Abstract
Ex vivo stem cell self-renewal and maintenance is supported by absence
of serum-derived mitogens. In the present study, we sought to elucidate
the proteomes of stem-like cells grown in serum-free media across a
panel of high-grade serous ovarian cancer cell lines, which encompass a
gradient from epithelial, intermediate and mesenchymal cell phenotypes
to recapitulate the heterogeneity of the disease. MaxQuant-based
label-free quantification of proteins identified that despite their
different cellular and molecular architectures, all phenotypes
exhibited mitochondria- and stemness-related pathways under conditions
of serum starvation, although the specific proteins involved were
discrete to each phenotype. This suggests that common cellular programs
in a disease can be mediated through variable biological networks that
generates molecular heterogeneity. We further explored if these
pathways are inter-related, co-regulated or just incidentally
associated in response to an environment depleted of growth factors and
mitogens. Irrespective of their phenotype, cell lines on
serum-starvation displayed an increased amount of mitochondrial DNA,
mitochondrial biogenesis and mitochondrial activity with a switch from
glycolysis to oxidative phosphorylation fuelled by the fatty acid
oxidation. Ultra-structural studies implicated this metabolic
fluctuation was regulated by dynamic mitochondrial remodelling. This
also led us to explore a possible therapeutic strategy of targeting
mitochondrial function to restrict tumor regenerative potential and
disease recurrence. Conclusively, these new avenues contribute to a
more comprehensive understanding of ovarian cancer.
graphic file with name 41419_2025_7987_Figa_HTML.jpg
Subject terms: Ovarian cancer, Cancer stem cells, Cancer metabolism
Introduction
Amongst gynecological malignancies, high-grade serous ovarian carcinoma
(HGSC) is regarded as most aggressive and presents challenges of early
detection and treatment emerging from the complex biologies of inter-
and intra-tumor heterogeneity [[30]1–[31]4]. Amongst the several
attempts towards addressing these issues, we had earlier resolved
inter-tumor molecular heterogeneity into three discrete molecular
subtypes [[32]5–[33]7]. While these clinically validated out in patient
tumors, we also studied their correlation with distinct cellular
phenotypes in vitro that facilitated deeper molecular investigations
[[34]8, [35]9]. Briefly, the three tumor subtypes encompass five cell
phenotypes, each of which was exemplified by at least one cell line.
Each of these epithelial (E-OVCAR3), intermediate epithelial
(iE-CAOV3), epithelial-mesenchymal hybrid (E/M-OVCA420), intermediate
mesenchymal (iM-A4), and mesenchymal (M-OVMZ6) states displays a
distinct molecular and phenotypical architecture as well as biological
functions.
Resolution of intra-tumor heterogeneity reveals yet another pivotal
aspect of tumor biology encompassing genomic changes (mutations,
aneuploidy) and cellular dynamics involving intrinsic dormancy
(quiescence) and regeneration by cancer stem cells (CSCs) within tumors
that generate hierarchies of multiple clones [[36]10]. CSCs are
implicated in processes such as metastasis, drug resistance and
recurrent disease by leveraging their state of quiescence, presence of
drug efflux pumps, signaling pathways that aid self-renewal, such as
Hedgehog signaling pathway, Notch signaling pathway and Wnt signaling
[[37]11, [38]12]. Studying CSCs in vitro in the presence of serum that
contributes several essential growth factors and mitogens perturbs
their quiescence, creates a state of rapid proliferation rather than
that of a tightly self-renewal program. Such system artifacts make it
irrelevant to applying outcomes to the in situ state where cells do not
divide continuously. Serum depletion has hence, earlier been indicated
to provide for more relevant systems of CSC maintenance by permitting
entry into a state of quiescence or that of slow cycling that also
influences cellular plasticity, migration and invasiveness of tumor
cells [[39]13–[40]17]. While serum depletion eliminates cellular
replicative stress, it may impose nutritive stress and altered cellular
energetics that remains to be elucidated.
In the present study, we initially affirmed our working hypothesis that
serum starvation (SS) would provide an in-situ relevance for studying
the molecular networks contributing to CSC maintenance and/or
self-renewal. Following this, differential proteomics across the
spectrum of clinical heterogeneity of HGSC cell subtypes identified
several enriched proteins under SS conditions, none of which were
common across all the phenotypes yet, mitochondrial translation and
activation were indicated to be a common function. More specifically,
mitochondrial metabolic pathways viz. TCA and OXPHOS involving
mitochondrial matrix and Electron transport chain (ETC) proteins were
prominently enhanced in all phenotypes under SS, along with acquisition
of a stem-like state in association with upregulation of mitochondrial
proteins. Our exploration of the same suggests a coregulation and
cross-talk between the two pathways. This vulnerability may present a
potential opportunity to target residual regenerative potential despite
the inherent heterogeneity of HGSC.
Results
Serum starvation modulates enrichment of ‘stemness’ features and a
phenotype-specific proteomic profile in HGSC cell lines
Comparing cycling profiles of HGSC cell lines under conditions of serum
starvation (SS) in comparison with their growth in the presence of
serum (+S; controls) indicated significant enrichment of a G0/G1
fraction and reduced S phase across the entire gradient of phenotypes
in HGSC cell lines (Fig. [41]1a, Supplementary Fig. [42]1a). We
assessed the slow-cycling and quiescent nature of this fraction using a
vital fluorophore PKH26, that binds to the plasma membrane and is
progressively quenched with each cell division [[43]18]. This allowed
us to distinguish HGSC cell populations in vitro into distinct PKH
fractions based on label retention, viz. PKH^hi, PKH^lo, and PKH^neg
[[44]18]. The PKH^hi fraction represents quiescent/slow-cycling cells,
PKH^lo includes cells that have undergone limited divisions, and
PKH^neg reflects rapidly dividing cells that undergo complete label
quenching. All phenotypes retained a substantial proportion of cells in
the PKH^hi and PKH^lo fractions at all time points examined post serum
starvation. In contrast, in presence of serum these fractions are
rapidly depleted (Fig. [45]1b; Supplementary Fig. [46]1b). Notably, an
increased number of quiescent/slow-cycling cells under serum-depletion
was also associated with significantly enhanced expression of the
self-renewal marker Nanog across all phenotypes examined (E/M state
being an outlier), while Oct4 appeared to be enriched in the epithelial
phenotypes (Fig. [47]1c; Supplementary Fig. [48]1d). These findings
suggest that serum starvation may serve as an effective surrogate model
for studying the stem-like state in HGSC across its molecular
phenotypes.
Fig. 1. Serum starvation enriches slow cycling stem-like cells across a
phenotype gradient ranging from epithelial (E-OVCAR3), intermediate
epithelial (iE-CaOV3), epithelial-mesenchymal hybrid (E/M-OVCA420),
intermediate mesenchymal (iM- A4) and mesenchymal (M-OVMZ6) in HGSC cells
with discrete enriched (exclusive and upregulated) proteomic profiles
associated with each phenotypic state.
[49]Fig. 1
[50]Open in a new tab
a Altered propidium iodide (PI)-based cell cycle kinetics following SS
of HGSC cell lines across the phenotypic gradient across three time
points (48 h, 72 h and 96 h). Within each cluster of stacked-bar
graphs, the first one represents data from serum-supplemented (+S)
conditions, while the subsequent bar depicts corresponding values under
serum-free (SS) conditions. Colored asterisks on the second bar of each
pair (SS condition) indicate statistically significant differences
(p-values) in each cell cycle phase relative to their respective +S
controls; b Representative flow cytometry-based dot-plot of PKHhi,
PKHlo and PKHneg fractions in OVCA420 (E/M) + S vs SS sample, left and
right panel respectively, at different time points (0 h, 24 h, 48 h,
72 h and 96 h) under SS and +S conditions across the phenotype gradient
revealed through PKH label-chase (green-PKHhi, red-PKHlo, blue-PKHneg);
c Relative RNA expression of self-renewal genes (Nanog,Oct3/4), in SS
and +S states across different phenotypes; d Analytical pipeline for
label-free quantification and identification of differentially
expressed proteins; e Differentially expressed protein candidates
across the phenotypic gradient under SS & +S conditions, Top panel –
Volcano plots (red–exclusive to +S, black–upregulated in +S,
green-exclusive to SS, blue– upregulated in SS), Lower panel -
LFQ_intensity plot (gray–E, yellow–iE, green–E/M, brown-iM, light
green–M) *p < 0.05, **p < 0.01, and ***p < 0.001.
We further developed a pipeline for mass spectrometry-based protein
profiling following 48 h of SS using MaxQuant^TM-based label-free
quantification (LFQ) and data analysis (Fig. [51]1d). This identified
differentially enriched, significantly upregulated proteins exclusive
to each of the two groups within each HGSC phenotype (SS and +S), as
well as across the entire gradient of phenotypes (Supplementary Fig.
[52]1e, f; Supplementary Table [53]1). Proteins that were either
exclusive to or significantly upregulated (>2-fold change) in
individual groups (+S or SS) were considered as “enriched proteins” for
further analysis (Supplementary Table [54]1). Interestingly, no
proteins were commonly enriched in all phenotypes under SS conditions,
although neighboring states in the gradient shared a few candidates
(Supplementary Fig. [55]1f). Conclusively, the absence of serum-derived
growth factors and mitogens that perhaps could lead genes towards
acquisition of a ‘stem-like’ state under conditions of serum depletion
state in HGSC as affirmed through lowered cell cycle kinetics and
discreet enrichment of self-renewing cells within the population
associated with a specific yet varied protein expression in each
phenotype (Fig. [56]1e). Such discrete proteomic profiles despite a
common stem cell state across phenotypes is well aligned with our
earlier studies correlating phenotype-specific molecular networks and
responses to microenvironmental cues [[57]7, [58]9].
Mitochondrial metabolic pathways are enriched under conditions of serum
deprivation regardless of cell phenotype
Phenotype-specific functions were further delineated through pathway
analysis of enriched proteins (exclusive and significantly
upregulated_>2-fold change) in each phenotype following serum
depletion. The E, iE and E/M phenotypes were thus revealed as being
quite discrete through their association with differential biological
functions and pathways, while M and iM phenotypes shared several common
pathways (Fig. [59]2a, b). The most interesting and unexpectedly common
enriched pathway across all phenotypes was of mitochondrial translation
(initiation, elongation, termination - REACTOME and DAVID analyses)
associated with enrichment of Tricarboxylic acid (TCA) cycle, ETC,
oxidoreductase complex, mitochondrial ribosomes, mitochondrial
membrane, mitochondrial gene expression, mitochondrial matrix etc.
(Gene Ontology analysis, GO; ClueGo v2.5.10; Fig. [60]2c; Supplementary
Fig. [61]2). This association was unique and emerged despite a majority
of mitochondria-associated proteins being distinctly unique to each
phenotype and limited commonality of proteins between neighboring
phenotypes (Fig. [62]2d). This could reflect on the nature and
stability of discrete associated molecular networks governing its
phenotype and may also extend to a distinct cellular architecture in
each tumor class [[63]8]. Concurrent enrichment of CSC-related
molecular pathways was also evident across the phenotypes affirming
recapitulation of stemness features in response to SS through similar
responses between neighboring phenotypes (MAPK signaling and RAS
mutants enriched in E, iE and E/M phenotype whereas Hedgehog signaling,
ABC-family proteins mediated transport etc. were enriched in iM and M;
Fig. [64]2e). Conclusively, these data suggesting activation of
mitochondrial metabolic pathways following serum deprivation regardless
of the unique protein profiles across phenotypes may be pivotal as a
stress response, which is associated with acquisition of a stem-like
state, and are consistent with previous reports associating stemness
with cellular stress [[65]19–[66]23].
Fig. 2. Activation of mitochondrial machinery is common across all phenotypes
on serum depletion.
[67]Fig. 2
[68]Open in a new tab
a Venn diagram representing enriched (shared and unique) pathways
following SS; b A heatmap illustrating RAECTOME pathway enrichment,
based on enrichment p-values, depicting molecular pathways
significantly enriched in SS compared to the corresponding +S condition
across the phenotypic spectrum (enriched mitochondrial translation
pathways, p < 0.05, are highlighted at the top of the heatmap); c Bar
graph indicating enriched GO terms across HGSC phenotypes following SS
(number of proteins corresponding to each GO term indicated on the
bar); d LFQ intensity scatter plot indicating distribution of
SS-enriched mitochondria-related proteins across the phenotype gradient
(green-E, red-iE, blue-E/M, yellow-iM and black-M); e Horizontal bar
graph representing enrichment of stemness-related pathways under SS
across phenotypes *p < 0.05, **p < 0.01, and ***p < 0.001.
Serum starvation triggers a metabolic switch from glycolysis to OXPHOS, with
increased mitochondrial biogenesis, DNA copy number, membrane potential and
ROS
The most widely studied effects of mitochondria in cancer relate to
tumor cell metabolism, with glycolysis being a preferred pathway
[[69]24]. Surprisingly, we observed that SS contrarily led to reduced
glucose uptake and lactate production across all phenotypes, suggesting
a preferred switch towards oxidative phosphorylation (OXPHOS; Fig.
[70]3a–i, a–ii). Inhibition of glycolysis using 2-DG and sodium
dichloroacetate (DCA), or of OXPHOS employing Rotenone, Antimycin A,
and Oligomycin, revealed that only OXPHOS inhibition significantly
compromised cell viability under serum-deprived conditions across the
various phenotypic subtypes. An exception was noted with DCA, which
elicited a marked reduction in survival compared to controls
specifically in the E (OVCAR3) and iM (A4) phenotypes. This
differential response is likely attributable to the mechanistic effect
of DCA. DCA primarily inhibits pyruvate dehydrogenase kinase (PDK),
thereby activating pyruvate dehydrogenase (PDH) and promoting
mitochondrial respiration [[71]25]. Consequently, we hypothesize that
the survival of a subset HGSC phenotypes was affected by DCA through
its modulation of mitochondrial metabolic flux. Additionally, the
unique metabolomic architectures intrinsic to each phenotypic subtype
contributed to the heterogeneity in response, rendering the effects of
DCA non-universal across all phenotypes (Fig. [72]3b; Supplementary
Fig. [73]3a-i, b). We further explored and affirmed through flow
cytometry that such enrichment of mitochondrial molecular signatures on
serum withdrawal is associated with increased mitochondrial biomass
across all phenotypes and at all the timepoints examined (Fig. [74]3c,
Supplementary Fig. [75]3c; upper panel). Along with this, increased
mtDNA copy number (as compared to nuclear DNA) and mitochondrial
membrane potential (TMRM assay) indicating activation of mitochondria
on SS were noted (Fig. [76]3d, e, Supplementary Fig. [77]3c-lower
panel), concurrently with increased levels of reactive oxygen species
(ROS), which may not be cytotoxic as the HGSC cells have entered a
quiescent state (Fig. [78]3f, Supplementary Fig. [79]3-middle panel).
These data indicate that, unlike rapidly dividing cancer cells that
rely on glycolysis, quiescent CSCs have more active mitochondria and
rely on OXPHOS as their primary source of energy, which enables them to
survive under conditions of nutritional stress.
Fig. 3. HGSC cells tide over SS by virtue of enhanced mitochondrial activity
and preferential switch towards OXPHOS as a source of energy.
[80]Fig. 3
[81]Open in a new tab
a-i, a-ii. Glucose consumption and lactate assay respectively across
all phenotypes under SS & +S conditions (rosy beige-48h +S, golden
olive-48h SS, teal blue-72h +S, coral orange-72h SS, pear green-96h +S,
purple-96h SS); b Representative cell viability across all phenotypes
following treatment with IC50 concentrations of OXPHOS (pale
goldenrod-Oligomycin,13-22 uM; teal green-Rotenone,11-22 uM; pear
green-AntimycinA,11-40 uM) and glycolysis inhibitors (teal
blue-2-DG,150-340 uM; rosy beige-DCA,19–32 uM; black-untreated
controls) for each cell line following serum-deprivation for 48h
(cell-line specific IC50 values are given in Supplementary Fig.
[82]3b); c Representative mitochondrial mass analysis (MitoTracker
green_FM assay) under SS & +S conditions; d Relative mtDNA content
under 48h SS & +S conditions across cell phenotypes; e Representative
mitochondrial membrane potential (TMRM assay) under SS & +S conditions
across phenotypes and at different time points; f Truncated violin plot
indicating mitochondria-associated ROS levels in under SS & +S
conditions across phenotypes *p < 0.05, **p < 0.01, and ***p < 0.001.
HGSC cells subjected to serum starvation rely on stored fatty acids for their
energy requirements
To explore the possibility that cells under serum starvation may rely
on stored fatty acids (FAs), through fatty acid oxidation (FAO), as a
source of energy, we profiled the frequency of lipid droplets (LDs) in
cells under SS for 48 h as compared with controls (+S). A substantial
decrease in LDs following starvation was a common feature across all
phenotypes (Fig. [83]4a, b), indicating stored FAs mobilization for
energy generation under nutrient deprivation. However, the precise
mechanisms underlying this mobilization, whether it is predominantly
mediated by lipophagy or conventional lipolysis, is remain to be
elucidated. The dependence on FAO and use of LD-derived FAs as a source
of energy was further affirmed by profiling cell survival on exposure
to Etomoxir (an inhibitor of carnitine palmitoyltransferase 1, CPT1).
This clearly displayed all cell lines except OVCA420 (E/M phenotype)
under SS conditions to be more sensitive to the inhibitor over controls
(Fig. [84]4c). The comparable sensitivity of OVCA420 to Etomoxir under
both conditions is likely to be due to some undetermined
phenotype-specific metabolic feature that leads to elevated basal level
FAs transport to mitochondria and fatty acid β-oxidation activity.
Since pathway analysis had earlier also indicated significant
enrichment of “TCA cycle and respiratory electron transport” across all
phenotypes (Fig. [85]4d), we profiled and identified enhanced
production of TCA metabolites (Cis-Aconitate, Iso-citric acid,
Succinate, Fumarate and Oxaloacetate) after 48 h of SS as compared with
their respective +S controls (Fig. [86]4e, Supplementary Fig. [87]4).
Conclusively, HGSC cells on serum depletion to rely on FAO and stored
FAs to fuel TCA cycle and ETC and subsequently OXPHOS for their energy
requirements.
Fig. 4. HGSC cells rely on fatty acid oxidation and fatty acids to fuel the
TCA cycle and respiratory ETC for their energy needs under SS conditions.
[88]Fig. 4
[89]Open in a new tab
a Representative confocal images identifying BODIPY lipid droplets
(green) in cells under SS and +S conditions (lower and upper panel
respectively), nuclei stained with Hoechst; b Quantification of lipid
droplets (Confocal imaging- 50 cells from each sample) across HGSC
phenotypes under SS and +S conditions; c Void bar-graph indicating cell
viability under SS and +S conditions following exposure to Etomoxir; d
Table indicating significant enrichment of the TCA cycle and electron
transport chain across HGSC phenotypes; e Abundance of TCA metabolites
under SS and +S conditions across the gradient from E to M phenotypes
(top to bottom) *p < 0.05, **p < 0.01, and ***p < 0.001.
Mitochondria in HGSC cells under conditions of serum starvation undergo
fusion and display thinner cristae and tight crista junctions
In alignment with the principle that “form follows function,” we
hypothesized that altered metabolism of HGSC cells in response to serum
deprivation may also be associated with changes in mitochondrial
morphology. Indeed, a shift from a globular to more elongated
presentation of mitochondria across all the HGSC phenotypes was
identified, quantitation of which affirmed the same [higher Aspect
Ratio (AR) and Form Factor with lowered Roundness and Solidity features
in 48-h SS cells compared +S controls; Supplementary Fig. [90]5a-i,
a-ii]. Validation through two-dimensional Transmission Electron
Microscopy (2D-TEM) imaging and analysis further suggested potential
mitochondrial fusion events in HGSC SS cells as (marked increase in
Feret’s diameter, AR, perimeter and length; Fig. [91]5a, b). This was
further supported by the protein level expression of the mitochondrial
dynamics regulators. An overall trend of increased OPA1 expression and
decreased DRP1 levels was observed in HGSC phenotypes under
serum-deprived conditions; with CaOV3 (iE) and A4 (iM) cells being
outliers for DRP1 expression, while OVCA420 (E/M) and OVMZ6 (M) were
outliers for OPA1 expression (Fig. [92]5c–i, c–ii). This differential
response may reflect heterogeneity in mitochondrial dynamics across
different HGSC phenotypes, potentially mediated by interactions with
other regulatory proteins.
Fig. 5. SS-driven mitochondrial fusion is associated with thinner cristae,
tight CJs and an increased number of cristae per unit mitochondrial length.
[93]Fig. 5
[94]Open in a new tab
a Representative 2D-TEM images highlighting mitochondria (red asterisk)
in HGSC cells under 48 h of SS in comparison with controls; b Bar-graph
representation indicating quantitative mitochondrial structural
parameters from 2D-TEM images (a minimum of 30 mitochondria from 12
cells were analyzed each in +S and SS condition across the different
phenotypes); c-i Representative Western blots indicating expression of
mitochondrial fusion and fission proteins (OPA1 and DRP1 respectively)
48 h SS vs + S samples, actin used as loading controls; c-ii
Quantification of OPA1 and DRP1 protein expression in Western blots; d
Truncated violin plot representing cristae width computed from 2D-TEM
images (a minimum of 50 mitochondria from 12 cells were analyzed each
in +S and SS condition across the differenst phenotypes); e
Representative bar-graph wherein individual data points indicate number
of cristae per unit mitochondrial length across phenotype gradient
under SS and +S conditions; f Representative TEM images indicating the
mitochondria and cristae (highlighted by red squares) under SS and +S
conditions, lower and upper panels respectively across the phenotypic
gradient *p < 0.05, **p < 0.01, and ***p < 0.001.
Given that cristae play a crucial role in OXPHOS by housing ETC
complexes and facilitating ATP synthesis through H+ gradient
optimization, we examined their structural dynamics under conditions of
serum deprivation. 2D-TEM images of cells revealed an inverse
correlation between cristae width and number of cristae per unit
mitochondrial length (Fig. [95]5d, e). Interestingly, the (E) state was
associated with fewer and broader cristae that increased progressively
towards the (M) state under +S conditions. A reversal of this trend was
observed on SS, along with significant reduction in width of crista
junctions (CJs) and increased number of cristae per unit length of
mitochondria as compared with controls across all phenotypes (Fig.
[96]5d–f; Supplementary Fig. [97]5b-i, b-ii). Collectively, these
observations strongly demonstrate that HGSC cells adapt to serum
deprivation through a metabolic switch to OXPHOS and remodeling of
their mitochondrial ultrastructural features to optimize energy
demands.
Mathematical modeling supports mitochondrial dynamics readouts and predicts
higher ATP turn over in fused mitochondria as a response to serum starvation
Towards a deeper understanding and prediction of phenotype-dependent
mitochondrial dynamics and energy outcomes in response to SS
(considered as nutritional stress in the system), we modeled some of
the ultrastructural features based on the following assumptions (Fig.
[98]6a; Table [99]1)—
* Mitochondrial number is a function of mitochondrial biogenesis and
mitophagy;
* A system possesses healthy mitochondria (those less likely to
undergo mtDNA mutations) as well as deviant derivatives (prone to
mtDNA mutations).
* Healthy and deviant mitochondria have identical rates of fusion and
fission that are reflected in the expression levels of OPA1 and
DRP1 (key mitochondrial fusion and fission proteins respectively);
* Four classes of mitochondria viz. healthy units (HU), healthy fused
(HF), deviant units (DU) and deviant fused (DF) were considered;
since both deviant and healthy mitochondria have the same rate of
biogenesis, hence formation rate (B) of all four classes is
uniform;
* Healthy and deviant mitochondria contribute to ATP production;
however, the former are more efficient, producing ATP at a factor
(α) greater than the latter;
* Fused mitochondria exhibit significantly enhanced ATP production
efficiency, modeled as a fused efficiency factor (ϵ). This
indicates that fused mitochondria (both healthy and deviant) are
more productive than their unfused counterparts;
* The rate of mitochondrial ATP production (θ) is based on
mitochondrial structural parameters including, number of cristae
per unit length (c_n), membrane potential [e^(m_p)], average
cristae width (c_w), and cristae junction width (c_jw);
* As ATP machinery resides within cristae, greater number of cristae
correlates with increased ATP production;
* ATP production is directly proportional to membrane potential;
higher membrane potential hence indicates increased ATP synthesis;
* Cristae junctions are crucial for minimizing proton leakage from
the inner cristae space, and hence increased numbers enhance proton
density and ATP production;
* Thinner cristae also contribute to increased proton density and
improved OXPHOS efficiency;
* Healthy as well as deviant mitochondria initially display increased
rate of fusion in response to serum starvation stress to mitigate
mitophagy; concurrently, the rate of fission is expected to
decrease under stress. Hence, our model incorporates an exponential
increase in initial fusion (F_0) and decrease in fission (K_0)
rates;
* The processes of mitochondrial fusion and fission are factored into
the model as being influenced by the availability and concentration
of ATP.
Fig. 6. Mathematical modeling of mitochondrial dynamics and energetics of
HGSC phenotypes under SS and normal conditions.
[100]Fig. 6
[101]Open in a new tab
a Graphical representation of mitochondrial dynamics based on
parameters described in the text; b Line plot indicating the number of
HF mitochondria over time in stress free (+S) and SS conditions; Line
plot indicating the number of mitochondria over time in different HGSC
phenotypes under +S (c) and SS (d); e, f Line plot indicating the ATP
energetics across the phenotypes under +S and SS conditions,
respectively; g Bar-graph indicating levels of ATP production in SS and
+S conditions *p < 0.05, **p < 0.01, and ***p < 0.001.
Table 1.
Table indicating the mitochondrial parameters considered for
mathematical modeling.
Cristae Width (c_w) Cristae count per unit length (c_n) Cristae
Junction Width Membrane Potential (m_p) Fusion Promoter (OPA1) Fission
Promoter (DRP1)
OVCAR3 + S 1 0.28 0.62 0.99 0.02 0.31
OVCAR3SS 0.05 0.6 0.3 1 0.06 0.2
CAOV3 + S 0.55 0.32 1 0.63 0.03 0.1
CAOV3SS 0.47 0.59 0.73 0.76 0.07 0.09
OVCA420 + S 0.42 0.31 0.89 0.29 0.03 0.23
OVCA420SS 0.23 1 0.47 0.32 0.04 0.13
A4 + S 0.44 0.54 0.63 0.54 0.44 1
A4SS 0.18 0.97 0.34 0.65 0.65 0.98
OVMZ6 + S 0.29 0.62 0.41 0.54 0.93 0.23
OVMZ6SS 0.34 0.78 0.23 0.51 1 0.16
[102]Open in a new tab
The above assumptions were applied to the derivation of coupled
ordinary differential equations as follows:
[MATH:
dNHUdt=B−M<
mi>H−FATPe<
/mi>stress<
msub>NHU+KATPe<
/mi>−stressNHF :MATH]
[MATH:
dNDUdt=B−M<
mi>D−FATPe<
/mi>stress<
msub>NDU+KATPe<
/mi>−stressNDF :MATH]
[MATH:
dNHFdt=B−KATPe<
/mi>−stress<
msub>NHF+FATPe<
/mi>stressNHU :MATH]
[MATH:
dNDFdt=B−KATPe<
/mi>−stress<
msub>NDF+FATPe<
/mi>stressNDU :MATH]
[MATH: dATPdt=θϵαNH<
mi>F+NDF+αNH<
mi>U+NDU−<
/mo>μ−KATPNHF<
/mrow>+NDFe−stress−FATPNDU<
/mrow>es<
/mi>tress−FATPNHU<
/mrow>es<
/mi>tress :MATH]
wherein,
* (i)
ATP production (θ) is assumed to be a function of all the
above-mentioned parameters,
[MATH:
θ=cn+empcw+cjw :MATH]
* (ii)
Mitochondrial fusion (F) and fission (K) rates are assumed to be a
function of OPA1 and DRP1,
[MATH:
F=F0*OPA1 :MATH]
[MATH:
K=K0
*DRP1
:MATH]
* (iii)
The rate of mitochondria created by biogenesis is equal to the
total number of mitochondria that eliminated through mitophagy,
[MATH: BNHU+NHF+NDU+NDF=
MH<
mrow>NHU+MDNDU :MATH]
* (iv)
The initial conditions were set at,
[MATH: NHU=1,NHF=1,NDU=1,NDF=1,ATP=0,μ=3 :MATH]
Our model thus predicted an increase in the number of HF mitochondria
under serum stress, with HU and DU being almost completely depleted
from the system over the course of time (Fig. [103]6b). This was
substantiated experimentally through TEM analysis wherein regardless of
the phenotype, the frequency of elongated HF mitochondria under SS was
higher over that in a stress-free +S environment that displays higher
frequency of HU mitochondria (Fig. [104]6b). In addressing the
time-dependent mitochondrial dynamics of individual HGSC phenotypes, a
spectrum of HF distribution was revealed ranging from being highest in
“M” phenotype (OVMZ6) and lowest in the “E” phenotype (OVCAR3) under +S
conditions; this was completely reversed on SS (Fig. [105]6c, d).
Further prediction of cellular energetics revealed a remarkable
increase in ATP production across all HGSC phenotypes following SS as
compared with +S conditions (Fig. [106]6e, f). Within this, our model
predicts E and E/M phenotypes (OVCAR3 and OVCA420 respectively) to be
associated with maximal ATP production, while iE (A4) is likely to be
the least energetic (Fig. [107]6f). Importantly, these predictions
regarding cellular energetics were experimentally substantiated by
assaying ATP, which corroborated the E, E/M and iE phenotypes to
exhibit elevated levels of ATP production following SS (Fig. [108]6g).
Inhibition of OXPHOS and mitochondrial translation impairs self-renewal in
vitro and tumor progression in vivo
We further evaluated in vitro suspended spheroid-formation capability
of A4 cells to validate the purported enhanced generation of stem-like
cells following SS and their dependency on OXPHOS and mitochondrial
functions for self-renewal and/or maintenance. Notably, a significant
reduction in spheroid-forming capabilities was observed following
individual and combined drug treatments, including OXPHOS (ETC)
inhibitors and antibiotics (leveraging the similarity between bacterial
and mitochondrial ribosomes that can inhibit mitochondrial translation;
Supplementary Fig. [109]3a-i, b-i, b-ii; Supplementary Fig.
[110]6a,[111] b). While Doxycycline and Erythromycin exhibited the most
pronounced inhibitory effects, Antimycin A, Oligomycin and
Chloramphenicol also were inhibitory (Fig. [112]7a–i, a–ii). In
contrast, Tetracycline induced spheroid formation, albeit the extent of
formation was significantly reduced compared to the controls (Fig.
[113]7a–i, a–ii). To assess whether enhanced OXPHOS could restore the
spheroid-forming capacity of A4 cells compared to its inhibition, we
treated the cells with DCA (augment OXPHOS by inhibiting PDK and
subsequent activation of PDH). Notably, DCA treatment resulted in
partial restoration of spheroid formation ability (Fig. [114]7a–i,
a–ii). However, this restored capacity remained significantly below
that observed in vehicle-treated control ability (Fig. [115]7a–i,
a–ii). These findings suggest that the intrinsic balance between
glycolysis and OXPHOS in untreated cells likely represents an optimal
metabolic state for spheroid formation [[116]26]. The modest increase
in mitochondrial activity induced by DCA appears insufficient to
surpass this physiological equilibrium (Fig. [117]7a–i, a–ii).
Collectively, the data support a critical role for mitochondrial
function in maintaining the self-renewal and stem-like properties of
cancer cells.
Fig. 7. Inhibition of OXPHOS and mitochondrial translation significantly
impairs spheroid formation in vitro and tumor progression in vivo.
[118]Fig. 7
[119]Open in a new tab
a–i Representative images and quantification of A4 spheroids exposed to
various drugs and their combinations (Panels with red star denotes the
antibiotic drugs Doxycycline and Erythromycin that most significantly
inhibited spheroid formation over other the drugs, and hence were
subsequently used for in vivo evaluation); a-ii Dot plot representing
the number of spheroids under different drug treatment measured on
Day14 (post cell seeding); b Schematic representation of drug regimen
used in the study; c-i Representative A4 tumors harvested on Day 21 of
different drug regimens; c-ii Line graph indicating A4 tumor volumes
measured on Day 0,7,14 and 21 during treatment of various drugs (single
and in combination; Day 0 corresponds to the initiation of treatment,
which occurs 14 days post subcutaneous tumor cell inoculation); c-iii.
Bar-graph indicating the anti-tumor efficacy of various drug
combinations, Y-axis represents the tumor inhibitory score (where
control represent a score of “1”); d-i Line graph depicting A4 tumor
volumes over the course of treatment with the ETC complex I inhibitor
metformin, administered either as a monotherapy or in combination with
other therapeutic agents. Day 0 marks the initiation of treatment.
Asterisks of different colors denote p-value significance (student’s t
test) between each treatment group and the vehicle control on the
corresponding day. The dotted line denotes p-vlaue significance among
the various drug regimens on Days 7, 14, and 21, as assessed by one-way
ANOVA. The solid line indicates the statistical significance of the
comparison between Metformin monotherapy and the combination therapy
comprising Metformin, Erythromycin, and Paclitaxel (Metformin + Ery +
Pax); d-ii Representative A4 tumors harvested on 21st day after
different drug regimens. *p < 0.05, **p < 0.01, and ***p < 0.001.
We further explored if the association between mitochondrial activity
and CSC self-renewal could be harnessed in situ using Doxycycline and
Erythromycin (that exhibited the most pronounced inhibitory effects on
spheroid formation). Indeed, the growth and progression of A4
xenografts in mice subjected to the antibiotic regimen, either alone or
in conjunction with Paclitaxel were significantly reduced following
treatment; despite the finding that some extent of drug
resistance/evasion may emerge with Doxycycline treatments after Day 14
(Fig. [120]7b, c-i, c-ii, c-iii). A consequent label-chase in
xenografts using the vital dye (PKH26) revealed an increase in the
frequency of label retaining cells, however with significantly reduced
tumor volumes following drug treatment as compared with vehicle
controls. To quantify the efficacy of each treatment regimen, we
calculated a “tumor inhibitory score” wherein label-retaining PKHhi
fractions representing CSCs were normalized to tumor volumes under
different drug regimens and compared with that of vehicle control
(Methods). An enhanced tumor inhibitory score reflects on effective
reduction in the number of slow-cycling/quiescent tumor cells following
treatments (Fig. [121]1ci, cii, ciii; Supplementary Fig. [122]7a).
Since CSCs may rely on OXPHOS, we also incorporated the ETC complex I
inhibitor Metformin into our drug regimen to study the effects of
co-targeting of OXPHOS and mitochondrial translation on tumor
progression; additional inclusion of Paclitaxel in this scheme would
permit targeting of non-CSC dividing cells within the tumor (Fig.
[123]7b). Different drug regimens exhibited distinct tumor suppression
kinetics over the treatment window (Fig. [124]7d–i). Among the various
pharmacological interventions assessed, Metformin administered as a
monotherapy or in combination with Doxycycline, Paclitaxel, or
Erythromycin produced a significant inhibitory effect on tumor growth
at Day 7 (Fig. [125]7d–i). Notably, the combination of Metformin with
Erythromycin and Paclitaxel (Metformin + ery + Pax) exhibited a
comparatively reduced, yet still significant, tumor-suppressive effect
relative to the vehicle control (Fig. [126]7d–i). In contrast, the
regimen comprising Metformin, Doxycycline and Paclitaxel (Metformin +
Doxy + pax) did not result in any noticeable tumor reduction at Day 7
(Fig. [127]7d–i). However, extended administration of either Metformin
+ Ery + Pax or Metformin + Doxy + Pax led to tumor inhibition at Day
14, comparable to other drug regimens. Upon cession of the treatment at
Day21, Metformin + Ery + Pax demonstrated the most pronounced tumor
inhibitory effect among all groups evaluated (Fig. [128]7d–i, ii,
Supplementary Fig. [129]7b). This differential tumor inhibitory effect
shown by Metformin + Ery + Pax on Days 7 to Day 21, may suggest an
initial delayed synergetic interaction, potentially due to
undetermined- pharmacodynamic factors.
Altogether, our findings strongly indicate that targeting mitochondrial
metabolism and translation, which are crucial for CSC self-renewal and
maintenance, in conjunction with conventional chemotherapeutic agents
that target the non-CSC tumor population, can enhance treatment
efficacy.
Discussion
Challenges in improving HGSC patient survival include intrinsic drug
resistance, tumor heterogeneity, resilient cellular energetics and
metabolic heterogeneity, all of which depend on the cellular
environment [[130]3, [131]27]. Cellular energetics is primarily
attributed to the ability of mitochondria to respond to
microenvironmental changes and altered gene and protein expression that
also supports stem-like cell maintenance in a tissue-specific manner
[[132]28–[133]37]. Serum depletion recapitulates the in-situ behavior
of CSC’s and their ability to opportunistically exploit available
resources within a nutrient-poor tumor microenvironment (TME; [[134]38,
[135]39]). Quiescent CSCs possess lower yet more efficient energy
generation as their metabolism is predominantly confined to minimal
maintenance levels; consequently, these cells preferentially rely on
slower anaerobic respiration processes such as OXPHOS, over glycolysis
[[136]39]. As the TME becomes hypoxic and nutrient scarce, CSCs also
metabolically adapt to utilize free fatty acids for survival [[137]38].
Hence, our identification of altered mitochondrial pathways including
enrichment of mitochondrial translation, FAO, TCA and OXPHOS, with
metabolites like succinate, oxaloacetate, fumarate and iso-citric acid,
accompanied by acquisition of stemness following serum deprivation
irrespective of their intrinsic cellular plasticity, is important.
At an ultrastructural level, the above changes are also associated with
orchestrating mitochondrial states between fusion and fission, along
with conserved patterns of cristae remodeling across all cellular
phenotypes. The ensuing cristae dynamics is directly linked with
cellular metabolic flux since ETC complexes are assembled along their
membranes and ATP synthase at their edges [[138]40–[139]42]. We thus
observed increased number of leaner cristae, tight CJs, OPA1 levels
which along with mitochondrial contact sites and cristae organization
system (MICOS) complex can play a crucial role in efficient ATP
synthesis by minimizing the proton leakage with the tight CJ openings
[[140]43]. However, neither fused nor fragmented mitochondrial
morphology is universally linked to cancer stemness. 2D-TEM analysis
also revealed altered inter-organelle communication between the
endoplasmic reticulum (ER) and mitochondria with reduced
mitochondrial-ER contact (MERC) distance and increased coverage
following serum deprivation (Supplementary Fig. [141]8). These findings
need a comprehensive investigation in the future, as MERC distance is
known to impact lipid metabolism, calcium-mediated OXPHOS, and
autophagy [[142]44–[143]47]. Various studies have used computer
simulation and mathematical models to investigate mitochondrial
fission-fusion dynamics and their response to different substrate
inputs, very few of which have integrated the mitochondrial dynamics
with cellular energetics especially, details regarding OXPHOS
[[144]48–[145]52]. In this context, our study is distinctive and one of
its kind, as it demonstrated the potential to predict cellular
bioenergetics by incorporating ultrastructural details from 2D-TEM
images along with expression data of the fusion-fission associated OPA1
and DRP1, which are central to mitochondrial dynamics. All observations
were valid across the entire gradient of phenotypes, thereby accounting
for disease heterogeneity.
The vulnerability created through increased dependency of CSCs on
mitochondrial OXPHOS has led to development of new drugs
(Mitoriboscins, Mitoketoscins, MitoTam) or repurposing of earlier
FDA-approved drugs such as antibiotics, Metformin etc. [[146]26,
[147]53–[148]56]. Ongoing clinical investigations with compounds like
IACS-010759 have shown promise in leukemia and glioma models [[149]57].
However, metabolic heterogeneity within tumor populations limits the
efficacy of these treatments in several instances. Our study too
demonstrates the potential of targeting mitochondrial translation and
OXPHOS using antibiotics and Metformin respectively; Etomoxir also
compromise these metabolic pathways through inhibition of the CPT1
transporter. The combination of these agents with conventional
chemotherapy that targets the non-CSC population, significantly
inhibited HGSC tumor progression in vivo.
Materials and methods
Cell culture
Five HGSC cell lines used in the study include, A4 (established earlier
in our lab from the ascites of a HGSC patient, [[150]58]), OVCAR3
(sourced from BRIC-NCCS Cell Repository, Pune, India), CaOV3 and
OVCA420 (provided by Prof. Judith Clements, Translational Research
Institute, Australia) and OVMZ6 (from Prof. Viktor Magdalen (Klinische
Forschergruppe der Frauenklinik der Tu, Munchen). All cell lines were
maintained at 37 °C under 5% CO[2] in a humidified incubator, cultured
in appropriate media—OVCAR3, CaOV3 and OVCA420 in RPMI 1640
(Gibco) + 10% fetal bovine serum (FBS,Gibco), A4 in Minimal Essential
Medium (MEM;Gibco) + 5% FBS + 1% non-essential amino acid (Gibco),
OVMZ6 in Dulbecco’s Modified Essential Medium (DMEM;Gibco) + 10% FBS +
100uM asparagine and 100uM arginine (Merck-Sigma). For serum starvation
(SS) and serum-fed condition (+S), cells were allowed to grow for 24 h
following which the media was replaced with equal volume of either
serum-free or complete media respectively. All cell lines were
authenticated via short tandem repeat (STR) profiling (Project No.
STR24082023), employing GeneMapper™ ID-X Software version 1.5 for
analysis. All cell lines were also confirmed to be free of mycoplasma
contamination.
Cell cycle analysis, assays for self-renewal and cell survival, PKH label
chase
48 h SS or +S HGSC cells were used for cell cycle and self-renewal
assays as described earlier [[151]58, [152]59]. Briefly, label chase
was performed with the vital lipophilic membrane dye fluorophore PKH26
(Merck, #PKH26GL), following the manufacturer’s instructions. PKH26
fluorescence intensity was assessed at designated time points (0, 24,
48, 72, and 96 h) using a BD FACS Canto flow cytometer. A4 3D-spehroid
formation capability was assayed in response to IC50 concentration of
different drugs as described earlier [[153]10]. Briefly, 5000 cells
were seeded in each well of a 96-well ultra-low attachment plate and
cultured in minimum essential medium containing 1% serum that was
replenished every 48 h along with drug/vehicle control-containing media
as per individual groups/experiments for 14 days. Images were captured
using Olympus FV3000.
OCT4 (forward-5’GACAACAATGAAAATCTTCAGGAGA3’, reverse
5’TTCTGGCGCCGGTTACAGAACCA3’), NANOG (forward-
5’AGTCCCAAAGGCAAACAACCCACTTC3’, reverse-
5’ATCTGCTGGAGGCTGAGGTATTTCTGTCTC3’) and SOX2 (forward-
5’TGGCGAACCATCTCTGTGGT3’, reverse- 5’CCAACGGTGTCAACCTGCAT3’) expression
were profiled for self-renewal through q-PCR analysis as described
earlier [[154]59]. MTT
[3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazo lium bromide]
(Sigma-Aldrich, #M2128) assays were used for determination of IC50
values and cell viability under glycolysis (2-deoxy-D-glucose,
Sigma-Aldrich, #D8375 and sodium dichloroacetate, Sigma-Aldrich,
#347795) or OXPHOS (Rotenone, Sigma-Aldrich, #R8875, Antimycin A,
Sigma-Aldrich, #A8674 and Oligomycin, Sigma-Aldrich, #O4876)
inhibitors, CPT1 inhibitor (Etomoxir, Sigma-Aldrich, #236020) and
antibiotic drugs (Doxycycline, Sigma-Aldrich; Erythromycin,
Sigma-Aldrich, #E5389; Chloramphenicol, Sigma-Aldrich; Tetracycline,
Sigma-Aldrich) as described earlier [[155]60]. All experimental data
presented were obtained from triplicate experiments to ensure
reproducibility.
In solution digestion, acquisition of spectrometry profiles, label free
quantification (LFQ)-based pathway analysis and data representation
HGSC cells harvested 48 h post-treatment (48h_SS) or as controls (+S)
in triplicate were processed per previously established protocols
[[156]61]. Data acquisition was performed using an Orbitrap Fusion™
mass spectrometer (Thermo Fisher Scientific) coupled with an EASY-nLC™
1200 nano-flow liquid chromatography (LC) system (Thermo Fisher
Scientific), and an EASY Spray column (50 cm × 75 µm ID, PepMap C18).
LFQ analysis was performed using MaxQuant version 1.6.17.0 [[157]62,
[158]63], followed by downstream data processing and statistical
analysis in Perseus (version 1.6.14.0, [[159]64]). A comprehensive
pipeline was developed to identify differentially expressed proteins
between +S and SS conditions [upregulated defined as a fold change
(FC) > 2 downregulated as FC < 0.5], or exclusively expressed proteins
in either condition; and were further visualized in volcano plots.
Proteins identified with at least two peptides and 10% sequence
coverage in at least two of the three replicates were included in the
analysis. Proteins which were exclusively expressed and significantly
upregulated (>2FC) in each phenotype following SS were collectively
considered as SS-enriched proteins and subjected to pathway enrichment
analysis, which was conducted using the REACTOME pathway database v84,
Gene Set Enrichment Analysis (GSEA), Cytoscape with the ClueGO plugin,
and the DAVID knowledgebase v2022q3 [[160]65–[161]68].
Mitochondrial-specific pathway analysis was performed using the
Mitocarta 3.0 database [[162]69]. Heatmaps were generated using MeV
version 4.9.0, with Euclidean distance applied for hierarchical
clustering of samples and genes/proteins.
Metabolomics
Prechilled methanol: acetonitrile: water (1:1:0.5) solvent was added to
HGSC cell line pellets (48h_SS & 48h_+S, in triplicates) to extract
metabolites, followed by freeze-thaw cycles and sonication. Samples
were centrifuged at 16,000 G for 20 min at 4 °C, the supernatant
lyophilized and resuspended in 80% methanol. Liquid
chromatography-high-resolution mass spectrometry (LC-HRMS) was
performed on Shimadzu Prominence HPLC system (Shimadzu Corporation,
Japan) connected to a SCIEX QTRAP 6500+ hybrid triple quadrupole/ion
trap mass spectrometer. Samples were loaded onto a Waters Atlantis T3
column (5 µm, 4.6 × 150 mm) maintained at 40 °C. Solvents used were
0.1% formic acid in LC/MS grade water (buffer A) and 0.1% formic acid
in acetonitrile (buffer B), with a gradient from 0 to 98% B over 38 min
at 0.5 mL/min, followed by 5 min at 98% B and 5 min of re-equilibration
and was operated in negative ion mode. Multiple reaction monitoring
(MRM) was applied for TCA cycle metabolites (Merck-Sigma, #ML0010) and
D2-L-phenylalanine was added as an internal control. Data were analyzed
using SCIEX-OS (Version 3.0.0.3339) at the BRIC-NCCS proteomic
facility.
Glucose consumption, lactate production, ROS, mitochondrial mass analysis,
TMRM assay, ATP assay and mitochondrial DNA copy number analysis
Glucose consumption and lactate production assays, and reactive oxygen
species (ROS) analysis were performed as described earlier [[163]59].
MitoTracker green FM (Cell Signaling Technology, #9074) was used
(150 nM) for the mitochondrial mass (mito-mass) analysis. Mitochondrial
potential differences and activity were examined using the image-iT
TMRM reagent (ThermoFisher Scientific, #[164]I34361). Briefly, HGSC
cells exposed to +S and SS conditions were incubated with 100 nM TMRM
reagent for 30 min in incubator, harvested, washed with PBS and
fluorescence was acquired. All FACS data were acquired in BD FACS Canto
and analysis were performed in FlowJo software v.6. For intracellular
ATP levels, ATP assay kit (Abcam, #ab83355) was used according to
manufacturer’s instruction. Colorimetric reading for the same was
acquired at 570 nm using a microplate reader and readings were
normalized to cell numbers. For mtDNA copy number analysis, 48 h SS vs
+S HGSC cells were incubated overnight at 55 °C with digestion buffer
(10 mM Tris-Cl, 100 mM NaCl, 0.5% SDS, 25 mM EDTA, 0.1 mg/ml proteinase
K). DNA was separated from RNA, proteins, and debris using
phenol:chloroform: isopropanol (25:24:1), followed by centrifugation.
The aqueous layer was treated with sodium acetate and ethanol,
centrifuged, washed with 70% ethanol, dried, and dissolved in NFW.
Quantitative real-time PCR was performed with SYBR Green PCR master mix
(TaKaRa, #RR820) on an Applied Biosystems StepOne Plus PCR system.
Human cytochrome-b (forward- 5’GCGTCCTTGCCCTATTACTATC3’, reverse
-5’CTTACTGGTTGTCCTCCGATTC3’) for mitochondrial DNA (mtDNA) and human
RPL13A (forward- 5’CTCAAGGTCGTGCGTCTG3’, reverse-
5’TGGCTTTCTCTTTCCTCTTCTC3’) for nuclear DNA (nuDNA) primers were used
for the analysis. Cycle threshold (Ct) values from triplicate reactions
in qPCR were computed using the following equation to calculate the
relative mtDNA content.
[MATH: Delta CtΔCt=nuDNA
Ct−mtDNAct;Relative mtDNA
content=2*(2ΔCt) :MATH]
All experimental data presented were obtained from at least triplicate
experiments to ensure reproducibility
Immunoblotting
Immunoblotting of 48 h SS and +S HGSC samples was performed as
described earlier [[165]70]. OPA1 (CST, #80471, 1:1000) and DRP1 (CST,
#8570, 1: 1000) primary antibodies were used. After incubation with
secondary antibody for 2 h at room temperature, membranes were
developed using SuperSignal West Pico PLUS chemiluminescent substrate
(ThermoFisher Scientific, #34579). Quantitative data presented are
derived from triplicate measurements.
Lipid droplet (LD) staining and confocal mitochondrial network analysis
48 h +S or SS HGSC (each in triplicate) cells were fixed with 2%
paraformaldehyde for 10 min, stained with 2 µM BODIPY^TM 493/503
(ThermoFisher Scientific, #D3922,) for 30 min and Hoechst for 10 min in
the dark. For mitochondrial network analysis, 72 h HGSC + S and SS
cells were fixed and stained with 100 nM MitoTracker deep red for
30 min followed by Hoechst staining. Images were captured on an Olympus
FV3000 and analyzed with ImageJ (V1.54 f). Mitochondrial network
analysis was performed using the “MitochondrialAnalyzer” plugin
(V2.1.0, [[166]71]).
Transmission electron microscopy (TEM)
Cells from 100 mm plates were pelleted down and washed with cold PBS
followed by fixation using 3% glutaraldehyde for 2 h at 4 °C. After
washing with 0.1 M sodium cacodylate buffer, cells were fixed using a
second fixative, 1% osmium tetroxide for 1 h at 4 °C in dark. After
dehydration and resin infiltration, cells were embedded in Araldite B
resin. Further, ultrathin sections with 70 nm thickness were cut on
Leica UC7 ultra-microtome and collected on copper 200 mesh grids. Cells
were stained with uranyl acetate and lead citrate, followed by scanning
using JEOL JEM 1400 PLUS transmission electron microscope at 120 kV.
Images were acquired using EMSIS TENGRA camera. All the TEM image
acquisition and sample processing were carried out in electron
microscope facility, ACTREC, Mumbai. Quantitative data represented were
derived from at least 15 TEM images from each +S and SS derivatives of
HGSC phenotypes
Mathematical modeling
Mathematical modeling was performed in Google Colaboratory
([167]https://colab.research.google.com/, [[168]72]) with Python V3.10,
the following abbreviations were used in differential equations.
[MATH: NHUisthenumberofunfusedHealthymitochondria :MATH]
[MATH: NDUisthenumberofunfuseddeviantmitochondria :MATH]
[MATH: NHFisthenumberoffusedHealthymitochondria
:MATH]
[MATH: NDFisthenumberoffuseddefectivemitochondria :MATH]
[MATH:
MHisthespecificmitophagyrateofhealthymitochondria :MATH]
[MATH:
MDisthespecificmitophagyrateofdefectivemitochondria<
/mi> :MATH]
[MATH: θistheATPproductionfactor :MATH]
[MATH:
cnisthenumberofcristaeperunitlength
:MATH]
[MATH: Ψisthetransmembranepotential :MATH]
[MATH:
cwisthecristaewidth :MATH]
Codes used for the mathematical model studies can be provided on
request.
Xenograft generation and drug evaluation
All procedures were performed on approval from the Institutional Animal
Ethics Committee (IAEC, project no. B-388), and mice were bred and
maintained at the BRIC-NCCS Experimental Animal Facility. Subcutaneous
xenografts were established by injecting 2.5 × 10^6 A4 cells into
6–8-week-old female NOD/SCID mice; wherever described some were
pre-labeled with PKH26. Mice were randomized to different groups
(n = at least 4 per group) and treatment initiated on Day 14 post-cell
injection. The following doses were administered intraperitoneally:
25 mg/kg of Paclitaxel (Sigma-Aldrich, #T7191), 50 mg/kg of each
Doxycycline and Erythromycin (Sigma-Aldrich, #E5389), and 250 mg/kg of
Metformin (Sigma-Aldrich, #317240); detailed description of drug
regimens is provided in Fig. [169]7b (for the metformin included mice
experiments 3.5 × 10^6 cells were inoculated for the subcutaneous
xenograft generation). In vivo tumor progression was monitored and
measured on Days 7th, 14th, and 21st following treatment by measuring
tumor volume using the formula: 0.5 × length × (width)^2. No blinding
was performed while assessing the tumor measurements. Mice were
euthanized on completion of the drug regimen (Day 21) and tumors were
harvested. A “tumor inhibitory score” of each drug regimen was computed
based on differences in % of PKH^hi and PKH^low fractions (i.e CSC and
progenitor populations) normalized to tumor volumes between treated and
vehicle control tumors.
[MATH: Tumorinhibitoryscore=Control%PKHhi+%PKHlowC.tumorvolume−Test%PKHhi+%PKHlowT.tumorvolume
:MATH]
* C. tumor: Vehicle control tumor
* T. tumor: Test (different drugs) tumor
* PKH^hi : CSC fractions
* PKH^low: Progenitors
Statistics
Unless specified otherwise, all experiments were conducted with a
minimum of three independent replicates. Statistical comparisons
between two groups were performed with two-tailed Student’s t test,
while comparisons involving three or more groups were analyzed using
one-way analysis of variance (ANOVA). Data are presented as the
mean ± standard error of the mean (SEM), unless otherwise noted.
Statistical significance was determined using p-values, with *p < 0.05,
**p < 0.01, and ***p < 0.001. Graphical representations and statistical
analyses were performed using GraphPad Prism software (version 8.4.2,
[[170]73]).
Supplementary information
[171]Supplementary Figures^ (2MB, pdf)
[172]Supplementary Table 1^ (75.1KB, xlsx)
[173]Western Blot Supplementary File^ (113.8KB, pdf)
Acknowledgements