Abstract
Metabolic profiling of cell line collections has become an invaluable
tool to study disease etiology, drug modes of action and to select
personalized treatments. However, large-scale in vitro dynamic
metabolic profiling is limited by time-consuming sampling and complex
measurement procedures. By adapting a mass spectrometry-based
metabolomics workflow for high-throughput profiling of diverse adherent
mammalian cells, we establish a framework for the rapid measurement and
analysis of drug-induced dynamic changes in intracellular metabolites.
This methodology is scalable to large compound libraries and is here
applied to study the mechanism underlying the toxic effect of
dichloroacetate in ovarian cancer cell lines. System-level analysis of
the metabolic responses revealed a key and unexpected role of CoA
biosynthesis in dichloroacetate toxicity and the more general
importance of CoA homeostasis across diverse human cell lines. The
herein-proposed strategy for high-content drug metabolic profiling is
complementary to other molecular profiling techniques, opening new
scientific and drug-discovery opportunities.
__________________________________________________________________
Sébastien Dubuis et al. investigated a dynamic metabolic adaptation of
five ovarian cancer cells to an anti-cancer drug dichloroacetate. This
study finds an unexpected role of coenzyme A metabolism in mediating
the toxicity of dichloroacetate, illustrating the power of metabolic
drug profiling.
Introduction
A major bottleneck in drug discovery pipelines is the lack of
mechanistic information on the primary targets and downstream secondary
effects of selected lead compounds. Large-scale approaches enabling the
characterization of cell responses to external perturbations have
therefore turned into highly relevant technologies in drug discovery
and development^[28]1–[29]4. Among these approaches, the profiling of
drug-induced changes in model organisms at the mRNA and protein
level^[30]5,[31]6 has provided invaluable insights into drug modes of
action (MoA)^[32]7–[33]9, drug–drug interaction mechanisms^[34]10 and
drug repurposing^[35]2,[36]11. Conceptually similar to transcriptomics
and proteomics platforms, metabolomics provides an orthogonal
multi-parametric readout aiming at quantifying the full spectrum of
small molecules in the cell, the so-called metabolome. Applied to drug
discovery research, metabolome profiling of drug-perturbed cell lines
in vitro was key in revealing drug modes of action and in identifying
potential weaknesses in cellular drug response, as well as genetic
polymorphisms associated with drug susceptibility^[37]12–[38]19.
Metabolomics-based approaches have a notable advantage over existing
functional genomics platforms in that they enable an unparalleled
throughput^[39]20,[40]21. However, despite significant advancements in
high-resolution mass-spectrometry (MS) profiling of cellular
samples^[41]21–[42]23, efficient experimental and computational
workflows for large-scale dynamic metabolome profiling in mammalian
cells in vitro are lagging behind. Metabolome screenings that adopt
classical metabolomics techniques^[43]24,[44]25 are often hampered by a
limited throughput, laborious sample preparation and the lack of
rigorous, yet simple, data analysis pipelines to interpret dynamic
metabolome profiles. To address these limitations, our group developed
a high-throughput and robust method to perform large-scale metabolic
profiling in adherent mammalian cells at steady state^[45]26, using a
96-well plate cultivation format combined with time-lapse microscopy
and flow-injection time-of-flight mass spectrometry^[46]23 (TOFMS).
Here, we extend this methodology to allow rapid sample collection and
the analysis of dynamic changes in the intracellular metabolome of
diverse mammalian cell lines upon external perturbations. We applied
this methodology to profile the diversity of metabolic adaptive
responses in five ovarian cancer cell lines to the potential
anti-cancer drug dichloroacetate (DCA), and shed light on its mode of
action.
The presented framework for in vitro large-scale dynamic metabolomics
of perturbed adherent mammalian cell lines is complementary to and
scales with high-throughput growth-based phenotypic screens of large
compound libraries. Moreover, we provide a proof of principle that our
approach can generate testable predictions to elucidate the origin of
drug response variability and drug modes of action. Such a platform may
complement and improve the translational value of classical in vitro
phenotype-based drug screenings^[47]21,[48]27, and provide insights
into the mechanisms of action of small molecules facilitating early
stages of drug discovery^[49]28–[50]30.
Results
High-throughput dynamic metabolome profiling of drug action
Large-scale metabolic profiling of transient drug responses among
diverse cell types necessitates new methodologies enabling parallelized
and rapid sample collection, high-throughput metabolome profiling and
an effective normalization approach for metabolomics data. Here, we
developed a combined experimental–computational approach enabling the
rapid profiling of drug-induced dynamic changes in the baseline
metabolic profile of diverse cell lines in parallel. This approach was
applied here to study the metabolic responses of five ovarian cancer
cell lines to DCA, an activator of pyruvate dehydrogenase (PDH).
The five ovarian cell lines IGROV1, OVCAR3, OVCAR4, OVCAR8, and SKOV3
were grown in parallel in 96-well plates for 4 days. Cells were exposed
to the corresponding drug dose yielding 50% growth inhibition
(GI[50],Table [51]1) and metabolomics samples were collected every 24 h
following the extraction protocol described in ref. ^[52]26 and
summarized in Supplementary Figure [53]1. In the present study, nine
replicate plates were prepared: one plate served to continuously
monitor cell growth via cell confluence by time-lapse microscopy using
an automated multi-well plate reader (Fig. [54]1), while the remaining
plates were used for metabolome extraction immediately before, and at
24, 48, 72 and 96 h after drug exposure (Supplementary Figure [55]1).
At each sampling time point, one plate was used to generate cell
extract samples, while the second plate served to determine extracted
cell numbers per well using bright-field microscopy^[56]26. Cell
extract samples were profiled by flow injection analysis (FIA) and
TOFMS (FIA–TOFMS) as described previously^[57]23, enabling
high-throughput analysis of large sample collections. The detected ions
were annotated based purely on the accurate mass, and by assuming that
deprotonation is the most frequent and reliable form of ionization in
negative mode. By matching measured m/z against calculated monoisotopic
masses of metabolites listed in the Human Metabolome Database
(HMDB^[58]31) and in the genome-scale reconstruction of human
metabolism (Recon 2^[59]32), we putatively annotated 2482 ions
(Supplementary Data [60]1). Importantly, in absence of prior
chromatographic separation, FIA-TOFMS cannot distinguish isobaric
metabolites, as well as in-source fragments that are detected at the
identical exact mass.
Table 1.
Drug concentrations used in metabolomics experiments
Cell line Oxamate dose (mM) Dichloroacetate dose (mM)
IGROV1 13 11
OVCAR3 40 12
OVCAR4 18 25
OVCAR8 6.7 25
SKOV3 31 25
[61]Open in a new tab
The given concentrations correspond to the effective concentration in
the medium
Fig. 1.
[62]Fig. 1
[63]Open in a new tab
Growth of ovarian cancer cell lines upon drug perturbation. Cell
confluence measured by time-lapse microscopy during growth of five
ovarian cancer cell lines in RPMI-1640 medium (i.e. untreated condition
in gray), and upon dichloroacetate (orange) and oxamate (green)
treatments (Supplementary Figure [64]11). Cell confluence is reported
as mean ± SD across three replicates
To estimate time-dependent (e.g. drug-induced) changes in intracellular
metabolite abundances from non-targeted metabolomics data, we here
developed a regression-based analysis approach to compare transient
changes in the metabolome of drug-treated cells against steady-state
unperturbed cell metabolic profiles (Supplementary Figure [65]2)
determined following the approach described in ref. ^[66]26 and here
briefly summarized.
By definition, the intracellular concentrations of metabolites at
steady state are constant in time. Hence, in samples from unperturbed
growing cells for each metabolite i in cell line j, measured
intensities, I[j,i], scale proportionally with the metabolite abundance
in the cell [m[i]], times the extracted cell number (N[c], derived from
bright-field microscopy^[67]26, see Supplementary Figure [68]3):
[MATH:
Ij,i∝Nc⋅mij :MATH]
1
Hence, we can model the measured metabolite intensities in a given cell
line as follows:
[MATH:
Ij,i=αj,i⋅N
c+βi, :MATH]
2
where β[i] is an offset value corresponding to the experimental MS
background signal, and α[j,i] represents the abundance of metabolite i
per cell. Notably, α[j,i] contains an unknown scaling factor that is
reflective of the fundamental proportionality between metabolite
concentration and MS signal intensity. For each metabolite, we use a
multiple regression scheme to fit the linear model and regress the cell
line-specific α values and the offset β across all cell lines at once
(Supplementary Figure [69]4). To assess the reliability of the
parameter estimates, each fitted parameter is associated with a p-value
(F statistic with the hypothesis that the coefficient is equal to
zero). It is worth noting that this procedure allows systematically
filtering out annotated ions which are unlikely to originate from
extracted cells because the measured ion intensity does not exhibit any
dependency with the cell number, as well as ions for which the measured
intensities are below the detection limit, and the estimated cell
line-specific α values are below or close to 0. Out of the 2482 ions
annotated in the ovarian cancer cell dataset, we obtained relevant
parameter estimates for 1546 putatively annotated ions, i.e. α > 0 and
p ≤ 0.001 in at least one cell line, with a median coefficient of
variation of 17.4% (Supplementary Figure [70]5).
To evaluate dynamic metabolite changes upon an external perturbation,
we calculate time-dependent fold-change values for each metabolite
based on the parametrized model derived from steady-state unperturbed
metabolome profiles. For each metabolite i and time point t after
exposure to treatment D, we estimate the deviation of metabolite
abundance from the unperturbed steady-state condition as follows:
[MATH: FCt,i
D,j=<
msub>log2I
t,iD,jα
j,i⋅Nj,t+βi
:MATH]
3
where
[MATH:
It,iD,j :MATH]
is the intensity measured for metabolite i at time t after exposure of
cell line j to compound D.
[MATH:
It,iD,j :MATH]
is compared to the corresponding theoretical steady-state unperturbed
metabolite intensity (denominator in Eq. ([71]3)) which is calculated
from the previously estimated α and β parameters and the extracted cell
number at the time of sampling, N[j,t], derived from bright-field
microscopy images (Supplementary Figures [72]1 and [73]3). For each
time point, the difference between measured and expected metabolite
intensities is expressed in log[2] fold-changes, and significance is
quantified by means of p-values from t-test analysis. In the following,
our metabolome profiling pipeline was applied to investigate the
metabolic response to the small-molecule agent DCA (Fig. [74]2a and
[75]b), in the five ovarian cancer cell lines IGROV1, OVCAR3, OVCAR4,
OVCAR8, and SKOV3.
Fig. 2.
[76]Fig. 2
[77]Open in a new tab
Analysis of transient dynamic metabolic changes upon external
perturbation. a and b Schematic representation of the enzymatic
reactions targeted by oxamate and dichloroacetate, and volcano plots
summarizing overall metabolome changes. Each dot corresponds to a
metabolite. The negative log[10] of the product between minimum
p-values over the time course across the five cell lines is plotted
against the median of maximum fold-changes. Metabolites highlighted in
red have an absolute log[2] fold-change >1 and a p-value ≤ 1e−10. c
KEGG pathway enrichment of metabolites consistently affected by
dichloroacetate and oxamate treatments, highlighted in panel b. Only
enriched pathways with a significance corrected for multiple tests
q-value (Storey) ≤ 0.001 are considered. d Time-dependent fold changes
(red line) of lactate upon oxamate treatment, and pyruvate upon oxamate
and dichloroacetate treatments. Data are the mean ± SD of three
replicates. The profiles of all other detected metabolites are shown in
gray
Interplay of DCA MoA and coenzyme A (CoA) metabolism
DCA is a mitochondria-targeting small molecule that activates PDH by
inhibiting pyruvate dehydrogenase kinase (PDK)^[78]33. By blocking PDH
phosphorylation, DCA favors increased flux of pyruvate into
mitochondria^[79]34. To date, the exact mechanism by which DCA is toxic
to cancer cells has remained unclear^[80]35. Recent studies suggested
that the activation of PDH diverts metabolism from fermentative
glycolysis to oxidative phosphorylation, leading to a loss in
mitochondrial membrane potential and a reopening of voltage-sensitive
and redox-sensitive mitochondrial transition pores, which ultimately
triggers an apoptotic cascade in cancer cells^[81]33.
Despite large differences in doubling times (Supplementary
Figure [82]6), degrees of invasiveness^[83]36 and metabolic
phenotypes^[84]37 (Supplementary Figure [85]7), the five selected
ovarian cancer lines exhibited common metabolic adaptive changes to DCA
exposure. In our dynamic metabolome data, we observed a consistent
reduction in pyruvate levels across all cell lines upon DCA treatment
(Fig. [86]2d), and a reduction in lactate secretion (Supplementary
Figure [87]8). Strikingly, the most significant metabolic change
(p-value 5.7e−43) across all five cell lines was a marked depletion of
intracellular pantothenate (Figs. [88]2a and [89]3a), which was
additionally confirmed by quantitative LC–MS/MS measurements
(Supplementary Figure [90]9). While pyruvate depletion and reduced
lactate secretion are likely direct consequences of PDH activation, a
depletion of pantothenate and a concomitant increase in the total pools
of CoA (Fig. [91]3a and Supplementary Figure [92]10) hints at an
unexpected activation of de novo CoA biosynthesis.
Fig. 3.
[93]Fig. 3
[94]Open in a new tab
Influence of CoA metabolism on dichloroacetate action. a Schematic
representation of the CoA biosynthetic pathway. Pantothenate kinases
(PANK) catalyze the rate-limiting step in CoA biosynthesis^[95]39. For
each pathway intermediate, relative steady-state (SS) abundances in the
untreated condition (right-hand side) and dynamic changes upon
dichloroacetate treatment (left-hand side) are shown. Steady-state
levels of pathway intermediates are represented as the distribution of
residuals in the linear fitting of raw MS measurements multiplied by
the inferred cell line specific α values. b Determined GI[50]
concentrations for oxamate and dichloroacetate across cell lines
(Supplementary Figure [96]11). c IGROV1 and SKOV3 cells were grown in
RPMI-1640 medium before addition of perturbing agents and continuous
confluence monitoring for ∼5 days. Six conditions were tested: normal
RPMI-1640 medium (Control), addition of 2.1 µM pantothenate, with and
without 11 mM (IGROV1) or 25 mM (SKOV3) of dichloroacetate
(Pan/Pan + DCA), addition of 100 µM CoA with and without 13 mM (IGROV1)
or 31 mM (SKOV3) of dichloroacetate (CoA/CoA + DCA). d Schematic
representation of CoA metabolism. CoA plays a central role in energy
and fatty acid metabolism, acting as an acyl group carrier to form
acetyl-CoA and other important compounds, such as fatty acids,
cholesterol, and acetylcholine. PANK2, the first and rate-limiting
metabolic enzyme in the CoA biosynthetic pathway, is allosterically
regulated^[97]38,[98]42 and localizes in the mitochondrial
inter-membrane space^[99]41,[100]42. CoA is produced in the cytosol and
subsequently actively transported into the mitochondrial matrix.
Alternatively, can access CoA from the extracellular environment thanks
to the action of extracellular ectonucleotide pyrophosphatases
contained in the serum^[101]70. These enzymes cleave the CoA molecule
to form 4’-phosphopantetheine, which can enter the cells one enzymatic
step above CoA formation by COASY
Pantothenate is the primary precursor required for CoA biosynthesis.
CoA in turn regulates its own biosynthesis via allosteric inhibition of
the first enzymatic step in the pathway, catalyzed by mitochondrial
pantothenate kinase 2 (PANK2)^[102]38,[103]39 (Fig. [104]3a, d). While
human PANK2 locates in the inner mitochondrial
membrane^[105]40,[106]41, the remaining CoA biosynthetic steps take
place in the cytoplasm. Notably, CoA pools in the different cellular
compartments are tightly regulated, such that typical CoA
concentrations are 1–2 orders of magnitude higher in mitochondria
(∼2–5 mM) than in the mitochondrial intermembrane space and the cytosol
(∼0.02–0.14 mM)^[107]42,[108]43. We hypothesize that a hyper-activation
of PDH in the mitochondrial matrix could entail a depletion of CoA in
the immediate surroundings of PANK2, hence lifting allosteric
inhibition of de novo CoA biosynthesis. In such a scenario, our
observations are consistent with an attempt of DCA-treated cells to
re-equilibrate CoA levels across compartments by increasing CoA
biosynthesis (Fig. [109]3a). According to this model, the resulting
increased pantothenate phosphorylation would explain the observed
depletion of intracellular pantothenate, and the parallel accumulation
of total CoA in the cells^[110]44 (Fig. [111]3a). Moreover, we observed
the largest amount of intracellular pantothenate at steady state
(Fig. [112]3a) in the two cell lines with the highest sensitivity (i.e.
lowest GI[50]) to DCA, OVCAR3 and IGROV1 (Fig. [113]3b and
Supplementary Figures [114]11 and [115]16), supporting a functional
association of CoA metabolism with the MoA of DCA.
To test our hypothesis, we selected IGROV1 and SKOV3 cells, which
exhibit different steady-state levels of pantothenate and a distinctly
different sensitivity to DCA (Fig. [116]3b). We monitored the growth of
IGROV1 and SKOV3 cells upon DCA treatment, with and without
supplementing the medium with 2.1 µM pantothenate or 100 µM CoA
(Fig. [117]3c). We found that increasing extracellular pantothenate
concentration strongly aggravated the toxicity of DCA in both cell
lines (Fig. [118]3c), while being neutral for cells in normal RPMI-1640
medium (containing 0.25 µM pantothenate). Surprisingly, even in the
absence of DCA, supplementing CoA to the medium had a strong toxic
effect on cells. Cells supplemented with 100 or 500 µM CoA
(Fig. [119]3c and Supplementary Figure [120]12) exhibited a first phase
of normal growth, followed by rapid growth arrest (Fig. [121]3c).
Interestingly, supplementation of CoA completely masked DCA toxicity
when co-administered (Fig. [122]3c and Supplementary Figure [123]12).
The synergistic effect of pantothenate with DCA, and the antagonistic
interaction of DCA with CoA reinforce our premise of a functional
interplay between CoA metabolism and cell growth inhibition caused by
DCA (Fig. [124]3d). Because CoA biosynthesis is regulated (i.e.
repressed) immediately downstream of pantothenate (Fig. [125]3a), CoA
levels can be controlled in spite of high pantothenate concentrations.
Hence, supplementing pantothenate to the medium has no toxic effect to
cells (red curve in Fig. [126]3c). However, when cells are additionally
challenged with DCA, CoA biosynthesis is activated and higher levels of
pantothenate can lead to higher CoA biosynthetic flux (orange curve in
Fig. [127]3c). To verify our conclusions, we monitored metabolome
changes in IGROV1 cells upon addition of 2.1 µM pantothenate or 200 µM
CoA. Consistent with our expectations, supplemented pantothenate is
internalized but metabolism is otherwise unperturbed, while CoA
addition induces pleiotropic changes in intracellular metabolite
abundances, indicating a large deviation from metabolic steady state
(Supplementary Figure [128]13). We concluded that by directly providing
CoA extracellularly we bypassed the main control mechanism for CoA
homeostasis and in turn impaired cell growth.
Model-based analysis of DCA MoA and CoA toxicity
We next asked whether the observed growth inhibitory effect of CoA was
restricted only to ovarian cancer cell lines. To this end, we tested
the effect of CoA on seven additional cancer cell lines from different
tissue types, and one non-cancer cell line (HEK293 kidney cells).
Despite distinct differences in sensitivity, all cell lines exhibited
growth reduction upon supplementation of culture media with 100 µM CoA
(Fig. [129]4). To better understand the interplay between
biosynthesis/utilization of CoA and DCA toxicity, we created a minimal
kinetic model that consists of only two reactions following
Michaelis–Menten kinetics: biosynthesis of CoA (Eq. ([130]4)) and CoA
utilization for biomass production (Eq. ([131]5), Fig. [132]5a),
assuming a growth inhibitory activity of CoA:
[MATH: vCoA=v<
/mi>CoAmax⋅DCADCA+KDCA+1 :MATH]
4
[MATH: vbiomass=vbiomassmax⋅CoACoA+KCoA⋅1+CoA3Ki :MATH]
5
where v[CoA,max] and v[biomass,max] represent the corresponding maximum
flux capacities, [DCA] and [CoA] are the concentrations of DCA and CoA,
respectively, K[DCA] and K[CoA] are the Michaelis–Menten constants, and
K[i] is the inhibitory constant for CoA. The model assumes that in
unperturbed cells, CoA is not limiting for biomass production (i.e.
[CoA] ≫ K[m]), and that cells are ultrasensitive to high levels of CoA,
which in turn inhibit biomass production (i.e. K[I] ≫ K[m]).
Fig. 4.
[133]Fig. 4
[134]Open in a new tab
CoA toxicity across multiple cell lines. Cell lines from breast,
kidney, cervical and liver tissues were grown with (green) and without
(gray) the addition of 100 µM CoA. Cell confluence is reported as
mean ± SD across three replicates
Fig. 5.
[135]Fig. 5
[136]Open in a new tab
Mechanistic model of CoA-mediated toxicity of dichloroacetate. a
Schematic overview of the minimal model, where CoA production can be
increased by dichloroacetate (DCA), or reduced by hopantenate (HoPan).
CoA is directly used to form new biomass, which in turn CoA can inhibit
by an undefined mechanism. b CoA inhibits growth in a
concentration-dependent manner, while (c) HoPan inhibition of the first
steps in CoA biosynthesis can reduce CoA levels and have an initial
beneficial effect on growth, until CoA biosynthesis becomes
growth-limiting. d On the contrary, DCA inhibits growth (bold red line)
by virtue of increasing CoA levels (dashed red line). Addition of HoPan
initially restores growth (bold blue line) by reducing CoA levels
(dashed blue line). e–h Experimental verification of the model. IGROV1
cells were supplemented with HoPan (e), or increasing concentrations of
DCA, in presence or absence of 1.5 mM HoPan (f–h). Growth is monitored
continuously using cell confluence measurements. Relative confluence
data reported are mean ± standard deviation across three replicate
wells, normalized to the initial confluence, while the kinetic
parameters used for model simulations can be found in the Methods
section
This simple model is able to qualitatively recapitulate the toxic
effect of increasing CoA levels either as a consequence of increased
CoA biosynthesis upon DCA treatment or external addition of CoA
(Fig. [137]5b). In addition, the model can be used to predict the
effect of a reduced CoA biosynthetic flux (e.g. reduced v[CoA,max]). In
such a scenario, the model qualitatively predicts two phases: a first
one where reduction of intracellular CoA levels increases biomass
production, and a second phase in which CoA biosynthesis becomes
limiting for growth (Fig. [138]5c). A qualitatively similar behavior is
expected when CoA biosynthesis is inhibited in the presence of DCA
(Fig. [139]5d).
To test the model predictions, we supplemented IGROV1 cells with
different concentrations of DCA, in the presence or absence of 1.5 mM
hopantenate, an inhibitor of the first enzymatic step in CoA
biosynthesis. In agreement with our minimal model, cells with
hopantenate exhibit an initially higher proliferation rate with respect
to unperturbed cells, before entering in a second phase of reduced
growth (Fig. [140]5e–h). Moreover, in the first phase hopantenate fully
reverted the growth inhibitory effect of DCA, allowing cells to grow at
similar if not higher rates as untreated cells, hence confirming that
enhanced CoA biosynthesis is at the core of the mode of action of DCA.
Overall, these experimental results are consistent with our minimal
model, and emphasize the importance of CoA homeostasis and its role in
mediating DCA toxicity. To our knowledge, this is the first time that a
toxic effect of CoA in mammalian cells has been shown and was linked to
the mode of action of DCA.
Consistent with our in vitro results, in vivo inhibition of PANK in
mice by hopantenate resulted in 167-fold higher expression of
PDK^[141]45. This observation suggests dichotomous compensatory
mechanisms to regulate CoA homeostasis: inhibition of CoA biosynthesis
activates PDK, which in turn represses PDH^[142]45, while inhibition of
PDK by DCA has the opposite effect, and promotes CoA biosynthesis. The
resulting over-induction of cytosolic de novo CoA biosynthesis can in
turn aggravate DCA toxicity. It is important to note that the mechanism
by which enhanced CoA biosynthesis inhibits growth remains unclear. It
is possible that rather than CoA directly, it is the accumulation of an
intermediary product of CoA metabolism or the hyper-utilization of
CoA^[143]46,[144]47 that becomes toxic to cells. To test whether the
observed adaptive response to DCA was an indirect effect associated
with a general stress response upon growth inhibition and/or reduced
lactate secretion, we tested the effect of oxamate, a small molecule
that inhibits the conversion of pyruvate into lactate.
Oxamate elicits different metabolic responses
Oxamate is a competitive inhibitor of lactate dehydrogenase (LDHA) with
respect to pyruvate^[145]48 (Fig. [146]2b). A growing body of evidence
indicates that oxamate induces apoptosis exclusively in cancer
cells^[147]48,[148]49. According to current theory, the inhibition of
lactate production, together with typically high glycolytic rates in
cancer cells, causes an over-production of toxic superoxide by the
mitochondrial electron transport chain. Since both drugs decrease
lactate secretion rates (Supplementary Figure [149]8), oxamate
treatment could lead to similar metabolic adaptive mechanisms as DCA.
In our dynamic metabolome profiling data, we observed a significant
accumulation of intermediates in TCA cycle upon oxamate treatment, and
a concomitant reduction of intracellular ATP levels, in accordance with
previous findings^[150]50. In particular, we observed a consistent and
large accumulation of sorbitol and sedoheptulose 7-phosphate
(Fig. [151]2b). Overall, we found that the significant metabolic
changes common to all cell lines locate in central metabolic pathways
like oxidative phosphorylation and nucleotide metabolism
(Fig. [152]2c). Taken together, changes induced by oxamate were largely
different from those induced by DCA (Fig. [153]3a–c, Supplementary
Figure [154]14), suggesting for radically different metabolic adaptive
strategies. Unlike PDH activation by DCA, inhibition of LDHA seems to
redirect intermediates in upper glycolysis to other pathways, such as
NADPH-dependent reduction of glucose to form sorbitol, or the pentose
phosphate pathway (as indicated by accumulation of sedoheptulose
7-phosphate, Fig. [155]2b). Both metabolic responses are known to
counteract oxidative stress^[156]51,[157]52. Interestingly, we also
observed a marked reduction in the levels of N-acetylaspartic acid
(Fig. [158]2b), a potent oxidative stress agent^[159]53 associated with
poor prognosis in ovarian cancer^[160]54. The marked differences
between metabolic adaptive responses to oxamate and to DCA reinforce
our previous observation of a selective functional link between CoA
metabolism and the mode of action of DCA.
Discussion
In this study, we present a novel experimental and computational
workflow for high-content dynamic metabolome profiling that enables a
systematic and high-throughput investigation of dynamic changes in the
intracellular metabolism of adherent mammalian cells upon environmental
perturbation. Our methodology provides a novel way to perform
high-throughput dynamic metabolic screens in adherent cell lines,
facilitated by a miniaturized parallel 96-well cultivation system, a
simple and rapid metabolite extraction procedure and automated
time-lapse microscopy^[161]26. We additionally exploit the unique
throughput advantages of flow-injection high-resolution MS-based
metabolomics which has become an invaluable tool^[162]55–[163]58 for
exploratory studies and the profiling of large sample collections.
Altogether, our methodology offers new scientific and clinical
opportunities for large-scale in vitro exploratory metabolome drug
screenings and a complementary tool to more targeted
approaches^[164]59. Of note, as compared to more conventional
LC–MS-based approaches, even moderately sized studies can benefit from
our exploratory methodology, given the extended metabolic and chemical
space covered, the reduced complexity of sample preparation, the rapid
measurement and the automatized acquisition of normalization
parameters.
A major challenge common to many high-content screenings, and
particularly relevant for non-targeted approaches, is the computational
analysis of large datasets for the generation of testable predictions.
Here, we implemented a systematic data processing and analysis pipeline
that allows comprehensively interpreting dynamic metabolic profiles and
extracting the most informative features (Supplementary Figure [165]2).
While changes in metabolite abundance do not necessarily correspond to
changes in conversion rates (i.e. fluxes), altered metabolite pools can
be reflective of functional changes in the cell^[166]60. By
investigating the dynamic responses to the perturbing agents DCA and
oxamate, we proposed a previously undescribed role of CoA metabolism in
mediating the toxicity of DCA. Therapeutically, high dosages of DCA are
needed in order to effectively suppress tumor growth^[167]35, limiting
further development and usage of this compound in clinics.
Nevertheless, our results suggest that compounds affecting CoA
production are likely to exhibit strong epistatic interactions with
DCA. In light of the promising initial evidence that we provided here,
this possibility warrants more attention in future studies.
We have shown here that our experimental and computational framework
for high-throughput drug metabolome profiling can provide key insights
into the cellular response to bioactive compounds. As such, this
technique can become a powerful complementary tool to aid lead
selection at early stages of drug discovery, and to predict compound
modes of action^[168]61, similar to approaches exploiting large
compendia of cellular gene expression profiles^[169]19. For instance,
comparative analysis can reveal uncharacterized compounds featuring
metabolic responses similar to drugs with known molecular
targets^[170]2. Our proof-of-principle example illustrates how in vitro
high-content metabolic drug profiling can provide a first
coarse-grained characterization of a compound mode of action and guide
the design of follow-up experiments in clinically relevant models,
aiming at a mechanistic understanding of drug action. Despite the
difficulty in translating the relevance of in vitro phenotypes into in
vivo outcomes^[171]62, we envisage that this approach can be applied to
the profiling of large sets of bioactive compounds^[172]28 in a large
cohort of cell lines^[173]63,[174]64. In such a setting, this
methodology can potentially deliver invaluable insights to highlight
mechanistic biomarkers to be tested in vivo, to resolve the
functionality of genetic variations, and to understand the interplay
between the drug mode of action and intrinsic cell-to-cell tolerance
variability.
Methods
Cell cultivation
The ovarian cancer cell lines IGROV1, OVCAR3, OVCAR4, OVCAR8, and SKOV3
were obtained from the National Cancer Institute (NCI, Bethesda, MD,
USA) and maintained according to standard protocols at 37 °C with 5%
CO[2] in RPMI-1640 (Biological Industries, cat. no. 01-101-1A)
supplemented with 2 mM l-glutamine (Gibco, cat. no. 25030024), 2 g/L
d-glucose (Sigma Aldrich, cat. no. G8644), 100 U/mL
penicillin/streptomycin (Gibco, cat. no. 15140122), and 5% fetal bovine
serum (FBS, Sigma Aldrich, cat. no. F6178). After thawing, the cells
were expanded in standard cell culture flasks (Nunc T75, Thermo
Scientific). After one week, the cells were transferred to fresh medium
where FBS was replaced by dialyzed FBS (Sigma Aldrich, cat. no. F0392)
in order to facilitate metabolite quantification. Cells were maintained
in this medium with dialyzed FBS for the remaining duration of the
experiment. Three of the cell lines (IGROV1, OVCAR3, OVCAR4) were
exemplarily tested for mycoplasma contamination, and were confirmed
mycoplasma-free. Overall, the cell lines were expanded in T75 for a
total of 3 weeks from thawing until perturbation and metabolomics
experiments. With the aim of determining the starting cell density for
the experiment, a preliminary cultivation in 96-well cell culture
plates was done one week prior to the experiment. To this end, for each
cell line, 150 µL of eight different dilutions containing different
starting cell numbers were plated in triplicate in a 96-well plate.
After 72 h, all wells were imaged using a Spark™ 10M (TECAN) and
confluence was determined in each well. The optimal starting cell
density was subsequently calculated so as to obtain 80% confluence
after 72 h.
Cell growth and segmentation
All procedures for cell growth monitoring and image analysis were
adopted from ref. ^[175]29, and are here briefly summarized. A TECAN
Spark 10M plate reader was used to monitor live adherent cell cultures
directly in the 96-well culture plate. The choice of image acquisition
frequency depends on how fast are the expected growth dynamic changes.
Here, we selected a time frequency of 1.5 h as a reasonable tradeoff
between the fastest doubling time among our cell lines (∼20 h) and the
time it takes to acquire the images for a full plate on the TECAN plate
reader (∼30 min). It is worth noting that our procedure can be adapted
to other commercially available plate readers. Full detail on
bright-field image processing and the extraction of cell confluence and
average adherent cell size is described in ref.^[176]26 (MATLAB code
available for download), and is summarized in Supplementary Figure
[177]3.
Perturbation experiments
Sodium oxamate and sodium DCA were obtained from Sigma Aldrich (cat.
no. O2751 and 347795, respectively), and stock solutions of 400 mM
oxamate and 250 mM DCA were prepared in distilled water. To determine
the GI[50] drug concentrations, nine different concentrations of
oxamate and DCA were tested (Supplementary Figure [178]10). For each
cell line, cells were seeded in 135 µL of fresh medium in 96-well
plates according to the previously calculated optimal density. After
24 h, oxamate and DCA, dissolved at different concentrations in 15 µL
of medium, were added to the cells in triplicates. Immediately upon
drug addition, 24, 48, and 72 h after drug exposure, all wells were
imaged using a Spark™ 10M (TECAN) plate reader, and the cell confluence
was determined. For each condition and cell line, the growth rate was
obtained by fitting an exponential curve to the cell confluence
measurements. After calculating the growth rate reduction relative to
the untreated condition for each drug concentration, and fitting a
sigmoidal curve to the degree of growth inhibition across drug
concentrations (shown in Supplementary Figure [179]10), the GI[50] was
estimated from the fitted curve as the drug concentration causing a 50%
reduction in growth rate (Fig. [180]3b).
Metabolomics experiments
Cell lines were plated in nine 96-well plates according to the optimal
density previously calculated, using 135 µL of medium. To minimize the
effect of evaporation, the outmost rows and columns of the plate were
omitted, and filled with PBS instead. After 24 h, cells were perturbed
with 15 µL of medium containing drug concentrations close to the
respective GI[50] for each cell line. When the calculated GI[50] dose
could not be reached due to limited solubility, the highest
concentration possible was used (400 and 250 mM for oxamate and DCA,
respectively). 15 µL of fresh medium without drug addition were used as
a control. The final concentrations used for each drug and each cell
line are given Table [181]1.
Sample collection and metabolite extraction
The metabolomics sampling procedure was adapted from an experimental
workflow for steady-state metabolome profiling described in ref.
^[182]26, and is here briefly summarized. Samples were collected
immediately before, and at 24, 48, 72, and 96 h after drug addition.
Two replicate 96-well plates were processed at each sampling time point
(plate A and plate B, see also Supplementary Figure [183]1). In plate
A, the cell culture medium was aspirated from all wells using a
multichannel aspirator, and 150 µL of ammonium carbonate (75 mM, pH
7.4, 37 °C) was gently added to each well using a multichannel
dispensing pipet. Immediately after aspiration of the washing solution,
100 µL of cold extraction solvent (40% methanol, 40% acetonitrile, 20%
water, 25 µM phenyl hydrazine^[184]65, −20 °C) were added to each well
using a multichannel pipet. Plates were sealed with aluminum adhesive
to prevent evaporation, immediately transferred to −20 °C for 1 h, and
subsequently stored at −80 °C until further processing. In plate B, the
cell culture medium was aspirated, and the cells were washed with
ammonium carbonate (75 mM, pH 7.4, 37 °C). After aspiration of the
washing solvent, 150 µL of PBS (Gibco, cat. no. 10010015) were added to
each well, and cell confluence was immediately measured in all wells
using a Spark™ 10M (TECAN) plate reader, adopting bright-field
microscopy. The cell confluence from plate B was later used to derive
extracted cell numbers for normalization, using previously determined
average adherent cell sizes (Supplementary Figure [185]3). Before
injection in the mass spectrometer, the 96-well plates were briefly
thawed on ice, and the bottom of all wells was scratched using a
multichannel pipet with wide-bore tips in order to disrupt and detach
all cells from the well bottom. The plates were centrifuged (4 °C, 4000
rcf), and the supernatant was transferred to fresh 96-well plates for
FIA-TOFMS measurements.
FIA-TOFMS analysis
FIA-TOFMS analysis was performed as described in ref. ^[186]23 on an
Agilent 6550 iFunnel Q-TOF LC/MS System (Agilent Technologies, Santa
Clara, CA, USA) equipped with an electrospray ion source operated in
negative ionization mode. In this setup, the samples are injected into
a constant flow of an isopropanol/water mixture (60:40, v/v) buffered
with 5 mM ammonium carbonate at pH 9 using a Gerstel MPS2 autosampler
(5 µL injection volume). Two compounds were added to the solvent for
on-line mass axis correction: 3-amino-1-propanesulfonic acid, (HOT,
138.0230374m/z, Sigma Aldrich, cat. no. A76109) and
hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazine (940.0003763m/z,
HP-0921, Agilent Technologies, Santa Clara, CA, USA). The ion source
parameters were set as follows: 325 °C source temperature, 5 L/min
drying gas, 30 psig nebulizer pressure, 175 V fragmentor voltage, 65 V
skimmer voltage, 750 V octopole voltage. The TOF detector was operated
in 4 GHz high-resolution mode with a spectral acquisition rate of 1.4
spectra per second. Mass spectra were recorded in the mass range
50–1000m/z. Alignment of MS profiles and picking of centroid ion masses
were performed using an in-house data processing environment in Matlab
R2015b (The Mathworks, Natick)^[187]23.
Ion annotation
The ion annotation process is based on a list of known metabolites,
compiled from the HMDB^[188]34 and the Recon2 genome-scale
reconstruction of human metabolism^[189]32. In order to allow
annotation of α-keto acid derivatives formed in presence of phenyl
hydrazine^[190]65 in the extraction solvent, we added the sum formulae
for the phenylhydrazones (+C[6]H[8]N[2] −H[2]O) of a total of 30 α-keto
acid compounds (selected via KEGG SimComp search
[191]http://www.genome.jp/tools/simcomp/) to the metabolite list for
annotation. The monoisotopic mass is calculated for each of the listed
metabolites based on its sum formula. A list of expected ion masses
corresponding to the listed metabolites is subsequently generated,
considering only ionization by deprotonation (−H+) in negative mode
electrospray ionization. Subsequently, these theoretical ion masses are
searched against the detected ion mass-to-charge ratios (m/z) within a
tolerance of 0.003 amu. The final list of annotated ions is compiled
considering the best metabolite match (i.e. smallest difference to the
expected mass) for each ion.
Data processing and computational analysis for steady-state metabolome data
All steps of data processing and further analysis were performed in
Matlab 2015b (The Mathworks, Natick). For steady-state metabolome
profiles, the bioinformatics pipeline is described in ref. ^[192]26 and
is here summarized. Multiple regression analysis to estimate the
relative metabolite concentrations at steady state was performed using
the Matlab fitlm function. This function infers model parameters α
(cell line-specific) and β by minimizing the Euclidian distance between
measured metabolite intensities and model predicted ones. It is worth
noting that the β represents the MS background signal, or in other
words the ion intensity when no cells are extracted. Hence, this
particular parameter is independent from cell types. Because of the
difficulties in reliably estimating the extracted cell number from
bright-field microscopy images above a confluence of 80% (Supplementary
Figure [193]3), and the observed deviation from metabolic steady state
(Supplementary Figure [194]4), we excluded all metabolome measurements
taken above this cell density threshold. For each metabolite, we solve
the following linear model:
[MATH: Icell1
,1Icell1
,2Icell1
,3……Icell2
,1Icell2
,2Icell2
,3…Icellm,p=Ncell1
,10…01Ncell1
,20…01Ncell1
,30…01…………1…………10Ncell2
,1…010Ncell2
,2…010Ncell2
,3…01……………00…Ncellm,p1⋅αcell1αcell2…αcellmβT :MATH]
6
where I[cell1,1] is the measured metabolite intensity in sample 1 of
cell line 1, N[cell1,1] is the corresponding number of cells extracted
in sample 1 of cell line 1. Cell line specific αs and β are the unknown
parameters to be fitted. We selected the metabolites exhibiting a
significant alteration in at least one cell line using a one-way ANOVA
test, including a step correcting for multiple hypothesis
testing^[195]66–[196]68 (Supplementary Figure [197]15).
Data processing and computational analysis for dynamic drug-induced
metabolome changes
The full matrix of dynamic metabolic profiles after DCA and oxamate
treatments is provided in Supplementary Data [198]2. In order to deduce
a specific metabolic fingerprint induced by an external perturbing
agent, we first selected the most significant metabolic changes
conserved across the cell lines, performed pathway enrichment analysis
on the resulting list of metabolites, and lastly analyzed response
variability across all cell lines.
Separately for each perturbation (i.e. oxamate and DCA), we extracted
the most significant and prominent metabolic changes that are conserved
across the different cell lines. To this end, for each individual
metabolite time course we calculated the median of maximum absolute
fold changes and the product of lowest p-values across cell lines. As a
result, each metabolite is associated with a unique median fold-change
and p-value, summarizing the effect of the perturbation on all cell
lines.
Metabolites with an absolute log[2] fold-change ≥ 1 and a combined
p-value ≤ 1e−10 were then tested against KEGG metabolic pathways.
Pathways with an overrepresented number of altered metabolites were
selected based on a hypergeometric statistical test and p-value
correction for multiple tests^[199]66,[200]67.
Metabolites that exhibit cell line-specific responses to a given
perturbation were selected on the basis of the response variability
exhibited across the different cell lines. The standard deviation for
each metabolite was calculated from the aforementioned maximum fold
changes in each cell-line time course, and metabolites with a standard
deviation ≥ 1.5 are retained and subjected to pathway enrichment
analysis (Supplementary Figure [201]15).
Minimal kinetic model
Here we describe cell proliferation as a function of CoA biosynthesis,
assuming that DCA activates CoA biosynthesis and CoA inhibits growth.
Reactions for the production (v[CoA]) and consumption (v[biomass]) of
CoA follow a Michaelis–Menten type of kinetics (see Eqs. ([202]4) and
([203]5) in the main text).
The two key assumptions are that CoA is not limiting for biomass
production (i.e. [CoA] ≫ K[m]), and that cells are ultrasensitive to
high levels of CoA, which in turn inhibit biomass production (i.e.
K[i] ≫ K[m]). To simulate the effect of a reduced CoA biosynthesis as a
function of hopantenate (HoPan) we divided v[CoA,max] by HoPan
concentrations. In the simulations reported in Fig. [204]5, we used the
following parameters: v[biomass,max ]= 1, K[CoA] = 0.01, K[i] = 1,
v[CoA,max] = 0.45, [CoA][0] = 10, [DCA] = 0.01, and [HoPan] = 5. The
system of differential equations was solved using the SymBiology
toolbox in Matlab 2015. It is worth noting that qualitatively the model
would behave similarly if we assumed that the growth inhibitory
compound is not the total pool of CoA, but an intermediary toxic
compound of CoA metabolism that rapidly equilibrates with the CoA pool.
Quantification of pantothenate using LC–MS/MS
SKOV3 cells were seeded in RPMI-1640 medium in six-well plates, and
supplemented with 11.3 mM DCA in RPMI-1640, or an equal volume of
medium without DCA. After 24 h, cells were washed once with 75 mM
ammonium carbonate (pH 7.4, 37 °C) and extracted with 500 µL extraction
solvent (40% acetonitrile, 40% methanol, 20% water, 25 µM phenyl
hydrazine), pre-cooled to −20 °C. The six-well plates were sealed,
incubated at −20 °C for one hour, and then stored at −80 °C until
further processing. Prior to LC–MS/MS measurements, the plates were
thawed, and cells were detached from the bottom of each well using a
cell culture scraper. The extract was transferred to separate sample
tubes and centrifuged for 5 min at 13,000 rpm to separate cell debris.
The supernatants were then transferred to fresh sample tubes,
supplemented with equal volumes of fully ^13C-labeled extract of
Escherichia coli (prepared in-house), and subsequently dried by vacuum
centrifugation. Standard solutions containing different concentrations
of pantothenate (d-pantothenic acid calcium salt, Fluka 21210) were
prepared similarly, i.e. supplemented with ^13C-labeled cell extract
and dried by vacuum centrifugation. Immediately prior to analysis, all
samples were reconstituted in water (10× concentrated) and kept on ice
until analysis. Chromatographic separation and MS/MS detection on a
triple quadrupole mass spectrometer was performed as described in
detail in Buescher et al. ^[205]69, using an injection volume of 10 µL.
Data availability
All data generated or analyzed during this study are included in this
published article as supplementary data.
Electronic supplementary material
[206]Supplementary Information^ (3.5MB, pdf)
[207]42003_2018_111_MOESM2_ESM.docx^ (12.5KB, docx)
Description of additional Supplementary Infomation
[208]Supplementary Data 1^ (1.3MB, xlsx)
[209]Supplementary Data 2^ (5.6MB, xlsx)
Acknowledgements