the KEGG database^[339]75. We normalized the time
profiles for the 46 metabolites identified in all batches with the
ComBat tool^[340]76, using the initial, uninduced samples from each
batch to estimate inter-batch effects. We performed an integrated PCA
with the R package factoextra. We identified the specificities of the
metabolic response elicited by distinct stressors by performing a
sparse partial least squares discriminant analysis (sPLS-DA) using the
mixOmics package^[341]77. We selected the 101 metabolites
differentially accumulated in at least one time point for the CS
fibroblast datasets (RAS OIS, RAF OIS, DDIS and RS) and determined the
optimal number of components and features using the function perf. We
integrated the RAS OIS metabolic response in fibroblasts and myoblasts
by computing the overlap between each module identified for each
dataset and by generating river plots connecting these modules with the
R package networkD3
([342]https://cran.r-project.org/web/packages/networkD3/index.html).
Analysis of transcriptomics data
We downloaded the raw Affymetrix HTA v.2.0 transcriptome data for the
RAF-induced senescence and quiescence experiments from the Gene
Expression Omnibus (GEO) database (BioProject [343]PRJNA439263,
accession codes [344]GSE143248 and [345]GSE112084 (ref. ^[346]20)).
Oncogenic RAS, RAF, DDIS and RS transcriptomes were measured as
described above. For each dataset, we normalized expression levels
using the robust multichip average tool provided by the oligonucleotide
R package^[347]77 and performed a surrogate variable analysis with the
sva^[348]78 and limma^[349]79 R packages.
We eliminated internal Affymetrix control probes and annotated the
remaining probes using the hta20sttranscriptcluster.db R package. We
removed lowly expressed probes, specifically the bottom 40% of genes,
considering all samples in a dataset. We applied ANOVA FDR and selected
genes with a q value lower than 0.05 and 1.5 × log[2]FC for each
dataset.
The hierarchical clustering performed on the transcriptome data is
similar to the one applied to the metabolome. We clustered genes from
each experiment with WGCNA^[350]74, using their replicates median value
for each sample. As mentioned above, we individually determined the
‘soft threshold’ for each dataset. The parameters ‘minimum cluster
size’ and ‘deepSplit’ were set to 100 and 3, respectively. The
‘correlation threshold for cluster merging’ parameter was optimized
independently for each inducer and set to 0.7 for RAS OIS
(fibroblasts), 0.75 for RAS OIS (myoblasts), 0.75 for RAF OIS, 0.8 for
DDIS, 0.8 FOR RS and 0.9 for quiescence. Furthermore, we integrated the
fibroblast and myoblast RAS OIS response by computing the overlap
between each transcriptional cluster and by generating river plots
using the networkD3 R package, in a similar procedure as that performed
for the metabolome.
We investigated signalling pathways enriched for differential genes in
each dataset by performing an over-representation analysis using the R
package v.7.5.1.9001 Molecular Signature Database (MSigDB)
([351]https://igordot.github.io/msigdbr/) and clusterProfiler^[352]80,
combined with the MSigDB hallmark gene sets^[353]81,[354]82. For each
dataset, we analysed each coexpression module identified by WGCNA
separately. As described above, we also used river plots produced by
the R package networkD3 to integrate the gene expression dynamics from
both experiments. To assess DMOG and rapamycin-induced changes in SASP
gene expression of RAS OIS or DDIS cells, we ranked differentially
expressed genes according to their fold change, comparing the samples
at the end of each time course (day 7 for RAS OIS + DMOG and day 14 for
DDIS + rapamycin) and performed GSEA using a comprehensive, published
SASP Atlas as a ref. ^[355]22.
Batch-correction methods benchmark
We evaluated five batch-correction (BC) methods reported in the
literature to integrate the data from different senescence inducers.
Namely, the methods were quantile normalization, implemented by the
oligonucleotide R package^[356]77, a BC technique using the quality
control samples from each batch as a reference, as described
elsewhere^[357]83, a third approach based on the average of all samples
in a given batch as a reference for normalization^[358]84, a strategy
using samples corresponding to biological replicates in each batch as
reference for BC (cells before CS or quiescence induction) and the
ComBat tool, which infers the parameters of a linear model for BC using
a Bayesian approach^[359]76. The approaches consisting of using a set
of samples average as the normalization reference follow the general
form given by equation (1).
[MATH: Xp,s,b′
=Xp,s,bR
pC
p,s,bwithRp=average∀i,
j(Xp,i,
j) :MATH]
1
Where X′[p,s,b] and X[p,s,b] are respectively the normalized and raw
intensity of peak p at sample s in batch b; R[p] is a scaling factor
computed by the average of all detected values for a peak p in all
samples in all batches and C[p,s,b] is the correction factor computed
on the set of reference samples. The following equations give its
computation for each set of reference samples.
[MATH: Qualitycontrolsamples:Cp,s,b=averagei∈Q
C(b)
(Xp,i,b)
Uninducedsamples:Cp,s,b=averagei∈Q
C=D00(b)(Xp,i,b)
Allbatchsamples=Cp,s,p=average∀i∈
b(X<
/mi>p,i,b)
:MATH]
2
We compared those methods based on the values obtained by the
computation of three metrics: relative s.d. (r.s.d.), repeatability and
the Bhattacharyya distance.
The r.s.d. consists of the ratio between the s.d. (σ) and the average
intensity values (µ) measured for each peak p. This value is computed
for each sample s over all batches as determined by the following
equation^[360]84.
[MATH: r.s.d.=
σp,sμ
p,s :MATH]
3
Repeatability measures the fraction of the variance between replicates
of the same sample s over all batches^[361]85. Its computation is
performed for each measured peak p, dividing the variance between the
averages of all replicates for sample s by the variance of the
intensity observed in all replicates within the same sample, as shown
in equation ([362]5). High repeatability is attained by samples
sparsely distributed, with replicates densely clustered. As the
variance for replicates within a sample approaches (or surpasses) the
variance between samples, this quantity decreases.
[MATH: Repeatability=
σbetween;p,s2σbetween;p,s2
+σwithin;p,s2≈σbiol;p,s2σtotal;p,s2 :MATH]
4
The Bhattacharyya distance (DB) is an extension of the Malahanobis
distance. The Malahanobis distance measures the distance between two
sets of points, normalized by their covariance. Therefore, tighter
clusters will lead to a higher Malahanobis distance for the same
distance between their centre of mass. The DB extends this concept by
introducing a factor accounting for a distinct distribution in both
sets. This metric was calculated using the fpc R package and is given
by^[363]85:
[MATH: DB=18(μ1;s−μ2;s)T∑(−1
)(μ1;s−μ2;s)+<
/mo>12lndet∑sdet∑1;sdet∑2;s :MATH]
5
Where µ[b;s] corresponds to the centre of mass of sample s for batch b,
Σ[b;s] is the covariance matrix for samples replicates in batch b and
Σ[s] is the covariance matrix for sample s in all batches.
Integration of transcriptomics and metabolomics data
Aiming to identify potential non-linear molecular interactions, we
computed the Spearman correlation for each gene–metabolite pair in each
dataset in an approach inspired by previous work^[364]86. We calculated
the overlap of high correlations (absolute value higher than 0.5) in
all datasets with the R package Vennerable. We visualized these
overlapping correlations to build gene–metabolite networks with the R
packages ComplexHeatmap^[365]87, CyREST^[366]88, RCy3 (ref. ^[367]89)
and Cytoscape software^[368]90. Gene Ontology analysis of
G3P-correlating genes was performed on targets correlating positively
or negatively with G3P in at least three out of four senescence
inducers (quiescence condition excluded).
The analysis was performed on ShinyGO, using the hallmark MSigDB.
Curated Reactome analysis was performed on the G3P-correlating gene
either positively or negatively. The analysis was performed on ShinyGO,
using the Curated Reactome database.
Statistics
Quantitative data in graphs are presented as the mean ± s.d. unless
indicated otherwise in the figure legends. Statistical tests used in
this study include unpaired two-sided Student’s t-test and one-way
ANOVA as indicated in the figure legends. Significant differences are
reported as P values in the figure legends and the exact values are
indicated where appropriate. No statistical method was used to
predetermine the sample size, but our sample sizes are similar to those
reported in a previous publication^[369]20. Data derived from
time-series Affymetrix microarrays were highly reproducible. All
transcriptomics were performed in biological duplicate (WI38 Ras ± DMOG
and WI38 etoposide ± rapamycin). RT–qPCR on adenovirus-infected cells
was performed in biological triplicate. Metabolomics was performed on
biological triplicates for each time point and condition and lipidomics
were performed on a minimum of four biological replicates for each
condition. Data distribution was assumed to be normal but this was not
formally tested. Biological materials (cells and mice) were randomized
before experiments. Data collection and analysis were not performed
blind to the conditions of the experiments. Outliers were identified
and excluded by the ROUT method (default setting) on GraphPad Prism.
Reporting summary
Further information on research design is available in the [370]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[371]Supplementary Information^ (294.9KB, pdf)
Supplementary methods.
[372]Reporting Summary^ (4MB, pdf)
[373]Supplementary Table 1^ (35.4MB, xlsx)
Microarray gene expression data in RAS OIS, RAF OIS, DDIS, RS and
quiescence.
[374]Supplementary Table 2^ (30.2KB, xlsx)
Pathway enrichment analysis of transcriptomes of RAS OIS, RAF OIS,
DDIS, RS and quiescence.
[375]Supplementary Table 3^ (139.9KB, xlsx)
Metabolite levels in RAS OIS, RAF OIS, DDIS, RS and quiescence.
[376]Supplementary Table 4^ (28.9KB, xlsx)
Pathway enrichment analysis of metabolomes of RAS OIS, RAF OIS, DDIS,
RS and quiescence.
[377]Supplementary Table 5^ (10.7MB, xlsx)
Affymetrix microarray gene expression data in RAS OIS ± DMOG and
DDIS ± rapamycin.
[378]Supplementary Table 6^ (19.3KB, xlsx)
Pathway enrichment analysis of transcriptomes of RAS OIS ± DMOG and
DDIS ± rapamycin.
[379]Supplementary Table 7^ (20.2KB, xlsx)
List of genes overlapping between RAS OIS + DMOG and DDIS + rapamycin
as shown in the river plot (related to Extended Data Fig. [380]5e).
[381]Supplementary Table 8^ (7.1KB, xlsx)
GSEA for the hallmark SASP for the RAS OIS ± DMOG and DDIS ± rapamycin.
[382]Supplementary Table 9^ (21.1MB, xlsx)
Gene–metabolite correlations in RAS OIS, RAF OIS, DDIS, RS and
quiescence.
[383]Supplementary Table 10^ (32.1KB, xlsx)
Network metrics for overlapping gene–metabolite correlations in RAS
OIS, RAF OIS, DDIS, RS and quiescence.
[384]Supplementary Table 11^ (8.5KB, xlsx)
Gene Ontology analysis of genes correlating positively or negatively
with G3P.
[385]Supplementary Table 12^ (12.7KB, xlsx)
Lists of vectors, antibodies, primers and siRNA used in this study.
[386]Supplementary data 1^ (90.4KB, xlsx)
Supplementary lipidomics raw data.
[387]Supplementary data 2^ (101.3KB, xlsx)
Supplementary metabolomics raw data.
Source data
[388]Source Data Fig. 2^ (25.6KB, xlsx)
Statistical source data.
[389]Source Data Fig. 3^ (20.8KB, xlsx)
Statistical source data.
[390]Source Data Fig. 4^ (18KB, xlsx)
Statistical source data.
[391]Source Data Fig. 4^ (217.7KB, pdf)
Unprocessed western blots.
[392]Source Data Fig. 5^ (24.6KB, xlsx)
Statistical source data.
[393]Source Data Fig. 6^ (15.9KB, xlsx)
Statistical source data.
[394]Source Data Fig. 6^ (593.3KB, pdf)
Unprocessed western blots.
[395]Source Data Fig. 7^ (21.2KB, xlsx)
Statistical source data.
[396]Source Data Fig. 8^ (13.8KB, xlsx)
Statistical source data.
[397]Source Data Fig. 8^ (271.1KB, pdf)
Unprocessed western blots.
[398]Source Data Extended Data Fig. 3^ (38.9KB, xlsx)
Statistical source data.
[399]Source Data Extended Data Fig. 4^ (25.4KB, xlsx)
Statistical source data.
[400]Source Data Extended Data Fig. 6^ (20.2KB, xlsx)
Statistical source data.
[401]Source Data Extended Data Fig. 6^ (154.9KB, pdf)
Unprocessed western blots.
[402]Source Data Extended Data Fig. 7^ (11.8KB, xlsx)
Statistical source data.
[403]Source Data Extended Data Fig. 7^ (180.3KB, pdf)
Unprocessed western blots.
[404]Source Data Extended Data Fig. 8^ (17.1KB, xlsx)
Statistical source data.
[405]Source Data Extended Data Fig. 8^ (100.4KB, pdf)
Unprocessed western blots.
[406]Source Data Extended Data Fig. 9^ (9.5KB, xlsx)
Statistical source data.
[407]Source Data Extended Data Fig. 9^ (126.2KB, pdf)
Unprocessed western blots.
[408]Source Data Extended Data Fig. 10^ (11.7KB, xlsx)
Statistical source data.
[409]Source Data Extended Data Fig. 10^ (78.2KB, pdf)
Unprocessed western blots.
Acknowledgements