Abstract
The systemic metabolic shifts that occur during aging and the local
metabolic alterations of a tumor, its stroma and their communication
cooperate to establish a unique tumor microenvironment (TME) fostering
cancer progression. Here, we show that methylmalonic acid (MMA), an
aging-increased oncometabolite also produced by aggressive cancer
cells, activates fibroblasts in the TME, which reciprocally secrete
IL-6 loaded extracellular vesicles (EVs) that drive cancer progression,
drug resistance and metastasis. The cancer-associated fibroblast
(CAF)-released EV cargo is modified as a result of reactive oxygen
species (ROS) generation and activation of the canonical and
noncanonical TGFβ signaling pathways. EV-associated IL-6 functions as a
stroma-tumor messenger, activating the JAK/STAT3 and TGFβ signaling
pathways in tumor cells and promoting pro-aggressive behaviors. Our
findings define the role of MMA in CAF activation to drive metastatic
reprogramming, unveiling potential therapeutic avenues to target MMA at
the nexus of aging, the tumor microenvironment and metastasis.
Subject terms: Cancer microenvironment, Cancer metabolism, Metastasis
__________________________________________________________________
Methylmalonic acid (MMA) is increased in aging as well as produced by
advanced tumors, and can drive pro-aggressive changes in these tumor
cells. Here, the authors show that MMA can also act on fibroblasts in
the tumor microenvironment, recruiting and activating them to further
support tumor progression.
Introduction
Metastasis underlies mortality in the majority of solid-cancer tumors,
including lung cancer, the leading cause of cancer death, and melanoma,
in which the 5-year survival rate is <15% in patients with metastatic
disease^[58]1. As a problem of aging, metastasis is the number one
cause of death in people 60–79 years old and represents a direct avenue
to steer interventions for improving cancer survival and extending
overall lifespans^[59]2. Toward this end, much investigative effort
continues to focus on mutational, epigenetic, and metabolomic changes
within the cancer cell that abet the metastatic process. In this arena,
we discovered that methylmalonic acid (MMA), a byproduct of propionate
metabolism, is increased in the serum of elderly people and contributes
to acquisition of aggressive properties in tumor cells, uncovering a
systemic cause for the link between old age and negative cancer
outcomes. In addition to the age-dependent increase in circulatory MMA,
we have also demonstrated that tumor cells dysregulate propionate
metabolism in order to increase local MMA accumulation, driving cancer
progression in an autocrine manner^[60]3.
Considering that MMA is increased both in the aging body and locally
through tumor production, the next question was how these high local
concentrations of MMA could function in a paracrine fashion. The
influence of the tumor microenvironment (TME) on metastatic progression
is inextricable from the equation. Within the heterogeneous and dynamic
TME network, the exchange of secreted factors such as hormones,
enzymes, growth factors, cytokines and metabolites all facilitate a
cooperative tumorigenic and metastatic process between tumor and
stroma^[61]4. Cancer-associated fibroblasts (CAFs) represent critical
players in the formation of a favorable TME for cancer progression. In
addition to extracellular matrix (ECM) deposition and remodeling, CAFs
secrete cytokines, growth factors, and metabolites that influence the
behavior and function of tumor cells as well as other stromal
components. The concentrations, combinations and efficacy of these
secreted molecules can be specifically regulated by their delivery
through extracellular vesicles (EVs), although the mechanisms
controlling these parameters are not fully understood^[62]5. In tumor
cells, CAF-secreted messengers influence tumor growth, metastasis and
drug resistance through multiple underlying processes, including
inhibiting apoptosis pathways, induction of stemness programs, or
epithelial-to-mesenchymal transition (EMT)^[63]6,[64]7. Many of the
traits that epithelial-like tumor cells acquire through EMT enhance
successful remodeling of their surrounding ECM, support invasion
through tissue, and promote intravasation across the endothelial
barrier into the bloodstream. This is supported by histopathological
studies showing that cells at the invasive front of tumors exhibit an
EMT phenotype^[65]8,[66]9.
In the present study, we show that MMA, increased in the TME by aging
as well as by tumor production, activates stromal fibroblasts to CAFs
and induces a secretory phenotype. In turn, EVs secreted by MMA-induced
CAFs, harboring IL-6 and other factors, promote an EMT in tumor cells,
fostering the acquisition of aggressive traits including drug
resistance and increased metastatic formation.
Results
MMA secreted from tumor cells activates fibroblasts in the tumor
microenvironment
Aberrations in the enzymes downstream of methylmalonyl-CoA in the
propionate metabolism pathway, namely methylmalonyl-CoA mutase (MUT),
methylmalonyl-CoA epimerase (MCEE), methylmalonic aciduria type A
protein (MMAA), or cob(I)yrinic acid a,c-diamide adenosyl-transferase
(MMAB) result in pathogenic systemic MMA accumulation in methylmalonic
acidemias^[67]10–[68]13, and drive cancer drug resistance and
metastasis through increased MMA accumulation in vitro and in
vivo^[69]3 (Fig. [70]1a). We profiled the transcripts of these
metabolic enzymes in individual cells obtained from resected human lung
cancer primary tumors and metastases, and found that tumor cells with
reduced expression of these genes were enriched in mesenchymal
subpopulations (Fig. [71]1a, [72]b). Given this, and our previous
findings that metastatic inducers drive MMA production and
pro-aggressive effects on tumors through dysregulation of propionate
metabolism, we wondered if tumor-produced MMA might also act on other
cell types in the TME^[73]3. Fibroblasts comprise the major component
of the TME, and in some solid tumors even outnumber malignant
cells^[74]14. We knocked down MUT in A549 lung carcinoma and A375
melanoma cells to simulate MMA accumulation by altered propionate
metabolism during early steps of metastasis, and co-cultured these
cells with MRC5 lung and BJ dermal fibroblasts, respectively
(Fig. [75]1c–e). Five days of co-culture markedly increased CAF markers
in the fibroblasts, suggesting that tumor-produced MMA is secreted and
activates fibroblasts in the stroma (Fig. [76]1f). Conversely, blocking
MMA production in A375 cells by knockdown of PCCA, a component of
propionyl-CoA-carboxylase, repressed their ability to induce the
activation and infiltration of fibroblasts in the tumor in vivo
(Fig. [77]s1a–c). Notably, an RNA-sequencing dataset of 501 whole
tumors from patient lung squamous cell carcinomas showed a correlation
between low MUT, MCEE, MMAA and MMAB levels (indicating high MMA) and
high expression of cancer-associated fibroblast markers ACTA1 (encoding
for SMA) and FAP (Fig. [78]s1d), suggesting that human tumors with
greater levels of MMA do indeed harbor a great proportion of CAFs.
Fig. 1. MMA produced by tumor cells promotes a cancer-associated fibroblast
phenotype.
[79]Fig. 1
[80]Open in a new tab
a Patient-derived tumor cells (n = 2537) projected according to imputed
Vimentin expression and imputed EPCAM expression. Each cell is colored
by average imputed expression of negative regulators of MMA production
(MMAA, MMAB, MCEE, and MUT), highlighted in yellow in the diagram
depicting propionate metabolism pathway (above). b The z-normalized
imputed expression of relevant mesenchymal, epithelial, and MMA marker
genes is displayed on the heat map ranked by MES Ratio, defined as the
log10 transform of the expression ratio of imputed VIM and EPCAM for
each cell. The MES Ratio curve along the top of the heat map shows the
absolute value of the MES Ratio across the ranked cells. The color bar
along the bottom of the heat map shows the sample tissue source
(metastasis: red and primary: black). The gene correlation and
associated p values were computed by performing a two-sided spearman
test between the normalized (non-imputed) expression of each gene and
the MES Ratio. c MUT was knocked down in A549 and A375 tumor cells.
Immunoblots show the protein level of MUT in cell lysates. d MMA levels
in the conditioned medium of the tumor cells were measured, normalized
to the total cell number (n = 3 independent experiments, mean ± SEM,
two-sided paired t-test). e Schematic of the co-culture experiment
performed in (f). Tumor cells with shGFP or shMUT knockdown were seeded
in a transwell insert, and co-cultured with fibroblasts seeded on the
bottom of six-well plates. Fibroblast lysates were collected for
immunoblots. f Immunoblots measuring CAF markers in MRC-5 fibroblasts
co-cultured for 4 days with shMUT-knocked down A549 tumor cells and BJ
fibroblasts co-cultured for 4 days with shMUT-knocked down A375 tumor
cells. g Immunoblots measuring CAF markers in MRC-5 and BJ cell lysates
treated with 1 mM or 5 mM of MMA for 5 days.
We have previously demonstrated that MMA in the serum is largely
encapsulated in lipid vesicles, allowing for accelerated entry into
cells at much lower concentrations compared to free MMA^[81]15. When we
isolated extracellular lipid vesicles from the conditioned media (CM)
of MUT-knocked down tumor cells (EVs^shMUT-A549), we found that they
indeed carried more MMA compared to control vesicles (EVs^shGFP-A549),
and could induce CAF markers when used to treat fibroblasts
(Fig. [82]s1e–f). Depletion of these vesicles from the CM of
MUT-knocked down cells abolished its ability to induce CAF markers in
fibroblasts, confirming that tumor-produced MMA, like the MMA in the
serum of elderly people, is delivered and acts on cells through EVs
(Fig. [83]s1g).
Treatment of MRC-5 and BJ fibroblasts with exogenous MMA reproduced the
effect of co-culture with or EVs from MUT-knockdown tumor cells on CAF
marker expression in a dose-dependent manner (Fig. [84]1g). The ability
of exogenous MMA to induce CAF markers in fibroblasts was similar to
that of the CM and lipid vesicles from MUT-knocked down tumor cells, as
well as other known CAF inducers, including TGFβ (Fig. [85]s1h).
Proliferation was not affected by 1 mM MMA treatment, and mildly
decreased under 5 mM of MMA (Fig. [86]s1i). We confirmed that MMA
activation of CAFs was not simply due to decreased pH or altered TCA
cycle flux, as other acids from the propionate metabolism pathway were
unable to reproduce the phenotype (Fig. [87]s1j). Intriguingly, MMA
also induced CAF production of matrix metalloproteinases (Fig. [88]1f),
which contribute to the ECM remodeling that promotes intravasation of
tumor cells into the bloodstream in early stages of metastasis^[89]16.
MMA-treated fibroblasts secrete EVs to promote pro-aggressive reprogramming
in tumor cells
To determine if the secretome of MMA-activated CAFs might direct tumor
cell behavior, we cultured tumor cells with CM from vehicle- or
MMA-treated fibroblasts (CM^veh-MRC5/BJ and CM^MMA-MRC5/BJ) and
observed a marked increase in markers of EMT (Fig. [90]2a). In
addition, co-injection of A549 tumor cells with MMA-treated MRC5
fibroblasts into mice significantly increased the ability of tumor
cells to metastasize, indicating that one or more secreted factors from
MMA-activated CAFs promotes a pro-metastatic phenotype in cancer cells
(Fig. [91]2b).
Fig. 2. EVs secreted by MMA-treated fibroblasts increase tumor
aggressiveness.
[92]Fig. 2
[93]Open in a new tab
a Immunoblots of A549 and A375 tumor cells after 5-day treatment by
conditioned media from vehicle or MMA-treated MRC-5 (for A549) or BJ
(for A375) fibroblasts. b A mixture of vehicle- or MMA-treated MRC5
with A549 cells were injected subcutaneously. The primary tumor and
metastasis formation was measured after 6 weeks (n = 9 mice,
mean ± SEM, two-sided unpaired t-test). c Experimental scheme. d–h
Pro-aggressive properties in A549 and A375 tumor cells treated with
EVs^veh-MRC5/BJ or EVs^MMA-MRC5/BJ from MRC-5 (for A549) or BJ
fibroblasts (for A375), evaluated by immunoblots measuring EMT marker
expression (d), drug resistance assays using carboplatin and paclitaxel
for A549 cells, and vemurafenib and AZD6244 for A375 cells (e; n = 3
independent experiments, mean ± SEM, two-way ANOVA), colony formation
assays for 3 weeks (f; n = 4 independent experiments, mean ± SEM,
two-sided paired t-test), transwell invasion and migration assays (g;
red scale bar indicates 100
[MATH: μ :MATH]
M, n = 3 independent experiments, mean ± SEM, two-sided paired t-test)
and measurement of primary tumor and metastasis formation 5 weeks after
subcutaneous injection into mice (h; n = 10 mice for A549 EVs^veh-MRC5
group, n = 9 mice for A549 EVs^MMA-MRC5 group, n = 8 mice for A375
EVs^veh-BJ group, n = 9 mice for A375 EVs^MMA-BJ group, mean ± SEM,
two-sided unpaired t-test).
Next, we aimed to identify the components of the CM secreted by
MMA-treated fibroblasts that was driving the EMT phenotype in tumor
cells. EVs are loaded with signaling molecules and genetic material,
and function as essential signaling mediators in the TME^[94]17,[95]18.
Considering that MMA is delivered from tumor cells to fibroblast in
EVs, we looked to see whether the fibroblast messengers reciprocally
driving EMT in tumor cells were also contained in EVs. From MRC-5 lung
and BJ dermal fibroblasts, we isolated EVs from the CM after vehicle or
MMA treatment (EVs^veh-MRC5 and EVs^veh-BJ, or EVs^MMA-MRC5 and
EVs^MMA-BJ, respectively) (Fig. [96]2c). We did not observe a
significant difference in the number or size of EVs secreted by
MMA-treated fibroblasts (EVs^MMA-MRC5/BJ) compared to those secreted by
vehicle-treated fibroblasts (EVs^veh-MRC5/BJ) (Fig. [97]S2a, b). Survey
of extracellular vesicle marker proteins confirmed the purity of these
EVs (Fig. [98]S2c). To determine if the CAF-secreted factor driving EMT
in tumor cells was being delivered through these structures, we then
added EVs^veh-MRC5/BJ or EVs^MMA-MRC5/BJ to their tissue-matched A549
or A375 tumor cells (Fig. [99]2c). Upon treatment of tumor cells with
EVs^MMA-MRC5, we once again observed an increase in EMT markers
(Fig. [100]2d). In contrast, the supernatant from the CM^MMA-MRC5/BJ
after isolation of the EVs lost its ability to induce this effect
(Fig. [101]S3a). We noted that when A549 tumor cells treated with
isolated EVs^MMA-MRC5 were then cultured in normal media, they
converted back to an epithelial phenotype after 5 days, highlighting
the plasticity of EMT (Fig. [102]S3b). Importantly, when
EVs^MMA-MRC5-treated tumor cells were released from EVs^MMA-MRC5
treatment, but subsequently co-cultured in the presence of untreated
fibroblasts, the tumor cells maintained their aggressive phenotype
(Fig. [103]S3b). This underscores the importance of a positive feedback
loop between the tumor and stroma, wherein fibroblast activation drives
tumor cell aggression, which reciprocally drives more fibroblast
activation, ultimately leading to metastatic progression. A375 and A549
tumor cells treated with EVs^MMA-MRC5/BJ also exhibited increased
resistance to chemotherapeutic and targeted therapy drugs, and
displayed increased colony formation in soft agar compared to tumor
cells treated with EVs^veh-MRC5/BJ (Fig. [104]2e, f). In addition,
tumor cells treated with EVs^MMA-MRC5/BJ exhibited increased invasion
and migration ability in transwell assays, and formed more metastases
following a subcutaneous primary tumor implantation in vivo
(Fig. [105]2g, h). Intriguingly, despite having significantly higher
metastases formation, tumor cells treated with EVs^MMA-MRC5 did not
form significantly larger primary tumors (Fig. [106]2h). This indicates
that the EVs isolated from MMA-activated fibroblasts specifically drive
an aggressive, metastatic phenotype in tumor cells, rather than
increased cell proliferation.
IL-6 in fibroblast-secreted EVs mediates tumor cell metastatic signaling
As we did not see a change in the number and size of EVs induced by MMA
treatment, we speculated that the potent tumor cell response observed
after MMA treatment could be due to differentially loaded EV cargo. To
identify the active factor in EVs from MMA-treated fibroblasts driving
metastatic progression, we performed proteomic analysis on EVs^veh-MRC5
and EVs^MMA-MRC5. One of the most significantly upregulated secreted
proteins in EVs^MMA-MRC5 compared to EVs^veh-MRC5 was IL-6, a
pro-inflammatory cytokine that has been implicated in promoting EMT and
metastasis (Fig. [107]3a, Fig. [108]s4a)^[109]19–[110]21. We also
observed that genes driving IL-6/JAK/STAT3 pathway activity were
enriched in more mesenchymal cells characterized by downregulation of
key genes restricting MMA production from human lung cancer tumor and
metastasis tissue samples (Fig. [111]S4b). Indeed, both IL-6/JAK/STAT3
signaling, measured by JAK2 and STAT3 phosphorylation, and TGFβ
signaling, measured by phosphorylation of SMAD proteins, were activated
in A549 cells upon treatment with EVs^MMA-MRC5 (Fig. [112]3b). Notably,
while EVs^MMA-MRC5 increased Y705 phosphorylation of STAT3, which is
the main regulator of cytokine-induced JAK/STAT3 signaling, it did not
affect phosphorylation at S727 (Fig. [113]3b), suggesting a specificity
in EVs^MMA-MRC5-mediated downstream signaling. To determine the
necessity of these signaling cascades for the ability of EVs^MMA-MRC5
to drive EMT, we blocked their activation in A549 tumor cells using the
TGFβR or STAT-3 inhibitors, SB431542 and cryptotanshinone, respectively
(Fig. [114]3c). Inhibition of these pathways effectively blocked EMT
induction by EVs^MMA-MRC5 in A549 tumor cells, re-sensitized cells to
drug treatment, and suppressed the increase in invasion and migration
(Fig. [115]3d–f). Similarly, knockdown of IL6R in tumor cells
suppressed both IL-6/JAK/STAT3 and TGFβ signaling, and suppressed
EVs^MMA-MRC5-induced EMT marker expression, drug resistance, and
invasion and migration, suggesting that IL-6R activation functions
upstream of TGFβ pathway signaling in this context (Fig. [116]3g–j,
Fig. [117]S4c). In addition, treating A549 lung tumor cells with
tocilizumab, an IL-6R antibody and inhibitor, replicated the effect
of IL6R knockdown, effectively blocking IL-6/JAK/STAT3 and TGFβ
signaling and suppressing the induction of EMT and drug resistance by
EVs^MMA-MRC5 (Fig. [118]S4d–f). Finally, we knocked down IL6 in MRC-5
fibroblasts before treating them with MMA and isolated their secreted
EVs. While IL-6 knockdown in fibroblasts did not have any effect on the
ability of MMA to induce CAF marker expression in fibroblasts, it
effectively suppressed the ability of EVs^MMA-MRC5 to induce
IL-6/JAK/STAT3 and TGFβ signaling in tumor cells, and was sufficient to
abolish the EMT-inducing effect of EVs^MMA-MRC5 and their ability to
boost drug resistance, invasion and migration (Fig. [119]s5).
Fig. 3. IL-6 in the EVs of MMA-treated fibroblasts mediates pro-aggressive
STAT-3 and TGFβ signaling in tumor cells.
[120]Fig. 3
[121]Open in a new tab
a Volcano plots showing protein level distribution from proteomics
analysis comparing the compositions of EVs isolated from vehicle- or
MMA-treated MRC-5 fibroblasts (n = 3 independent experiments, two-sided
paired t-test). b Immunoblots showing JAK2-STAT3 signaling and TGFβ
signaling after 3 h of EV treatment in A549 cells. c Immunoblots
measuring signal activation in A549 cells pre-treated with vehicle,
TGFβR inhibitor SB431542 or JAK inhibitor cryptotanshinone for 30 min,
then treated with EVs from MRC-5 fibroblasts for 3 h. d–f
Pro-aggressive properties of A549 cells treated with EVs^veh-MRC5 or
EVs^MMA-MRC5 with or without the TGFβR inhibitor SB431542 or the JAK
inhibitor cryptotanshinone for 4 days, evaluated by immunoblots
measuring EMT marker expression (d), carboplatin resistance assay (e;
n = 3 independent experiments, mean ± SEM, two-way ANOVA), and invasion
and migration transwell assays (f; red scale bar indicates 100
[MATH: μ :MATH]
M, n = 3 independent experiments, mean ± SEM, two-sided paired t-test).
g Immunoblots measuring signal activation in A549 cells with shGFP or
shIL-6R knockdown (#1 and #2) and treated with EVs^veh-MRC5 or
EVs^MMA-MRC5 for 3 h. h–j Pro-aggressive properties of A549 cells
treated with of EVs^veh-MRC5 or EVs^MMA-MRC5 with shGFP or shIL-6R
knockdown for 5 days, evaluated by immunoblots measuring EMT marker
expression (h), carboplatin resistance assay (i; n = 3 independent
experiments, mean ± SEM, two-way ANOVA), and invasion and migration
transwell assays (j; red scale bar indicates 100
[MATH: μ :MATH]
M, n = 3 independent experiments, mean ± SEM, two-sided paired t-test).
MMA activates fibroblasts through ROS activated NF-κB and TGFβ signaling
Next, we set out to characterize the mechanism by which MMA treatment
of fibroblasts led to activation of the CAF phenotype and IL-6 loading
into and secretion from EVs. We performed RNA-seq on MRC-5 fibroblasts
treated with vehicle or MMA, and a pathway enrichment analysis of the
RNA-seq data showed an upregulation of genes in the NF-κB and TGFβ
signaling pathways in MMA-treated fibroblasts (Fig. [122]4a). Crosstalk
between these two pathways has been described previously, wherein TGFβ
signaling leads to the sequential phosphorylation of TAK1, IKK, and
NF-κB (Fig. [123]4b)^[124]22. We confirmed that these pathways are
activated in MRC-5 fibroblasts upon MMA treatment, or treatment by EVs
derived from MMA-producing tumor cells (EVs^shMUT-A549) (Figs. [125]4c,
s[126]6a). Using time course analysis, we noted that p65
phosphorylation occurred later than SMAD3 and TAK1 phosphorylation
(Fig. [127]4c). Pharmacological inhibition of TGFβR using SB43152, but
not of TAK1 and IKK using Takinib and IKK16, respectively, effectively
suppressed the induction of CAF markers by MMA, suggesting that the
MMA-induced CAF phenotype is largely regulated by TGFβ separately from
NF-κB signaling (Fig. [128]4d, e). Similarly, genetic knockdown of
TGFBR1, but not CHUK1 (encoding for IKK1), negated the ability of MMA
to induce CAF markers (Fig. [129]s6b, c). Interestingly,
pharmacological inhibition of TGFβR, TAK1 and IKK were all individually
able to reduce IL-6 loading into EVs^MMA-MRC5, indicating that
MMA-induced IL-6 secretion through EVs is mediated by NF-κB downstream
of TGFβ-TAK1-IKK activation, and we saw the same effect with genetic
knockdown of TGBR1 and CHUK1 (Figs. [130]4f, s[131]6d, e). In line with
this and our earlier findings demonstrating the necessity of IL-6, all
three inhibitors abrogated the ability of EVs^MMA-MRC5 to induce
IL-6/JAK/STAT3 and TGFβ signaling in A549 tumor cells, along with EMT
(Fig. [132]4g, h, Fig. [133]s6f, g). In addition, all three inhibitors
were able to suppress the ability of EVs^MMA-MRC5 to increase drug
resistance in tumor cells, although this effect was small using SB43152
or TAKinib (Fig. [134]4i).
Fig. 4. MMA promotion of the CAF phenotype and EV-associated IL6 secretion
occurs through TGF-β and NF-κB signaling.
[135]Fig. 4
[136]Open in a new tab
a Pathway enrichment analysis on RNA-seq data from MRC-5 cells treated
with vehicle or MMA for 5 days. Genes with expression differences
greater than two-fold and p < 0.001 were counted (n = 3 independent
experiments, two-sided paired t-test). b TGFβ signaling leads to
downstream NF-κB activation. c Immunoblots measuring signal activation
over time in MRC-5 fibroblasts treated with 1 mM MMA. d Immunoblots
measuring signaling activation in MRC-5 fibroblasts treated with MMA
alone or in combination with IKK inhibitor IKK16, TGFβR inhibitor
SB431542, or TAK1 inhibitor TAKinib for 6 h. e Immunoblots of CAF
markers in MRC-5 lysates treated with MMA in combination with IKK
inhibitor IKK16, TGFβR inhibitor SB431542, or TAK1 inhibitor TAKinib
for 5 days. f Immunoblots showing IL-6 amount in EVs from MRC-5
fibroblasts treated with vehicle or MMA or MMA alone or in combination
with IKK inhibitor IKK16, TGFβR inhibitor SB431542, or TAK1 inhibitor
TAKinib. g Immunoblots measuring signaling activation in A549 cells
treated with EVs from MRC-5 fibroblasts treated with MMA alone or in
combination with IKK inhibitor IKK16, TGFβR inhibitor SB431542, or TAK1
inhibitor TAKinib for 3 h. h, i Pro-aggressive properties of A549 cells
treated with EVs from MRC-5 fibroblasts treated with vehicle, MMA
alone, or MMA in combination with IKK inhibitor IKK16, TGFβR inhibitor
SB431542, or TAK1 inhibitor TAKinib, evaluated by immunoblots measuring
EMT marker expression (h) and carboplatin resistance assay (i; n = 3
independent experiments, mean ± SEM, two-way ANOVA).
Notably, IKK inhibition had a greater effect than both TGFβR inhibition
or TAK1 inhibition in reducing IL-6 loading into EVs^MMA-MRC5, which
also corresponded with a greater effect in suppressing the potency of
EVs^MMA-MRC5 for promoting EMT and drug resistance in A549 tumor cells
(Fig. [137]4f–i). This suggested that the NF-κB activation downstream
of TGFβR signaling was supplemented by a certain level of NF-κB
activation independent of TGFβR signaling, together producing the full
effect of IL-6 loading into EVs^MMA-MRC5 and the full potency of
EVs^MMA-MRC5 to induce EMT and increase drug resistance in tumor cells.
As increased generation of reactive oxygen species (ROS) has been
established to trigger both NF-κB and TGFβ signaling^[138]23,[139]24,
we conjectured that ROS activation of NF-κB both independently and
through TGFβR-TAK1-IKK-NF-κB signaling may be at the apex of the MMA
signal that induces the CAF phenotype and function. In addition,
pathway enrichment analysis of RNA-seq data showed that the oxidative
stress response was upregulated in MMA-treated MRC-5 fibroblasts
(Fig. [140]4a). Indeed, MMA treatment, as well as treatment by EVs from
MMA-producing tumor cells (EVs^shMUT-A549) increased ROS with peak
levels at 6 h, corresponding to the peak in TGFβ and NF-κB signaling,
while also increasing malondialdehyde (MDA), a marker of oxidative
stress, over several days (Fig. [141]4c, Fig. [142]5a,
Fig. [143]s7a–c). While ROS induction by MMA was similar to that
observed by other ROS inducers, including rotenone, TTFA, and hydrogen
peroxide, these other inducers were unable to drive the same level of
CAF activation in the fibroblasts (Fig. [144]s7d, e). This suggests
that MMA may increase ROS through a specific mechanism, or that MMA
activates other processes that work with ROS to induce activation of
fibroblasts.
Fig. 5. TGF-β and NF-κB mediated activation of fibroblasts and EV-associated
IL6 secretion occurs downstream of ROS generation.
[145]Fig. 5
[146]Open in a new tab
a (left) ROS levels and (right) MDA levels in MRC-5 fibroblasts after
1 mM MMA treatment (n = 4 independent experiments for ROS measurement,
mean ± SEM, one-way ANOVA; n = 3 independent experiments for MDA
measurement, two-sided paired t-test). b ROS levels in MRC-5
fibroblasts after MMA treatment alone or in combination with NAC or
SkQ1(n = 4 independent experiments, mean ± SEM, two-sided paired
t-test). c, d Immunoblots of MRC-5 fibroblasts treated with MMA alone
or in combination with NAC or SkQ1 for 6 h (c) or 5 days (d). e
Immunoblots showing IL-6 amount in EVs from MRC-5 fibroblasts after MMA
treatment alone or in combination with NAC or SkQ1. f Immunoblots
measuring signaling activation in A549 cells treated for 3 h with
EVs^veh-MRC5 or EVs^MMA-MRC5from MRC-5 fibroblasts treated with MMA
alone or in combination with NAC or SkQ1. g, h Pro-aggressive traits of
A549 cells treated for 5 days with EVs^veh-MRC5 or EVs^MMA-MRC5 from
MRC-5 fibroblasts treated with MMA alone or in combination with NAC or
SkQ1, evaluated by immunoblots measuring EMT marker expression (g) and
carboplatin resistance assay (h); n = 3 independent experiments,
mean ± SEM, two-way ANOVA). i Primary tumor and metastasis formation in
mice 4 weeks after subcutaneous injection of A549 cells treated with
EVs from MRC-5 fibroblasts treated with vehicle, MMA, or MMA and NAC
(n = 10 mice for EVs^veh-MRC5 and EVs^MMA-MRC5+NAC groups, n = 9 mice
for EVs^MMA-MRC5 group, mean ± SEM, two-sided unpaired t-test). Data
are partially previously represented in Fig. [147]2g.
Treatment of these fibroblasts with the antioxidants N-acetyl-cysteine
(NAC) or SkQ1 effectively inhibited MMA induction of NF-κB and TGFβ
signaling, along with the MMA-induced increase in CAF markers and
increased IL-6 loading into EVs^MMA-MRC5 (Fig. [148]5b–e). When
EVs^MMA-MRC5 were collected from fibroblasts that were co-treated with
antioxidants, they were no longer able to activate IL-6/JAK/STAT3 or
TGFβ signaling in A549 tumor cells (Fig. [149]5f). Consistently,
antioxidant treatment of fibroblasts suppressed the ability of
EVs^MMA-MRC5 to induce the EMT phenotype and increase drug resistance
in A549 tumor cells, and reversed the ability of these tumor cells to
form metastases in vivo (Fig. [150]5g–i). Together, our data
illustrates a mechanism wherein exposure of fibroblasts to MMA
generates ROS and induces oxidative stress, which activates NF-κB and
TGFβ signaling. Canonical TGFβ signaling regulates CAF marker
expression, while NF-κB signaling, which is activated by ROS both
independently of and downstream of TGFβ signaling through TAK1 and IKK,
regulates IL-6 association and secretion with vesicles. In tumor cells,
IL-6 enriched EVs^MMA-MRC5 activates IL-6/JAK/STAT3 and TGFβ signaling,
promoting EMT and the acquisition of pro-aggressive traits
(Fig. [151]6).
Fig. 6. MMA activates fibroblasts and induces their EV-associated IL-6
secretion, which drives metastatic reprogramming in tumor cells.
Fig. 6
[152]Open in a new tab
MMA produced by tumor cells induces generation of ROS in fibroblasts.
ROS activates TGFβ signaling, which promotes expression of CAF markers,
and NF-κB signaling, which promotes IL-6 loading into and secretion
through EVs. EVs loaded with IL-6 activate STAT3 signaling and TGFβ
signaling in tumor cells, promoting EMT, drug resistance and
metastasis.
Discussion
Here, we depict how a recently identified aging-associated and
tumor-produced oncometabolite, MMA, can also function as a tumor cell
messenger by acting on the TME to drive metastatic progression. We also
characterize the downstream signaling cascades activated by MMA in
fibroblasts. By increasing ROS generation, MMA induces a secretory
signature in CAFs wherein IL-6 delivered in EVs drives metastatic
signaling and progression of epithelial-like (or primary) cancer cells.
While the structure of the signaling cascades activated by MMA are
likely to vary according to cell-type, the ability of MMA to induce
oxidative stress may be a conserved phenomenon upstream of MMA-mediated
functions in other cellular contexts, such as the TGFβ
signaling-dependent increase in SOX4 in MMA-treated tumor
cells^[153]15. More research is warranted to verify this possibility,
as well as to elucidate how MMA may increase ROS. A potential mechanism
may involve MMA’s ability to inhibit succinate dehydrogenase, an
essential component of the mitochondrial respiratory chain complex
II^[154]25. Indeed, diseased mitochondria and mitophagy dysfunction has
been described in MUT deficiency underlying methylmalonic
acidemia^[155]26. Additionally, while previous studies have described a
role of ROS in CAF activation, the precise mechanisms by which ROS
contributes to this process across various contexts are still
unclear^[156]27,[157]28. For example, ROS induction may be preceded by
an upstream event, such as loss of CAV-1, and co-occur with
mitochondrial dysfunction and aerobic glycolysis to stimulate CAF
activation^[158]27. In our cellular system, we observed that other
known ROS inducers, including rotenone and hydrogen peroxide, were
unable to produce CAF marker activation to the same level as MMA. These
findings suggest that while ROS is essential for MMA-induced CAF
activation, other simultaneous processes, that are potentially also
downstream of MMA, are required.
The role of circulatory IL-6 in metastasis and therapy resistance has
been previously observed, and is long known to be increased in the
serum with age^[159]29,[160]30. IL-6 was also recently reported to be
increased in the serum of methylmalonic acidemia patients^[161]31. The
discovery of a specific mode of IL-6 delivery from stroma to tumor
through EVs, however, likely confers a particularly calibrated and
potent effect. The proportion of IL-6 released freely or delivered
through EVs has been shown to vary widely and depend on the biological
systems involved; for example, almost all IL-6 released from monocytes
are free, while all IL-6 released from T cells are
encapsulated^[162]32. In addition to providing a concentrated influx of
the cytokine when IL-6 is delivered through these lipidic structures,
EVs also protect their contents from environmental degradation, and
expression of surface proteins may facilitate the targeting of EVs to
distinct cell types^[163]32. Co-delivery of different cytokines
encapsulated together is also likely to have different synergistic
effects driving distinct phenotypes. A precise characterization of the
regulatory mechanisms dictating how IL-6 is loaded into EVs and the
proportions of IL-6 encapsulated or embedded in the membranes will
require further investigation, and will likely illuminate key pathways
for cytokine delivery through EVs in other cellular contexts.
As IL-6 signaling is pro-inflammatory and TGF-β signaling is
anti-inflammatory, their associated pathways are often described to
function antagonistically. For example, STAT3 can bind Smad3 and
disrupt formation of Smad2/Smad3 complexes, hindering the DNA-binding
ability of these transcription factors^[164]33. In contrast, we show
that STAT3 signaling in tumor cells treated with EVs^MMA-MRC5 promotes
TGF-β signaling in a positive crosstalk interaction to drive EMT. This
is supported by previous studies which found that STAT3-Smad3 complex
formation and nuclear translocation is required for TGF-β-induced Snail
promoter activation and EMT induction in KRAS mutated Panc-1
cells^[165]34. The factors that determine the nature of these
interactions in different contexts remain to be elucidated and may be
defined by the varying strengths of each pathway’s activation or by the
expression of additional co-factors.
Furthermore, while this was not explored in the current study, our
findings support a likely means by which the aging body shapes the TME
through MMA, further coloring in the link between age as a risk factor
and poorer cancer outcomes. Indeed, it has been shown that older people
have increased tissue fibrosis, and the possibility that MMA may play a
role in this is certainly intriguing^[166]35. Beyond the effect of MMA
on fibroblasts, the full scope of how MMA functions on other cell
components of the TME, such as the immune system, has yet to be
uncovered, and represents a huge untapped potential for therapeutic
interventions targeting various stages of the tumorigenic process.
Materials and methods
Ethics statement
All experiments performed in this study are in compliance with local
ethical regulations. All animal experiments were approved by the
Institutional Animal Care & Use Committee (IACUC) at the Belfer
Research Building Vivarium of Weill Cornell Medicine.
Analysis of additional validation datasets
The filtered, processed, and annotated single-cell RNA-seq data from an
independent lung adenocarcinoma patient cohort^[167]36 was received
directly from its authors. This dataset includes 17 patient samples – 5
metastatic, 8 primary tumor, and 4 normal samples. Analysis was limited
to annotated tumor cells from 8 primary tumors and 4 tumor metastases;
genes not expressed in any of the tumor cells were removed. The one
spinal metastasis was excluded because it exhibited stark
patient-specific effects within tumor cells that were not reproduced
across other samples. This yielded a filtered count matrix containing
2537 tumor cells and 18,947 genes. All analyses were performed using
the normalized or normalized-and-imputed count matrices provided by the
authors.
Cell lines
A549 cells (non-small cell lung cancer; CCL-185) were obtained from the
American Type Culture Collection (ATCC) and cultured in RPMI 1640
medium (Corning) supplemented with 10% FBS (Sigma-Aldrich) and 1%
penicillin-streptomycin (Gibco). A375 human melanoma cells (CRL-1619)
were also obtained from ATCC and cultured in high-glucose DMEM (Gibco)
supplemented with 10% FBS (Sigma-Aldrich) and 1%
penicillin-streptomycin (Gibco). MRC-5 human lung fibroblasts (CCL-171)
and BJ foreskin fibroblasts (CRL-2522) were also obtained from ATCC and
maintained in EMEM (ATCC) supplemented with 10% FBS (Sigma-Aldrich) and
1% penicillin-streptomycin (Gibco). HEK293T cells were obtained from
GenHunter and cultured in high-glucose DMEM (Gibco) supplemented with
10% FBS (Sigma-Aldrich) and 1% penicillin–streptomycin (Gibco). All
cell lines were maintained at 37 °C and 5% CO[2]. All cell lines were
routinely tested for mycoplasma and were at all times mycoplasma
negative.
Mice
Female nu/nu athymic mice (Envigo) were purchased at the age of 6–7
weeks, and the experiments were started 7–10 days after the mice were
received at the Weill Cornell Medicine Belfer Research Building
Vivarium. Experimental groups of up to 10 mice were created randomly
and mice were group housed (maximum five in a cage) in standard cages
with unrestricted acidified water and food (PicoLab Rodent Diet 5053
(Labdiet, Purina) containing 20% protein and 5% fat). Animal husbandry
was carried out by the vivarium technical staff in a human xenograft
designated area following animal biosafety level-2 procedures. The room
was maintained at 21–23 °C with a 12 h light–dark cycle. The mice were
maintained in compliance with Weill Cornell Medicine Institutional
Animal Care and Use Committee protocols. The tumor size limit on the
protocol was 20 mm on the largest dimension or 2.5 cm^3 tumor volume or
10% of body weight, whichever was reached first. For mouse studies, no
statistical method was used to predetermine sample size, mice were
randomly distributed among the treatment groups and no blinding was
performed. All mouse studies have received ethical approval by IACUC at
Weill Cornell Medicine under protocol number 2014-0060.
Immunohistochemistry staining
Immunohistochemistry (IHC) was performed on formalin-fixed,
paraffin-embedded tissue. Five-micron paraffin sections were cut. After
antigen retrieval with citrate solution, slides were rinsed and blocked
with a peroxidase-blocking reagent and incubated with αSMA antibody
(ab150301- Abcam, 1:200). Slides were scanned using the Zeiss
AxioScan7. Five views per mouse and eight mice per group were used for
the quantification. The signal intensity of each view was quantified by
using Image J. All the values were normalized to the average signal of
shGFP group.
Conditioned media collection and extracellular vesicle isolation
Fibroblasts were treated with MMA for 5 days. Then, the media was
removed, cells were washed twice with PBS, and serum free medium was
added. Two days later, the CM was collected. CM was centrifuged at
2000 g for 15 min, then 120,000 g for 20 min. The supernatant was then
ultracentrifuged at 100,000 g for 70 min. Then, the supernatant was
discarded, and the pellet was washed by resuspension in PBS and
re-ultracentrifugation at 100,000 g for 70 min. The total protein
amount in EVs was determined using the DC Protein Assay Kit II
(BioRad). Particle numbers were determined using the Nanosight NS500.
Electron microscopy
EVs were processed for EM imaging as previously described^[168]37.
Frozen EVs were thawed and fixed on ice for 5 min in 2% PFA (EMS,
15710). Then, the sample was deposited on Formvar/carbon-coated nickel
grids (EMS, FCF400H-NI-SB), fixed for 5 min in 1% glutaraldehyde (EMS,
16120), contrasted for 5 min with 4% uranyl oxalate and finally
embedded in 2% methyl cellulose (Sigma, M6385) and uranyl acetate
solution (EMS 22400). Images were acquired using a JEOL JEM 1400
transmission electron microscope (JEOL, USA, Inc, Peabody, MA) at
100 keV equipped with a Veleta 2 K x 2 K CCD (EMSIS, GmbH, Muenster,
Germany). For each experiment, two grids were prepared for each sample,
and 5 random images were acquired from 5 random hexagons in each grid.
The largest diameters of all membrane particles were quantitated using
ImageJ.
Co-culture experiments
Cells to be collected for immunoblotting were seeded in 2 ml RPMI media
with 10% FBS onto six-well plates. The next day, accompanying
co-culture cells were seeded in another 2 ml of RPMI media with 10% FBS
onto the top of 0.4 μm cell culture inserts (VWR) placed into the
previously seeded six-well plates. The plates were gently shaken four
times a day for 3 days. Inserts were discarded and proteins were
extracted as described in the Immunoblots section.
Immunoblots
Proteins were isolated directly from intact cells via acid extraction
using a 10% TCA solution (10% trichloroacetic acid, 25 mM NH[4]OAc,
1 mM EDTA, 10 mM Tris-HCl pH 8.0). Precipitated proteins were harvested
and solubilized in a 0.1 M Tris-HCl pH 11 solution containing 3% SDS
and boiled for 10–15 min. For EV proteins, samples were extracted using
RIPA buffer (40 mM HEPES [pH 7.4], 1 mM EDTA, 120 mM NaCl, 0.5 mM DTT,
10 mM b-glycerophosphate, 1 mM NaF, 1 mM Na3VO4, 0.1% Brij-35, 0.1%
deoxycholate, and 0.5% NP-40) supplemented with protease inhibitors
(250 mM PMSF, 5 mg/ml pepstatin A, 10 mg/ml leupeptin, and 5 mg/ml
aprotinin), incubated at 4 °C for 15 min, then incubated with 4× LDS
for 10 min at 70 °C. Protein content was determined with the DC Protein
Assay kit II (BioRad), and 30 μg total protein from each sample was run
on SDS–PAGE under reducing conditions. The separated proteins were
electrophoretically transferred to a nitrocellulose membrane (GE
Healthcare), which was blocked in TBS-based Odyssey Blocking buffer
(LI-COR). Proteins of interest were probed with specific antibodies
(listed as ‘target protein’ (catalog no. - vendor, dilution factor):
FAP (66562 s—Cell Signaling Technology, 1:1000), SMA (ab5694—Abcam,
1:1000), CAV-1(ab2910—Abcam, 1:1000), PAI-1 (612024—BD, 1:1000), PCCA
(ab187686—Abcam, 1:2000), Vinculin (V9264—Sigma Aldrich, 1:5000), MMP-2
(4022 S—Cell Signaling Technology 1:1000), MMP-14 (ab51074—Abcam,
1:1000), MMP-13 (ab39012—Abcam, 1:1000), CTGF (ab6992—Abcam, 1:500),
E-Cadherin (610181—BD, 1:1000), Fibronectin (ab2413—Abcam, 1:10,000),
Vimentin (5741 S—Cell Signaling Technology, 1:2000), Snail (3879 S—Cell
Signaling Technology, 1:1000), p-JAK2(Y1007/1008) (3771—Cell Signaling
Technology, 1:500), JAK2 (3230—Cell Signaling Technology, 1:1000),
p-Stat3 Y705 (ab76315—Abcam, 1:1000), p-Stat3 S727 (9136 S—Cell
Signaling Technology, 1:1000), Stat3 (9139 S—Cell Signaling Technology,
1:1000), p-Smad3 S423/425 (ab52903—Abcam, 1:1000), Smad3 (9523 S—Cell
Signaling Technology, 1:1000), IL-6 (12153 S—Cell Signaling Technology,
1:1000), CD81 (56039 S—Cell Signaling Technology, 1:1000), CD9
(ab223052—Abcam, 1:500), Flotillin-1 (610820—BD, 1:1000), GM130
(610823—BD, 1:1000), Lamin (4777—Cell Signaling Technology, 1:1000),
Calnexin (ab112995—Abcam, 1:1000), β-Actin (4967—Cell Signaling
Technology, 1:2000), p-Smad2 S465/467 (3108 S—Cell Signaling
Technology, 1:1000), Smad2 (3103 S, Cell Signaling Technology, 1:1000),
p65 (8242 S—Cell Signaling Technology, 1:1000), p-p65 S536 (3036 S—Cell
Signaling Technology, 1:1000), p-IκBα (Ser32/36) (9246 S—Cell Signaling
Technology, 1:1000), IκBα (9242 S—Cell Signaling Technology, 1:1000),
TAK1 (5206 S—Cell Signaling Technology, 1:1000), p-TAK1 S412
(9339 S—Cell Signaling Technology, 1:1000). Membranes were incubated
with primary antibodies overnight at 4 °C. Membranes were then either
incubated with the appropriate horseradish peroxidase-conjugated
anti-rabbit (NA934—GE Healthcare, 1:10,000), anti-mouse (NA931—GE
Healthcare, 1:10,000) or anti-goat (AP180P—Millipore, 1:10,000)
immunoglobulin for 1 h at room temperature, and signals developed using
Amersham ECL detection system (GE Healthcare), or they were incubated
with the appropriate donkey anti-rabbit Alexa Fluor 488 (A-21206—Thermo
Fisher Scientific, 1:10,000) or donkey anti-mouse Alexa Fluor 555
([169]A31570—Thermo Fisher Scientific, 1:10,000) immunoglobulin for 1 h
at room temperature, and signals were developed using the LI-COR
Odyssey CLx Imaging System.
Cell culture treatments
For all time courses signaling experiments, cells were seeded one day
before inhibitor and MMA treatments. Inhibitors were added 30 min
before any MMA treatments. Cells were treated at the same time, then
protein was harvested at different time points. Inhibitors and
antioxidants used were as follows: SB431542 (Selleck Chemicals, 5 μM),
Cryptotanshinone (Cayman Chemical, 1 μM), IKK16 (Millipore Sigma,
1 μM), Takinib (Cayman Chemical, 10 μM), SkQ1 (Cayman Chemical, 1 μM),
NAC (Sigma Aldrich, 2 mM).
For long-term EV treatments of tumor cells (such as for EMT marker
measurement and functional assays such as drug resistance, colony
formation and in vivo experiments), cells were seeded and then treated
with 5 μg/ml of EVs for 3–5 days. For short-term EV treatments of tumor
cells (such as for signaling activation measurement), cells were
treated with 5 μg/ml of EVs for 3 h.
Proteomics analysis of extracellular vesicles
EVs were isolated from the conditioned medium of vehicle- or
MMA-treated MRC5 fibroblast and total protein amount in EVs was
measured as described above. 50 μg of total protein from each sample
was TCA precipitated and acetone washed. Pellets were re-suspended in
8 M urea, 50 mM ammonium bicarbonate (AMBIC). Proteins were reduced and
alkylated with dithiothreitol and iodoacetamide. Samples were diluted
to 2 M urea with 50 mM AMBIC and digested overnight with Lysyl
endopeptidase (lysC, Wako Chemicals USA, Inc.), then diluted to 1 M
urea and digested with trypsin (Promega V5111) for 6 h. Peptides were
desalted on C18 STAGE Tips^[170]38. Eluted peptides were dried and
re-suspended in 5% formic acid.
Mass Spectrometric analysis was performed on a Thermo Orbitrap Fusion
mass spectrometer equipped with a FAIMS Pro ion mobility cell and an
Easy nLC-1000 UHPLC. Peptides were separated with a gradient of 5–26%
ACN in 0.1% FA over 75 min and introduced into the mass spectrometer by
electrospray ionization as they eluted off a self-packed 40 cm, 100 µm
(ID) column packed with 1.8 µm, 120 Å pore size, C18 resin (Sepax
Technologies, Newark, DE). The column was heated to 60 °C. Peptides
were detected using a data-dependent method. For each precursor scan in
the Orbitrap, we cycled through five FAIMS compensation voltage values
(−40, −50, −60, −70, −80). For each, we allowed up to 1 s for selection
of the most abundant precursors for HCD fragmentation (35% NCE) and
MS/MS analysis in the ion trap. AGC targets of 4e5 and 1e4 were used
for MS1 and MS2 scans, respectively. Ions selected for MS2 analysis
were excluded from reanalysis for 45 s. Ions with +1 or unassigned
charge were also excluded from analysis.
MS/MS spectra were matched to peptide sequences using COMET (version
2019.01 rev. 5)^[171]39 and a composite database containing the 20,415
Uniprot reviewed canonical predicted human protein sequences
([172]http://uniprot.org, downloaded 5/1/2019) and its reversed
complement. Search parameters allowed for two missed cleavages, a mass
tolerance of 25 ppm, a static modification of 57.02146 Da
(carboxyamidomethylation) on cysteine, and a dynamic modification of
15.99491 Da (oxidation) on methionine. Peptide spectral matches were
filtered to 1% FDR using the target-decoy strategy^[173]40 and then to
1% protein FDR. Label-free quantification was performed using peptide
intensities from the integrated areas under each corresponding
extracted-ion-chromatogram peak. Intensities for all peptides mapping
to each protein were summed for each sample. Subsequent data
processing, including normalization and statistical analysis, was done
using Perseus as described^[174]41.
Colony formation assays
The base layer of agarose gel plates was made by mixing 1.2% agarose
gel solution warmed up at 42 °C with 2× medium with 20% FBS in a 1:1
ratio and allowed to set at room temperature. To form the top layer,
0.6% agar and 2× medium with 20% FBS were warmed at 42 °C. Cells were
resuspended in this 1:1 mixture (giving 0.3% agar in 1× medium) and
allowed to set for 4 h at room temperature. Cells were treated with EVs
and media was changed twice a week. After 3-4 weeks, cells were stained
using 0.1% crystal violet in 10% ethanol for 10 min, followed by 5×
rinses in dH[2]O. Plates were first scanned and colonies were counted
using Image J.
Drug resistance assays
A549 and A375 tumor cells were treated with EVs for 5 days. Cells were
then seeded into 96-well plates in technical triplicates. The next day,
the cells were treated with vehicle control (DMSO (0.1%), carboplatin
(Cayman Chemical, 0–150 μM), paclitaxel (Cayman Chemical, 0–20 nM),
vemurafenib (Selleck Chemicals 0–800 nM), AZD6244 (Selleck Chemicals,
0–200 nM) at various concentrations. Cells were incubated for 3 days
and then fixed in 4% paraformaldehyde (Electron Microscopy Sciences)
diluted in PBS for 30 min. After the fixative solution was removed, the
plates were washed with PBS and stained with 0.1% crystal violet
solution for 15 min. The staining solution was removed and the plates
were washed five times and allowed to dry at room temperature. Crystal
violet staining was eluted using 10% glacial acetic acid and the
absorbance at 590 nm was measured using an Envision plate reader
(Perkin Elmer).
Transwell invasion and migration assays
A549 and A375 tumor cells were treated with EVs for 5 days. After
trypsinization, cells were counted and resuspended in serum free media
supplemented with 250 μg/ml BSA (Sigma-Aldrich) (assay media) at a
concentration of 2 × 10^5 cells/ml. 650 μl of media with 10% FBS was
used as the chemoattractant and added to the bottom chamber of cell
culture inserts, and 250 μl of cells in assay media was added to the
top chamber of cell culture inserts. For migration assays, Boyden
chamber inserts (BD Biosciences, 8 μm pore size) were used, and for
invasion assays, BD BioCoat invasion chambers coated with growth factor
reduced Matrigel were used. Invasion chambers were prepared according
to manufacturer instructions. Cells were allowed to migrate and invade
for 24 h, then cells that had migrated to the lower surface of the
membrane were fixed in ethanol and stained with 0.1% crystal violet.
10× images of crystal-violet stained cells were captured using a Nikon
DS-Fi2 camera, and quantifications were carried out in an automated
fashion using Fiji/ImageJ v1.52. In brief, binary images of the area
covered by crystal violet-positive cells was generated using
thresholding and settings that were appropriate for control samples,
and the same settings were used throughout the analysis. The percentage
area covered by crystal violet-positive cells was quantified for each
condition, using a minimum of three technical replicates.
Oxidative stress assays
For short-term ROS quantification, fibroblasts were seeded in 12-well
plates. After 12 h, MMA was added at indicated periods of time. Media
was removed and cells were washed with PBS. Then, media containing
20 μM of 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) (Cayman
Chemical) was added to the cells and incubated for 45 min. Cells were
then washed with PBS, trypsinized and added to an opaque black 96-well
plate. Fluorescence signaling was quantified using an Envision plate
reader (Perkin Elmer) and normalized to total cell number. For
measurement of MDA, cells were prepared and quantified using the
Thiobarbituric Acid Reactive Substances Assay Kit (Cayman Chemical)
according to manufacturer instructions.
Metabolite extraction and mass-spec analysis
Tumor cells were seeded in 10 cm dishes and infected with shMUT virus
for 3 days. Cells were washed with cold PBS and incubated with 5 ml FBS
free DMEM to collect conditioned medium. 48 h later, the conditioned
medium was collected and the tumor cells were counted. 4 volumes of
100% methanol were added to 1 volume of CM, then subjected to speed
vacuum for about 4 h until the solution was fully evaporated. The
pellets were resuspended in 10 µL of 50% MeOH (in H2O). Then, the
mixture was spun down, and 10 µL of supernatant was mixed with 50 µL
short-chain fatty acids derivatization solution. The resulting mixture
was vortexed and incubated at 60 °C for 1 h, then the mixture was
centrifuged at 21,000 × g for 20 min, and the supernatant was analyzed
using an Agilent 1290 LC system coupled to an Agilent 6530 quadrupole
time-of-flight mass spectrometer with a 130 Å, 1.7 μm, 2.1 mm × 100 mm
ACQUITY UPLC BEH C18 column (Waters). We used the following solvent
system: A: H2O with 0.1% formic acid; B: Methanol with 0.1% formic
acid. 20 µL of each sample was injected, and the flow rate was
0.35 mL/min with a column temperature of 40 °C. The gradient for
HPLC-MS analysis was: 0–6.0 min, 99.5–70.0% A; 6.0–9.0 min, 70.0–2.0%
A; 9.0–9.4 min, 2.0% A; 9.4–9.6 min, 2.0–99.5% A. Peaks were assigned
by comparison with authentic standards. Relative metabolite amounts
were normalized to cell number.
shRNA gene silencing
shMUT #1 (TRCN0000049038), shMUT #2 (TRCN0000049040), shIL6 #1
(TRCN0000059203), shIL6#2 (TRCN0000372668), shIL6R #1 (TRCN0000058779),
shIL6R #2 (TRCN0000058780), shGFP (TRCN0000072181), shPCCA #1
(TRCN0000078424), shPCCA #2 (TRCN0000078423), shCHUK #1
(TRCN0000194782), shCHUK #2 (TRCN0000199496), shTGFBR1 #1
(TRCN0000194693), shTGFBR1 #2 (TRCN0000196293) (all from Sigma Aldrich)
lentiviruses were produced by co-transfection of HEK293T cells with
plasmids encoding psPAX2 (Addgene plasmid 12260) and pMD2.G (Addgene
plasmid 12259) using X-tremeGene HP (Roche) in accordance with the
manufacturer’s protocol. Medium was changed 16 h post-transfection and
the virus harvested over 72 h, filtered, and used to infect fibroblasts
and tumor cells with 8 μg/ml polybrene (Sigma-Aldrich). Selection of
resistant colonies was initiated 24 h after selection using 2 μg/ml of
puromycin (Sigma-Aldrich).
qPCR
RNA was extracted from cells using the Ambion PureLink RNA Mini Kit
(Life Technologies) according to manufacturer’s instructions and
treated with DNase I (Amplification grade, Sigma-Aldrich). cDNA was
synthesized using the iSCRIPT cDNA synthesis kit (BioRad) and analyzed
by qPCR using SYBR green master mix (Life Technologies) on a
QuantStudio6 Real-Time PCR system with QuantStudio Real-Time PCR
software v1.3 (Life Technologies). Exported data were further processed
in Excel for Office 365. Target gene expression was normalized to
expression of TBP and ACTB. Primer sequences are as follows: IL6
forward: ACTCACCTCTTCAGAACGAATTG, IL6 reverse: CCATCTTTGGAAGGTTCAGGTTG.
IL6R forward: CCCCTCAGCAATGTTGTTTGT, IL6R reverse: CTCCGGGACTGCTAACTGG.
Global gene expression analysis (RNA-seq)
RNA was extracted from cells using the Ambion PureLink RNA Mini Kit
(Life Technologies) according to manufacturer’s instructions and
treated with DNase I (Amplification grade, Sigma-Aldrich). Total RNA
integrity was checked using a 2100 Bioanalyzer (Agilent Technologies,
Santa Clara, CA). RNA concentrations were measured using the Nanodrop
system (Thermo Fisher Scientific, Inc., Waltham, MA). Preparation of
RNA sample library and RNA-seq were performed by the Genomics Core
Laboratory at Weill Cornell Medicine. RNA was prepared using TruSeq
Stranded mRNA Sample Library Preparation kit (Illumina, San Diego, CA),
according to the manufacturer’s instructions. The normalized cDNA
libraries were pooled and sequenced on Illumina NovaSeq6000 sequencer
with pair-end 50 cycles. The sequencing libraries sequenced with
paired-end 50 bps on NovaSeq6000 sequencer. The raw sequencing reads in
BCL format were processed through bcl2fastq 2.19 (Illumina) for FASTQ
conversion and demultiplexing. After trimming the adaptors with
Cutadapt (version1.18)([175]https://cutadapt.readthedocs.io/en/v1.18/),
RNA reads were aligned and mapped to the GRCh38 human reference genome
by STAR (Version2.5.2)
([176]https://github.com/alexdobin/STAR)^[177]42, and transcriptome
reconstruction was performed by Cufflinks (Version 2.1.1)
([178]http://cole-trapnell-lab.github.io/cufflinks/). The abundance of
transcripts was measured with Cufflinks in Fragments Per Kilobase of
exon model per Million mapped reads (FPKM)^[179]43,[180]44. Gene
expression profiles were constructed for differential expression,
cluster, and principle component analyses with the DESeq2 package
([181]https://bioconductor.org/packages/release/bioc/html/DESeq2.html)^
[182]45. For differential expression analysis, pairwise comparisons
between two or more groups using parametric tests where read-counts
follow a negative binomial distribution with a gene-specific dispersion
parameter. Corrected p values were calculated based on the
Benjamini-Hochberg method to adjusted for multiple testing.
Subcutaneous injections and metastasis formation assay in mice
Tumor cells were infected with a EF1A-Luciferase-p2A-GFP vector prior
to EV treatments. Cells were trypsinized and resuspended on ice in a
1:1 of PBS: Matrigel mixture. Female nu/nu athymic mice were
anesthetized with isoflurane and injected with 500,000 A549 cells,
10,000 A375 cells or a mixture of 100,000 A549 and 400,000 MRC5 cells,
or 1,000,000 A375 cells with different mutation (for IHC) in 100 ul
subcutaneously into the left flank. Primary tumor growth was monitored
by imaging weekly using the IVIS Spectrum CT Pre-Clinical In Vivo
Imaging System (Perkin-Elmer), and luminescence was measured and
quantified using Living Image Software v.4.5 (Perkin-Elmer). To
visualize metastatic spread, mice were sacrificed at the endpoint of 5
weeks and organs, including livers, lungs, brains, spleens and kidneys,
were placed into 12-well plates and imaged using the IVIS Spectrum CT
Pre-Clinical In Vivo Imaging System (Perkin-Elmer), and luminescence
was measured and quantified using Living Image Software v.4.5
(Perkin-Elmer).
Statistics and reproducibility
All measurements used for statistical analyses in independent
experiments were taken from distinct samples. Data analyses were
performed using Microsoft Excel 2013 and GraphPad Prism7. Unless
otherwise specified, results are expressed as mean ± SEM. The
two-tailed Student’s t-test, one-way ANOVA and two-way ANOVA were used
to determine significance. For all western blots, experiments were
independently repeated n = 3 times and representative images are shown.
Sample sizes for mouse experiments were chosen based on power
calculations using expected results. No data were excluded from the
analyses. Mice and cells were randomized before allocation into
different experimental groups. The investigators were not blinded to
allocation during experiments and outcome assessment.
Reporting summary
Further information on research design is available in the [183]Nature
Research Reporting Summary linked to this article.
Supplementary information
[184]Supplementary information^ (1.4MB, pdf)
[185]Peer Review File^ (4.2MB, pdf)
[186]Reporting Summary^ (1.7MB, pdf)
Acknowledgements