Abstract
Objective
Aberrant activity of androgen receptor (AR) is the primary cause
underlying development and progression of prostate cancer (PCa) and
castration-resistant PCa (CRPC). Androgen signaling regulates gene
transcription and lipid metabolism, facilitating tumor growth and
therapy resistance in early and advanced PCa. Although direct AR
signaling inhibitors exist, AR expression and function can also be
epigenetically regulated. Specifically, lysine (K)-specific
demethylases (KDMs), which are often overexpressed in PCa and CRPC
phenotypes, regulate the AR transcriptional program.
Methods
We investigated LSD1/UTX inhibition, two KDMs, in PCa and CRPC using a
multi-omics approach. We first performed a mitochondrial stress test to
evaluate respiratory capacity after treatment with MC3324, a dual
KDM-inhibitor, and then carried out lipidomic, proteomic, and metabolic
analyses. We also investigated mechanical cellular properties with
acoustic force spectroscopy.
Results
MC3324 induced a global increase in H3K4me2 and H3K27me3 accompanied by
significant growth arrest and apoptosis in androgen-responsive and
-unresponsive PCa systems. LSD1/UTX inhibition downregulated AR at both
transcriptional and non-transcriptional level, showing cancer
selectivity, indicating its potential use in resistance to androgen
deprivation therapy. Since MC3324 impaired metabolic activity, by
modifying the protein and lipid content in PCa and CRPC cell lines.
Epigenetic inhibition of LSD1/UTX disrupted mitochondrial ATP
production and mediated lipid plasticity, which affected the
phosphocholine class, an important structural element for the cell
membrane in PCa and CRPC associated with changes in physical and
mechanical properties of cancer cells.
Conclusions
Our data suggest a network in which epigenetics, hormone signaling,
metabolite availability, lipid content, and mechano-metabolic process
are closely related. This network may be able to identify additional
hotspots for pharmacological intervention and underscores the key role
of KDM-mediated epigenetic modulation in PCa and CRPC.
Keywords: PCa, KDMs, AR, Metabolism, Lipid, Cell stiffness
Abbreviations: AR, androgen receptor; CRPC, castration resistant
prostate cancer; FASN, fatty acid synthase; KDM, lysine demethylase;
LSD1, lysine-specific demethylase 1; PCa, prostate cancer; PCs,
phosphatidylcholines; UTX, lysine-specific demethylase 6A
Highlights
* •
KDMs inhibition promotes increases H3K4me2 and H3K27me3 in PCa and
CRPC, which causes cancer selective pro-apoptotic pathways.
* •
KDMs regulate AR expression in PCa and CRPC, reducing ATP
production, mitochondrial respiration and intermediate metabolites
availability.
* •
Epigenetic controls metabolic pathways and redirects lipid
metabolic cascade.
* •
KDMs inhibition alters lipid distribution and composition,
impacting on physical and mechanical properties of PCa and CRPC.
1. Introduction
Prostate cancer (PCa) is the second leading cause of cancer death in
men worldwide [[61]1], and androgen receptor (AR) is a key driving
force in the tumorigenesis process. AR supports gene transcription
involved in primary tumor formation, progression, and metastasis
[[62]2]. The standard pharmacological treatment for PCa is based on the
use of androgen deprivation therapy (ADT) and AR ligand inhibitors
(e.g., enzalutamide and second-generation derivatives) to block hormone
signaling via sequestration of AR, thus preventing receptor nuclear
translocation and the subsequent activation of AR target genes [[63]3].
However, 20–40% of PCa patients do not respond to androgen antagonist
therapies and develop castration-resistant PCa (CRPC) [[64]4] as a
consequence of long-term adaptation [[65]5,[66]6]. An alternative
therapeutic strategy, independent of AR presence and androgen
antagonist sensitivity, therefore remains an urgent and unmet need for
CRPC patients. Intriguingly, unlike other types of cancer that often
exhibit the Warburg effect, PCa and CRPC present a “lipid-based
metabolic phenotype”, mainly supported directly and indirectly by AR
signaling [[67]7]. Indeed, AR promotes the transcription of genes
involved in lipid synthesis such as SREBF1 [[68]8], which promotes
fatty acid synthesis via fatty acid synthase (FASN) and acetyl-CoA
carboxylase (ACC), making lipids the primary source of ATP supporting
cancer growth and progression [[69]9,[70]10]. In addition, cellular
mechanics and metabolism are tightly interconnected. Thus, the
resulting changes in metabolism, in turn, affect the regulation of
every level of cell biology, including the physical and mechanical
properties of cells and tissues [[71]11]. To execute its function, AR
transcriptionally associates with epigenetic enzymes in multi-complexes
[[72]12]. Lysine (K)-specific demethylases (KDMs) physically interact
with AR [[73]13], and methylation of lysines is associated with a
repressive or permissive chromatin environment for AR-mediated
transcription. Specifically, LSD1 (KDM1A) and UTX (KDM6A) promote AR
expression [[74]14] and contribute to its transcriptional activity,
while inhibition of both epigenetic enzymes is associated with a
reduction in AR-mediated transcriptionalsignaling [[75][15], [76][16],
[77][17]]. We previously reported the link between KDM epigenetic
modulation and estrogen receptor alpha (ERα) in breast cancer (BC)
[[78]18]. Given the similarity between BC and PCa, here we translated
the anticancer effects of a dual KDM inhibitor of LSD1/UTX (MC3324) to
prostate models and provide data supporting the efficacy of epigenetic
treatment in both PCa and CRPC cell systems. In PCa and CRPC, LSD1 and
UTX inhibition i) globally increases methylation on H3K4me2 and
H3K27me3 residues, ii) downregulates AR independently of androgen
antagonist sensitivity, iii) perturbs ATP production, iv) alters
intermediate metabolite availability, v) blocks lipid metabolic
cascade, changing the amount and subclasses of cellular lipids, and vi)
alters physical and mechanical properties of cells. Inhibition of
LSD1/UTX changes the levels of ATP and metabolites in PCa and CRPC by
downregulating glycolysis and lipogenesis/lipolysis enzymes. As a
result, cancer growth is slowed. Metabolic substrates are fundamental
in determining lipid content and accelerating PCa cell proliferation,
growth, and plasticity, contributing to mechanical features of tumor
cells Here, the traditional paradigm of the histone code as being
regulated by epigenetic readers, writers, and erasers (e.g., LSD1, UTX)
working in association with transcriptional factors (e.g., AR) is
shifting to one in which the “ink” (metabolites and energy precursors)
is required to sustain PCa and CRPC. AR signaling and
metabolite-producing enzymes can be epigenetically regulated to weaken
bioenergy pathways, thus reducing proliferation index and overcoming
cell death resistance in PCa and CRPC, causing a disruption of the
tumor balance via alterations in mechano-metabolic properties. The
direct link between lipid metabolism and PCa is corroborated by
preclinical and clinical trials investigating the effect of FASN
inhibitors in the management of CRPC patients [[79]19,[80]20],
([81]https://clinicaltrials.gov/ct2/show/NCT04337580NCT04337580). Here,
we show that upstream epigenetic regulation of lipid metabolism via
LSD1/UTX inhibition may represent an additional approach for
interfering with hormone and metabolic signaling to overcome resistance
mechanisms associated with CRPC.
2. Methods
2.1. Chemicals
Tranylcypromine (TCP, #13492-01-8) and GSK-J4 (#1373423-53-0) were
purchased from Sigma–Aldrich (St Louis, MO, USA) and were used at a
final concentration of 100 μM and 5 μM, respectively. MC3324 was
synthesized by Prof. Mai's group (“Sapienza” University of Rome), as
reported in [[82]21] describing MC3324 structure. The compound was
obtained by fusing TCP and IOX1 KDM inhibitors and was used in each
experiment at a final concentration of 25 μM.
2.2. Cell culture
LNCaP, C4-2, and C4–2 B cell lines were purchased from ATCC (Milan,
Italy). 22RV1, DU145, and PC3 cells lines were kindly provided by Prof.
Carmen Jeronimo (Institute of Biomedical Sciences Abel Salazar [ICBAS]
University of Porto). PNT2 cell line was kindly provided by Prof.
Antimo Migliaccio (University of Campania “Luigi Vanvitelli”). Cells
were grown in Roswell Park Memorial Institute culture medium (RPMI;
EuroClone, Milan, Italy, ECB9006L), supplemented with 10%
heat-inactivated fetal bovine serum (FBS; Sigma–Aldrich, F7524),
antimicrobials (100 U/mL penicillin, 100 μg/mL streptomycin [EuroClone,
ECB3001D], 250 ng/mL amphotericin B [EuroClone, ECM0009D], 2 mM
l-glutamine [EuroClone, ECB3000D]), and 1% essential amino acids
solution (MEM; EuroClone, ECB3054D). All cell lines were cultivated at
37 °C with 5% CO[2] and were checked for mycoplasma contamination using
EZ-PCR Mycoplasma Test Kit (Biological Industries; #20-700-20).
2.3. Histone extraction
Histones were extracted as reported in [[83]22]. Briefly, after
treatment with the indicated compounds, cells were collected and washed
two times with PBS. Then, cell pellets were re-suspended in triton
extraction buffer (TEB; PBS containing 0.5% Triton X 100 [v/v],
2 mmol/L PMSF, 0.02% [w/v] NaN[3]); lysis was performed for 10 min with
stirring at 4 °C, and samples were centrifuged at 2000 rpm for 10 min
at 4 °C. After the wash step, samples were then precipitated in 0.2 N
HCl overnight at 4 °C for acid histone extraction. The day after, the
supernatant was recovered and protein concentration quantified by
Bradford assay (Bio-Rad Protein Assay Dye Reagent Concentrate,
#5000006) (Bio-Rad, California, U.S.A.). H3K4me2, H3K27me3, H3K9me2
(Diagenode, Ougrée, Belgium, pAB-035-050, C15410069, C15200154), and H4
(Abcam, Cambridge, UK, ab17036) were used according to the
manufacturer's instructions.
2.4. Western blot analysis
Cell pellets were suspended in lysis buffer (50 mmol/L Tris–HCl pH 7.4,
150 mmol/L NaCl, 1% NP40, 10 mmol/L NaF, 1 mmol/L PMSF, and protease
inhibitor cocktail). Next, the lysis reaction was carried out for
15 min at 4 °C, samples were centrifuged at 13,000 rpm for 30 min at
4 °C, and protein concentration quantified by Bradford assay (Bio-Rad
Protein Assay Dye Reagent Concentrate, #5000006). A total of 50 μg of
each sample was loaded on 8%, 10%, or 15% polyacrylamide gels and
electro-blotted on nitrocellulose membrane. Immunoreactive signals were
detected with a horseradish peroxidase-conjugated secondary antibody
(Bio-Rad, #1705046, #1706464). Primary antibodies were: AR (sc-52309),
P53 (sc-126), tubulin (sc-5286), and SQSTM1 (sc-48402) purchased from
Santa Cruz Biotechnology (Dallas, USA); BAX (ab53154), caspase 9
(ab9502), caspase 8 (ab9746), BCL2 (ab28725), p-eNOS (ab184154), eNOS
(ab5589), p-AKT (ab81283) and LSD1 (ab17721) from Abcam. Fatty Acid and
Lipid Metabolism Antibody Sampler Kit (#8335), GLUT1 (D3J3 A) and GAPDH
(D16H11) were purchased from Cell Signaling Technology (Danvers, MA,
USA); acetylated-tubulin (T7451) from Sigma–Aldrich. All antibodies
were used according to the manufacturer's instructions.
Semi-quantitative analysis was performed using ImageJ software (version
1.44), and the relative intensities are reported in figures.
2.5. RNA isolation and real-time PCR
Total RNA was purified as previously described in [[84]23]. RNA samples
were quantified using NanoDrop 1000 Spectrophotometer V3.8 (Thermo
Fisher Scientific), and their quality was checked using an Agilent RNA
6000 Nano Kit Guide (Agilent Technologies, Santa Clara, U.S.A.). VILO
cDNA Synthesis Kit (Invitrogen, Monza and Brianza, Italy, #11754050)
was used to convert RNA into cDNA. Then, 50 ng of cDNA was added with
1X SYBR Green PCR Master Mix (Bio-Rad #1708880), according to the
manufacturer's instructions. Primers used are listed in
[85]Supplementary File Table 4.
2.6. Cell cycle and cell death analysis
For cell cycle analysis and PI evaluation, PC3 cells were plated
(2 × 10^5 cells/mL) and after treatment with MC3324 at 25 μM for 6 h,
24 h, and 48 h were processed as described in [[86]24]. The results
were acquired on a BD Accuri TM C6 flow cytometer system (BD
Biosciences, New Jersey, U.S.A.).
2.7. shLSD1 transfection
shLSD1 vector (Santa Cruz, sc-60970) and the empty vector shSCR (Santa
Cruz, sc-108060) were used. 1 μg of each vector was transfected into
C4-2 cells using an Amaxa Nucleofector (Lonza), according to the
manufacturer's protocol. shLSD1 and shSCR were maintained in RPMI
medium (EuroClone) with 10% heatinactivated FBS (Sigma–Aldrich), 1%
glutamine (EuroClone), 1% penicillin/streptomycin (EuroClone), 0.1%
gentamycin (EuroClone), 500 μg/mL G418 (Gibco) and 1% essential amino
acids solution (EuroClone) at 37 °C in air containing 5% CO2.
Downregulation of LSD1 was confirmed by Western blot.
2.8. Cellular mitochondrial stress and ATP production
Metabolic status was investigated on a Seahorse XF24 Analyzer (Agilent
Technologies) with standard 24-well Seahorse microplates. A Mito Stress
Test Kit (Agilent Technologies, #103015) was used to assess oxygen
consumption ratio (OCR) and extracellular acidification rate (ECAR)
after MC3324 treatment. In brief, 2 × 10^4 cells were seeded into
plates 12 h prior to analysis. The medium was then replaced with 175 μL
of non-buffered RPMI containing 10 mM glucose, 2 mM glutamine, and 1 mM
pyruvate. The cells were then treated with 25 μM MC3324 for 3 h at
37 °C and incubated in a CO[2]-free incubator at 37 °C for 1 h to allow
for temperature and pH equilibration before being loaded into the XF24
Analyzer. The injection sequence was programmed as follows: 1st,
oligomycin (1 μM at final concentration); 2nd, carbonyl cyanide
m-chlorophenylhydrazone (FCCP; 1 μM at final concentration); 3rd,
rotenone and antimycin A (1 μM and 0.5 μM at final concentrations,
respectively). Data were analyzed with Wave software (version 2.2.0,
Seahorse Bioscience, Agilent Technologies, Santa Clara, CA, USA).
Experiments were performed in triplicates. P-values were calculated
using t-test. Statistical significance is expressed as ∗ p-value <0.05.
Standard deviations are reported as error bars.
2.8.1. Sample preparation and untargeted lipidomic analysis
After treatment for 6 h with MC3324 at 25 μM, PCa cells were harvested.
Lipids were extracted by adding to the cell pellet 1.5 mL chloroform:
MeOH (2:1 v/v), 0.5 mL ultrapure water, vortexed for 1 min, and
centrifuged at 3000 rpm for 10 min at 4 °C. The lower phase was then
transferred to a new tube for evaporation, and dried under nitrogen.
The dried extract was re-suspended with 200 μL isopropyl alcohol: MeOH
(1:1 v/v); 5 μL LPC (12:0) was added for internal standard. Each sample
was then centrifuged at 12,000 rpm for 10 min at 4 °C, and the
supernatant was transferred for liquid chromatography–mass spectrometry
(LC–MS) analysis. Separation was performed by Ultra Performance Liquid
Chromatography UPLC (Thermo Fisher Scientific, Ultimate 3000LC). The LC
system comprised an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm,
1.7 μm). The mobile phase was composed of solvent A (60% ACN + 40%
H[2]O + 10 mM HCOONH[4]) and solvent B (10% ACN + 90% isopropyl
alcohol + 10 mM HCOONH[4]) with a gradient elution (0–10.5 min, 30–100%
B; 10.5–12.5 min, 100% B; 12.5–12.51 min, 100−30% B; 12.51–16 min, 30%
B). The flow rate of the mobile phase was 0.3 mL/min. The column
temperature was maintained at 40 °C and the sample manager temperature
was set at 4 °C. Mass spectrometry parameters in ESI + mode were:
Heater Temp, 300 °C; Sheath Gas Flow Rate, 45 arb; Aux Gas Flow Rate,
15 arb; Sweep Gas Flow Rate, 1 arb; Spray Voltage, 3.0 kV; Capillary
Temp, 350 °C; S-Lens RF Level, 30%. Mass spectrometry parameters in
ESI- mode were: Heater Temp, 300 °C; Sheath Gas Flow Rate, 45 arb; Aux
Gas Flow Rate, 15 arb; Sweep Gas Flow Rate, 1 arb; Spray Voltage,
3.2 kV; Capillary Temp, 350 °C; S-Lens RF Level, 60%. Five quality
control (QC) samples were run to avoid small changes in both
chromatographic retention time and signal intensity.
2.8.2. Statistical analysis and software tool analysis
Raw data were acquired and aligned using Lipid Search software version
5.0 (Thermo Fisher Scientific) based on the m/z value and retention
time of the ion signals. Ions from both ESI+ and ESI- were merged and
imported into SIMCA-P multivariate data analysis software (version
14.1) for multivariate analysis. Principal component analysis (PCA) was
first used as an unsupervised method for data visualization and outlier
identification. Supervised regression modeling was then performed on
the dataset using partial least squares discriminant analysis (PLS-DA)
or orthogonal PLS-DA (OPLS-DA) to identify potential biomarkers.
Biomarkers were filtered and confirmed by combining the results of
variable importance in projection (VIP) values (VIP >1.0), t-test
(p < 0.05), FC > 2. A combination of databases was used to visualize
the pathway analysis: Lipid Maps ([87]https://www.lipidmaps.org/) and
Lipid Pathway Enrichment Analysis (LIPEA;
[88]https://lipea.biotec.tu-dresden.de/home) for the lipid species
present in treated and untreated conditions; lipidomeR Integrative
Visualizations of the Lipidome software was used to identify lipidomic
content as R package. Lipids were categorized by lipid classes and
presented on two-dimensional maps organized by lipid size and level of
saturation. Experiments were performed in triplicates for each
condition. P-values were calculated using t-test.
2.9. Co-immunoprecipitation
After induction of MC3324 at 25 μM for 6 h in C4-2 cells,
co-immunoprecipitation (Co-IP) of endogenously expressed AR protein was
performed using whole cell lysate (800 μg) in Co-IP buffer (10 mM Tris
pH 7.5, 50 mM NaCl, 10% glycerol, 1 mM EDTA, 1 mM DTT, 10 mM sodium
molybdate, 0.2 mM PMSF, 1X Roche protease inhibitor cocktail). Cell
lysis was obtained with sonication using Diagenode's Bioruptor
(Diagenode, B01020001). Protein A/G Plus Agarose (Santa Cruz, sc-2003)
was coated with AR antibody and IgG (Santa Cruz, sc-2025) and mixed
gently for 2 h at 4 °C for immunoprecipitation, in triplicate. A
fraction of the resulting complexes was washed three times with Wash
Buffer 1 (10 mM Tris pH 7.5, 50 mM NaCl, 10% glycerol, 1 mM EDTA, 1 mM
DTT, 10 mM sodium molybdate, 0.2 mM PMSF, 1X Roche protease inhibitor
cocktail), three times with Wash Buffer 2 (10 mM Tris pH 7.5, 50 mM
NaCl, 1 mM EDTA, 1 mM DTT, 10 mM sodium molybdate, 0.2 mM PMSF, 1X
Roche protease inhibitor cocktail), then denatured and eluted in 2X
bromophenol blue as a control for IP ([89]Supp. Figure 17). The
remaining IP fraction was then processed for downstream MS analysis.
First, proteins were digested and eluted from Ab-bead complexes by
adding trypsin (Promega, Trypsin/Lys-C Mix Mass Spec Grade, V5071) and
incubating for 30 min at room temperature. Eluates were then washed
with 2 M urea, 50 mM Tris pH 7.5, and 5 mM chloroacetamide, and then
fully digested overnight at room temperature [[90]25]. Digestion was
stopped by adding trifluoroacetic acid TFA to a final concentration of
1% v/v, and peptide cleanup was performed by dual C18 Stage tip and
SP3. Briefly, three C18 disks were stacked on top of each other and
transferred to a pipette tip. Tips were conditioned with methanol and
80% acetonitrile–0.5% acetic acid in LCMS-grade H[2]O (Buffer B) and
equilibrated with 0.5% acetic acid in LCMS-grade H[2]O (Buffer A).
Samples were loaded and washed with Buffer A, then eluted with Buffer
B. Peptides were then dried and kept at −80 °C until use. Any remaining
detergents from the IP protocol were removed using the SP3 protocol
[[91]26]. Dry peptides from the C18 cleanup were incubated with 2 μL of
a 50:50 mixture of SeraMag-A and SeraMag-B (Sigma–Aldrich, GE29343057)
beads and 200 μL acetonitrile. Beads were then washed once more with
pure acetonitrile and eluted by incubation with 2% DMSO in LCMS-grade
H[2]O. Peptides were then dried and re-suspended in a solution
containing 0.1% formic acid in LCMS-grade H[2]O. Data are available at
the ProteomeXchange Consortium via the PRIDE partner repository with
the dataset identifier PXD029249.
2.9.1. Proteomic analysis: cell lysis and protein digestion
PCa cells were treated for 24 h with MC3324 at 25 μM and were then
washed twice with PBS and harvested by centrifuge at 1200 rpm for
5 min. Next, the cell pellets were re-suspended in RIPA buffer
supplemented with protease inhibitors (Thermo Fisher Scientific, Halt
Protease Inhibitor; 0.5 M EDTA). Cells were lysed in a Bioruptor
sonicator bath (Diagenode) for 20 min (20 cycles: 30 s ON, 30 s OFF),
and lysates were centrifuged at 12,000 RPM for 30 min at 4 °C.
Protein-containing supernatants were collected for downstream
digestion. Protein concentration of whole cell lysates was measured by
bicinchoninic acid assay (Thermo Fisher Scientific), while AR IP
samples were measured on a DS-11-FX (DeNovix, DE, USA) at 280 nm. A
total of 100 μg of proteins was digested for downstream proteome
analyses. Solubilized proteins were precipitated using ice-cold
methanol and centrifuged down at 12,000 rpm for 20 min at 4 °C. Protein
pellets were re-suspended in 100 mM Tris buffer containing 100 mM
dithiothreitol and 4% w/v sodium dodecyl sulphate (SDS, pH 8.0), heated
at 95 °C for 30 min at 600 rpm. A total of 8 M urea in 100 mM Tris
buffer pH 8.0 solution was added to dilute SDS, samples were loaded on
30 KDa molecular filters (Sigma–Aldrich) and centrifuged at 12,000 rpm
for 20 min. Filters were washed twice with 8 M urea buffer and
incubated in 50 mM iodoacetamide in 8 M urea buffer for 30 min (in the
dark). Filters were washed four times (2 × 8 M urea buffer, 2 × 50 mM
triethylammonium bicarbonate buffer pH 8.0), and proteins were digested
with trypsin (enzyme–protein ratio 1:50) at 37 °C for 16 h under
agitation (650 rpm). Filters were then centrifuged at 12,000 rpm for
20 min to extract tryptic peptides.
2.9.2. Strong anion exchange peptide fractionation
Tryptic peptide mixtures were fractionated for downstream MS analysis
using strong anion exchange (SAX). Digested peptides were dried, then
re-suspended in Britton and Robinson Universal Buffer (BRUB; 20 mM
phosphoric acid, 20 mM boric acid, and 20 mM acetic acid in ultrapure
water) pH 11 and loaded on SAX Stage tips (Sigma–Aldrich) combined with
C18 filters (Sigma–Aldrich). SAX filter-containing tips were used to
elute peptides onto C18 tips (Sigma–Aldrich) using BRUB at decreasing
pH: 8, 6, 5, 4, and 3. C18 tips were then washed with 0.1% formic acid
solution, and peptides eluted with 0.1% formic acid and 80%
acetonitrile in ultrapure water. SP3 peptide purification was
subsequently performed on dried eluates. SP3 beads (Thermo Fisher
Scientific) were added to peptides, peptides were captured by adding
200 μL acetonitrile and eluted with 2% DMSO in water. Supernatants were
dried and stored at −80 °C until MS analysis.
2.10. High-resolution mass spectrometry for AR IP and whole proteome analysis
Digested peptides derived from AR IP and whole proteome digests were
re-suspended in 3% acetonitrile and 0.1% FA in ultrapure water and then
analyzed on a Q-Exactive HF-X mass spectrometer coupled to a Proxeon
EASY 1200 nano-liquid chromatography system (Thermo Fisher Scientific).
For AR IP and whole fractionated proteome samples, a volume
corresponding to 1 ug of digested peptides was analyzed. Digested
peptide mixtures were injected into a reverse phase EasySpray (Thermo
Fisher Scientific) analytical column (ID 75 μm × 50 cm C18 2 μm 100 Å
particle size). Gradient was run using LCMS-grade water with 0.1% FA
(solvent A) and 80% acetonitrile with 0.1% FA (solvent B) for 120 min.
Gradient was run as follows over a 350 μL/min flow-rate: 90 min10–30%
solvent B, 20 min 30–45% solvent B, 1 min 45–95% solvent B, and 9.5 min
95% solvent B. Eluting peptides were subjected to a 1.8 kV spray
voltage. Full scans were acquired at 60,000 resolution and the 15 most
intense ions were fragmented using high-energy induced collision
dissociation, whose spectra were collected at 15,00 resolution.
Precursor ions with charge 1 and > 6 and with intensities lower than
1.7 × E4 were excluded from triggering fragmentation. Ion accumulation
time was set to 60 msec. Automatic gain control was set to 1xE5.
Dynamic exclusion was enabled and set to 20 s. Thermo RAW files were
acquired using Xcalibur software (version 4.1).
2.11. Analysis of mass spectrometry data
MS analysis-derived RAW files were analyzed using MaxQuant (version
1.6.14.0) and MS spectra searched using the Andromeda search engine.
The Uniprot-Swissprot human proteome database (version released:
2020.02.24) was used for database searches. Selected protease was
trypsin. Carbamidomethylation of Cys residues was selected as fixed
modification. Met oxidation and acetylation of N-terminal residues were
selected as variable modifications. The Label-free Quantification (LFQ)
option was selected. Identification of peptides resulting from missed
cleavages was allowed. Precursor ion tolerance was 20 ppm and 4.5 ppm
for first and main searches, respectively. Match-between-run option was
enabled, and settings left to default. MaxQuant search-derived protein
intensities were used for statistical analyses. Protein tables were
filtered for protein q-value (<0.01), contaminant (excluded), reverse
sequences (excluded), and unique peptides (at least 1).
2.11.1. Statistical analysis
AR IP and proteome tables were filtered for missing data (cutoff: 30%),
and log2 transformed. Pseudocount (value: 0.1) was applied to the
protein table prior to log2 transformation. Differential expression was
assessed by Welch-corrected t-test (AR IP) and DeqMS (peptide
identification-adjusted linear model; proteome). Pathway enrichment
between conditions (treated vs untreated) was performed using Gene Set
Enrichment Analysis (GSEA) against the Hallmarks database (version
5.2). Settings were as follows: permutation type, gene set; scoring,
classic; metric, t test. Other parameters were kept to default
settings. False discovery rate cutoff to call significant pathways was
set to 0.25. Normalized enrichment scores were plotted to define
enrichment levels. All data were analyzed in R (version 3.6). Data are
available at the ProteomeXchange Consortium via the PRIDE partner
repository with the dataset identifier PXD029525.
2.11.2. Metabolomic analysis: metabolite extraction for nuclear magnetic
resonance (NMR) analysis
PCa cells were treated for 6 h with MC3324 at 25 μM and were washed
twice with PBS and then harvested by centrifuge at 1200 rpm for 5 min.
A dual phase extraction procedure introduced by Bligh and Dyer in 1959
[[92]27] was employed, with slight modifications, to extract
intracellular metabolites, as already reported elsewhere [[93]28].
Briefly, 6 mL cold methanol (−20 °C) and 6 mL chloroform were added to
the original aqueous solution (5.4 mL) containing quenched cells to
obtain a mixture of water, methanol, and chloroform in a volume ratio
of 0.9:1:1, corresponding to a total volume of 17.4 mL. The mixture was
then incubated for 20 min on ice and repeatedly vortexed to facilitate
the extraction. Next, centrifugation at 4000g at 4 °C was performed for
20 min to obtain a two-phase extract made of an upper phase containing
water-soluble intracellular metabolites and an organic lower phase
including non-polar metabolites such as lipids. The skin-like layer
between the two phases entrapped proteins and macromolecules. The upper
and lower phases were then separated and carefully transferred into
different falcon tubes. Solvents were completely removed from both
fractions using a vacuum concentrator (hydrophilic phase) and under a
gentle flow of N[2] gas (organic phase).
2.11.3. NMR spectroscopy
Aqueous cell extracts were dissolved in 600 μL D[2]O, briefly vortexed,
and transferred into 5-mm NMR tubes. All one-dimensional 1H NMR spectra
were acquired at 300 K on a Bruker Avance NEO 600 MHz spectrometer
(Bruker BioSpin, Rheinstetten, Germany) equipped with a QCI cryo-probe
set for 5-mm sample tubes and a cooled SampleJet autosampler. The 1H
NMR spectra of hydrophilic cell extracts were acquired with Topspin 4.1
(Bruker BioSpin) using the ‘noesygppr1d’ pulse sequence allowing for a
quantitative evaluation even closer to the water signal [[94]29], which
was presaturated at 4.698 ppm. All the experiments were performed with
an acquisition time of 3.67 s, a relaxation delay of 4 s, receiver gain
of 101, 128 scans, 4 dummy scans, and a spectral width of 17,857 Hz
(29.755 ppm). All samples were automatically tuned, matched, and
shimmed. Prior to Fourier transformation, free induction decays were
multiplied by an exponential function equivalent to a 0.3-Hz
line-broadening factor. The transformed spectra were then automatically
corrected for phase and baseline distortions and calibrated using
TopSpin built-in processing tools.
2.11.4. Metabolite identification
Assignment of the hydrophilic metabolites was achieved by i) analysis
of literature data [[95]28,[96]30], ii) comparison with the chemical
shifts of metabolites in the Human Metabolome Database (HMDB), and iii)
peak fitting routine within the spectral database in Chenomx NMR Suite
5.0 software package (Chenomx, Edmonton, AB, Canada). A representative
1H NMR with the relative assignment is reported in [97]Supp. Figure 21.
2.11.5. NMR data reduction and processing
NMR spectra were imported into MATLAB (Mathworks, Natick, MA, USA,
R2015b), where spectral regions above 10 ppm and below 0 ppm were
removed because they contained only noise. To correct for spectral
misalignment, an interval-based alignment step was carried out using
the icoshift algorithm [[98]31] and choosing the alanine doublet at
1.49 ppm as reference signal. Then, in order to reduce the model
complexity, peak areas of the well-separated resonances of 20 selected
metabolites were manually integrated and submitted to data analysis as
a data matrix made of 18 rows (samples) × 20 columns (metabolites).
This data matrix was then submitted to the PLS toolbox version 8.6.1
(Eigenvector Research, Manson, WA, USA), where it was normalized
according to the total area (1-norm) and then autoscaled. Autoscaling
employs standard deviation as a scaling factor, thus giving all
metabolites the same chance to affect the model and the mean-centering,
which is needed to compute the Principal Component Analysis (PCA).
2.12. Bulk RNA sequencing analysis
PCa, advanced PCa, benign prostatic hyperplasia (BPH) and CRPC RNA
sequencing data were downloaded from the NCBI repository [99]GSE80609
([100]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80609).
Statistical analyses were performed by using R (version 3.6.4).
Fragments Per Kilobase of exon per Million mapped reads (FPKM) was
normalized by the quantile method, log2 transformed, and
median-centered across genes and samples. To compare sample subgroups
in terms of gene expression, the DESeq package that utilizes a negative
binomial model was used to detect differentially expressed genes from
count data matrix. Expression differences in genes were considered
statistically significant if the p-value was <0.05 and the fold
difference in expression between two sample groups was ≥1.
2.12.1. Acoustic force spectroscopy cell seeding and measurements
Cell suspensions were harvested and centrifuged at 1200 rpm for 5 min,
and the supernatant was discarded. Cells were re-suspended in RPMI at
an average concentration of 1.3 × 106 cells ml−1 and transferred into a
1.5 mL Eppendorf tube. Then, 40 μL of 7.9 μm-diameter silica beads were
washed and re-suspended in 80 μL medium. The beads were mixed with the
cell suspension at a cell-to-bead ratio of 1:5, and the mixture was
injected into the flow channel of the AFS chip up to a desired cell
confluence of 60–70% and incubated at 37 °C with 5% CO[2] for 3 h to
allow the cells to attach to the bottom of the AFS channels under
static conditions. After incubation, the AFS chip was connected to a
peristaltic pump for growth media perfusion. Flow rate was set to
30 μL/min, until culture achieved 90–100% confluency. All perfusion
experiments were performed within a dry incubator to avoid damaging the
AFS chip. Upon reaching full confluency, the chip was placed on the AFS
microscope stage, and the temperature controller was set to the
physiological temperature of 37 °C. To measure the non-linear creep
response of cells, a constant force (ranging from 1.026 nN to
1.467 nN), was applied for 20 s. The displacement at each step was
fitted by a creep-compliance model (see section below: 2.16.2). To
measure the effect of MC3324 on PC3 cell mechanics, creep responses
were measured at 3 h, 6 h, and 24 h of incubation. All measurements
were performed at 37 °C with a peak-to-peak driving voltage of 30 Vpp
at 14.51 MHz frequency. Images were acquired with a bright-field
inverted microscope equipped with a 1.3 MP camera recording at 60 Hz
(IDS, UI-324CP) in combination with an air 20 × 0.75 NA objective
(Nikon, CFI Plan APO, VC 20×, MRD70200). The bead z-position was
determined using a predefined look-up table (LUT), a library of radial
profiles as a function of z position with 100 nm steps, created from a
series of microsphere images prior to application of the acoustic
force.
2.12.2. Acoustic force calibration
To determine the acoustic radiation force, we performed a force-balance
on acoustically driven beads. The acoustic force results from the
standing acoustic wave and pushes the bead toward the acoustic node.
The acoustic wave amplitude is proportional to the voltage applied to
the piezo-element, which excites the wave according to:
[MATH: F =cV2 :MATH]
(1)
where F is the acoustic force, V the voltage applied to the
piezo-element, and c the conversion factor.
The conversion factor is found by a setup calibration. During
calibration (i.e., without a cell), a single bead is subjected to
gravitational, acoustic, and drag forces according to:
[MATH:
Facoust
ic=Fstokesdrag+Fgravity–Fbuoyancy :MATH]
(2)
[MATH:
Facoust
ic=(μ_beadγ_Brenner)+(4/3πgrˆ3ρ_bead)–(43πgr3ρmedium) :MATH]
(3)
with
[MATH:
γBrenner=6πηr1−9r8h+r32h3−57r
4100r4
+r55h5+7r1
1200h11
:MATH]
(4)
where g is gravity, r is the bead radius, ρbead is the density of the
silica bead, ρmedium is the density of PBS, ubead is the bead velocity,
γBrenner is the correction factor for Stokes drag coefficient, η is the
viscosity of the medium, and h is the height of the bead center to the
surface. Brenner's drag coefficient was determined by measuring the
viscosity of the bulk fluid and directly inserting it into Equation
[[101]6]. In order to find the experimental local viscosity, η, and to
use it for the determination of the effective drag coefficient,
γBrenner, a method based on the terminal velocity of the bead, was used
as previously published [[102]32]. Briefly, the terminal velocity of
the bead is tracked as it settles from the acoustic node toward the
bottom.
2.12.3. AFS data fitting
To quantify the viscoelastic properties of PC3 cell line, the standard
linear liquid (SLL) model was utilized, where the creep compliance of
the cell is captured by:
[MATH: J(t)=(tηa+ηc)+((ηa)2Ea(ηa+ηc)2)∗(1−e(−tηa∗ηcEa(ηa+ηc)))
:MATH]
(5)
where Ea is the elasticity associated with the cytoskeleton, ηa the
viscosity associated with the cytoskeleton, and ηc is the background
viscosity. Each force step (i.e., z-height versus time) was first
converted to J(t) according to:
[MATH: J(t)=z(t)F×πr :MATH]
(6)
where z(t) is the extension curve in z-direction obtained by pulling on
the cell, F is the applied force, and r is the particle radius. J(t)
was then plotted as a function of time and Equation [[103]1] was fitted
to the resultant curves.
3. Results
3.1. LSD1/UTX inhibition induces AR signaling downregulation and
antiproliferative effects
MC3324 globally increased dimethylation of histone H3 at lysines 4 and
9 (H3K4me2, H3K9me2), two LSD1 targets, and trimethylation of histone
H3 at lysine 27 (H3K27me3), a UTX target, in both PCa (LNCaP) and CRPC
(C4-2) cell lines ([104]Figure 1A). Epigenetic rebalance was coupled
with a strong antiproliferative effect and increased anticancer
efficacy compared to TCP and GSK-J4 TCP, commercially available
inhibitors of LSD1 and UTX, respectively, used either alone or in
combination (1:4 ratio) ([105]Figure 1B). Among all available LSD1
inhibitors, TCP was chosen because MC3324 is constituted with its
active scaffold. Cell growth was impaired in both PCa and CRPC models,
suggesting that dual KDM inhibition may be a promising interfering
strategy in endocrine-resistant or AR-null PCa ([106]Supp. Figure 1).
Orthogonal assays have been applied to PC3 cell line to confirm cell
death induction and cell cycle arrest ([107]Supp. Figure 2). The
antiproliferative effect was associated with the induction of
pro-apoptotic proteins after treatment ([108]Figure 1C and [109]Supp.
Figure 3A-B). Furthermore, we observed an overexpression of TP53
([110]Figure 1D and [111]Supp. Figure 3C), a major tumor suppressor,
whose upregulation is associated with remodulation of cancer signaling
and reduced oncogenic activity [[112]33]. MC3324 induced time-dependent
downregulation of AR at protein and mRNA level ([113]Figure 1E–F). AR
downregulation was also confirmed in C4–2 B cells ([114]Supp.
Figure 3D), CRPC bone metastasis model androgen-independent. By
inhibiting the epigenetic modifiers LSD1 and UTX, treatment with MC3324
reduced cell survival and activated cell death mechanisms. MC3324
cancer selectivity was shown in the normal prostate PNT2 cell line.
Although a reduction in proliferation was observed ([115]Supp.
Figure 4A), H3k4me2 and H3k27me3 were not increased ([116]Supp.
Figure 4B), caspases were not cleaved ([117]Supp. Figure 4C), and,
interestingly, AR expression levels were not downregulated ([118]Supp.
Figure 4D). Taken together, these findings suggest the epigenetic
modulation of LSD1/UTX as an alternative/additional therapeutic
approach in PCa and CRPC phenotypes.
Figure 1.
[119]Figure 1
[120]Open in a new tab
Epi-downregulation in androgenic signaling and apoptotic triggering. A)
Western blot analysis of histone methylation levels after MC3324
treatment (25 μM) in LNCaP and C4-2 cells. Increase in H3K4me2,
H3K9me2, and H3K27me3 levels was evaluated after 6 h and 24 h of MC3324
treatment. The relative increase was quantified with ImageJ software
(1.46r, NIH, USA) and reported as normalized to control intensity.
Experiments were performed in triplicates. B) MTT assay results
expressed as fold change in treated cells normalized to the mean of
control in LNCaP and C4-2 cells after induction with MC3324 (25 μM),
tranylcypromine (TCP; 100 μM) and GSK-J4 (25 μM) for 24 h, 48 h, and
72 h. Experiments were performed in triplicates. Standard deviations
are reported as error bars, while p-values were calculated using
t-test. Statistical significance is expressed as p-value ∗ <0.05, ∗∗
<0.01, ∗∗∗ <0.001. C) Western blot analysis of extrinsic and intrinsic
apoptotic proteins after MC3324 treatment (25 μM) after 6 h and 24 h in
LNCaP and C4-2 cells. Experiments were performed in triplicates. D)
Western blot analysis of BCL2 and BAX acting as anti-apoptotic and
pro-apoptotic proteins, respectively, after MC3324 treatment (25 μM)
after 6 h and 24 h in LNCaP and C4-2 cells; relative expression of TP53
after treatment is also shown. Experiments were performed in
triplicates. E) Real-time PCR data, in triplicates, showing AR
downregulation by MC3324 treatment (25 μM) in LNCaP and C4-2 cells
after 6 h and 24 h. F) Western blot analysis showing AR downregulation
in LNCaP and C4-2 cells after MC3324 treatment (25 μM) for 6 h and
24 h.
3.2. LSD1/UTX inhibition affects maximal respiration and ATP production
Metabolic evolution is recognized as a fundamental hallmark of cancer,
and AR is known to orchestrate metabolism and biosynthesis at key
regulatory steps in PCa and CRPC [[121]9]. The AR interactome of a CRPC
cell line was analyzed following short-term MC3324 treatment, and AR
interactors were identified and grouped by biological pathway
([122]Figure 2A–B). The results returned proteins active in metabolism
and metabolic pathways such as ACAT1 and ELOVL5, both involved in fatty
acid synthesis, LDHA, active in the conversion of pyruvate to
l-lactate, and DLD, a mitochondrial enzyme that plays a vital role in
energy production [[123]34]. The displacement of AR metabolic
interactors, following MC3324 treatment, was coupled with the
alteration of pathways involved in cellular energy activity
(glycolysis, lipolysis, and glutaminolysis) in PCa and CRPC. Globally,
MC3324 treatment at 25 μM for 3 h reduced ATP production, impairing
maximal mitochondrial respiration capacity in LNCaP and C4-2 cells
([124]Figure 3). Lower mitochondrial efficiency was also observed in
the other CRPC and AR-null cell lines ([125]Supp. Figure 5), suggesting
that LSD1/UTX inhibition shuts down prostate metabolism independently
of AR expression. These findings suggest an impairment in mitochondrial
functions with a block of energetic processes that disrupts the
onco-metabolic balance supporting uncontrolled proliferation of PCa and
CRPC cells.
Figure 2.
[126]Figure 2
[127]Open in a new tab
Interactome of AR. A) AR interaction network of the C4-2 CRPC cell
line. Proteins identified by AR pulldown assay after treatment with
MC3324 (25 μM) were annotated and clustered. B) GO term annotation for
metabolic functions associated with AR interactors.
Figure 3.
[128]Figure 3
[129]Open in a new tab
Mitochondrial stress test. Metabolic flux analysis reported as oxygen
consumption rate (OCR) measurements performed in MC3324-treated and
untreated (control) LNCaP and C4-2 cell lines. P-values were calculated
using t-test based on triplicate values. Statistical significance is
expressed as ∗ p-value <0.05. Standard deviations are reported as error
bars.
3.3. MC3324 treatment alters metabolic gene expression profiles
Reduced mitochondrial activity leads to the impairment of cancer cell
homeostasis. The reduction in ATP production is due to the alteration
of genes involved in glycolysis and lipid pathways following MC3324
treatment. We investigated the differential expression of enzymes
catalyzing key reaction steps in glucose transport and conversion and
in beta oxidation of fatty acids. MC3324 treatment at 6 h induced GLUT1
upregulation in LNCaP, PCa cell line, C4-2, and DU145 CRPC cells
([130]Figure 4), probably as a protective mechanism in response to ATP
production impairment. A similar trend was observed for HKII, the
second enzyme involved in glycolysis; complete downregulation of GLU1
and HKII in this pathway was detected at 24 h of MC3324 treatment.
Because PCa and CRPC are lipid-enriched cancers ([131]Supp. Figure 6),
we also investigated ATP citrate lyase (ACLY), which acts as a bridge
between glycolysis and lipogenesis [[132]35]. Following MC3324
treatment, ACLY mRNA expression was reduced in AR-positive and
androgen-sensitive LNCaP cells, in AR-positive and androgen-insensitive
C4-2 cells, and in AR-null DU145 cells. The different response to
MC3324 treatment was also observed for other metabolic genes
([133]Figure 4), highlighting PCa heterogeneity. Interestingly, the
lipid metabolic gene FASN, encoding the key enzyme in fatty acid
synthesis, was found downregulated after MC3324 treatment at 24 h in
PCa and CRPC models. FASN plays a pivotal role in the acquisition of
tumor phenotype and directly favors PCa progression [[134]36], as
confirmed by its overexpression in PCa and CRPC associated with poor
outcomes [[135]37]. Bulk RNA sequencing re-analysis ([136]GSE80609) of
patients with different degrees of disease severity (PCa = 16, advanced
PCa = 9, CRPC = 12) compared to those with benign prostatic hyperplasia
(BPH = 8) revealed FASN overexpression in all cancer stages ([137]Supp.
Figure 7), further strengthening the correlation between PCa/CRPC and
the “lipid phenotype” acquired in oncogenic onset [[138]38]. To further
confirm that KDMs play a key role in AR expression and metabolic
control of prostate cancer we silenced LSD1 with shRNA. The
downregulation of LSD1 ([139]Supp. Figure 8A), similarly to its
pharmacological inhibition, induces AR downregulation together to key
metabolic proteins downregulation ([140]Supp. Figure 8B). Although the
involvement of LSD1 in the regulation of lipogenic genes is already
well known [[141]39], the role of UTX is less clear [[142]40,[143]41].
These findings suggest a potential role for KDM enzymes in cancer
treatment through repression of the metabolic oncogene FASN. Our
results show that metabolic downregulation via LSD1/UTX inhibition may
represent a novel strategy to modulate metabolic pathways in PCa and
CRPC, overcoming limitations related to AR expression and androgen
sensitivity.
Figure 4.
[144]Figure 4
[145]Open in a new tab
Glycolytic and lipogenesis/lipolysis gene expression. Graphs from
real-time PCR data showing different expression levels of genes
involved in glycolysis and in lipogenesis/lipolysis processes in LNCaP,
C4-2, and DU145 cell lines after 6 h and 24 h of MC3324 treatment at
25 μM. Standard deviations are reported as error bars based on
triplicate values. Primers used are available in [146]Supplementary
File Table 4.
3.4. LSD1/UTX inhibition disrupts lipid metabolism
The differential mRNA expression observed in key steps of energy
processes may account for the reduced metabolic capacity of PCa and
CRPC following MC3324 treatment. To further confirm this hypothesis, we
performed proteomic analyses to investigate the relative abundance of
enzymes involved in metabolism. Specifically, we selected two CRPC cell
lines recognized as having accelerated lipid metabolism: AR-positive
and androgen-insensitive C4-2 cells and AR-null PC3 cells. The Pearson
correlation and hierarchical clustering on the proteomic dataset
indicated a good correlation ([147]Supp. Figure 9), and normalized
enrichment score showed a reduction, after MC3324 treatment, in
oxidative phosphorylation, fatty acid metabolism, adipogenesis, and
glycolysis ([148]Figure 5 A-B-C), all pathways directly connected to
metabolic processes. To further validate these findings, we
investigated the expression level of proteins in key steps of lipid
metabolism in both systems ([149]Figure 5 D). We observed a
downregulation of ACC and FASN, pivotal proteins in lipogenesis, as
well as downregulation of ACLY and PDH, involved in carbohydrate and
lipid communication, and in acetyl-CoA procurement, respectively. These
findings indicate an epigenetic regulation of metabolic signaling,
highlighting the existence of a druggable axis between LSD1/UTX
inhibition and metabolic circuits promoting the more aggressive
phenotype. We also observed a downregulation of pathways equally
important in supporting tumor growth and progression ([150]Supp.
Figure 10), indicating a depletion of androgen response signaling,
accompanied by reduced protein secretion and a reduction of the pathway
driven by MYC, a known oncogene overexpressed in cancer ([151]Supp.
Figure 11). While it is not completely clear at which level the cascade
of events begins, the pleiotropic effect observed after LSD1/UTX
inhibition culminated in proliferative arrest and cell death. Indeed,
metabolic signaling is not only related to energy processes, but
mediates and enhances other cellular activities with multiple effects
such as duplication and cellular trafficking. Interestingly, mTORC1
pathway was also reduced after MC3324 treatment ([152]Supp. Figure 11).
Since mTORC1 is widely associated with autophagic activity, we
investigated the main actors in autophagy to further validate whole
proteome findings. In C4-2 cells, Western blot analysis showed an
increase in phospho-AKT and upregulation/phosphorylation of eNOS, an
AKT downstream target, as well as downregulation of acetylated tubulin
and upregulation of SQSTM1 ([153]Supp. Figure 12A), with upregulation
of phosphatidylinositol species ([154]Supp. Figure 12B). Autophagy
impairment affects fatty acid recycling [[155]42], thus showing a
further block of autophagic branch ([156]Supp. Figure 12C). All these
findings indicate that LSD1/UTX inhibition switches off oncogenic
signaling and lipid metabolism, acting as the driving energy source in
CRPC systems. The autophagy axis also seems to be disrupted,
suppressing further energy supply.
Figure 5.
[157]Figure 5
[158]Open in a new tab
Switch-off of metabolic pathways. Enrichment plot for C4-2 (A) and for
PC3 (B) cells showing that MC3324 treatment downregulates key
metabolic- and lipid-related pathways. C) GSEA enrichment of modified
pathways after MC3324 treatment in C4-2 and PC3 cell lines. Experiments
were performed in triplicates for each condition. D) Western blot
analysis in C4-2 and PC3 cells after MC3324 treatment (25 μM) for 6 h
and 24 h in ACC, FASN, ACLY, and PDH genes involved in lipid
metabolism. Experiments were performed in triplicates. The relative
increase was quantified with ImageJ software (1.46r, NIH, USA). Images
were normalized using gapdh/tubulin as loading control.
3.5. MC3324 treatment alters untargeted lipidomic contents
Having observed mitochondrial ATP production impairment with a
modulation of metabolic genes following LSD1/UTX inhibition in PCa and
CRPC models, we explored the evolution of lipid species after MC3324
treatment. We investigated lipid content by carrying out an untargeted
lipidomic analysis comparing MC3324-treated (6 h) and untreated PCa
cells. The untargeted lipidomic analysis was performed using liquid
chromatography high-resolution mass spectrometry (LC–MS/MS) ([159]Supp.
Figure 13A-B) ([160]Supplementary File Tables 2–3). In total, we
identified more than 15 different lipid classes modulated after MC3324
treatment, and more than 300 lipid species were quantified for each
prostate cell line, detected in electron spray positive-ion and
negative-ion mode. The classes identified included:
phosphatidylcholines (PCs), phosphatidylethanolamines,
triacylglycerols, ceramides, phosphatidylserines, sphingomyelins,
phosphatidylglycerols, cholesteryl esters, and diacylglycerols
([161]Figure 6A). Heatmaps show two panels of differentially expressed
lipid species ([162]Supp. Figure 14) after MC3324 treatment. In our
analysis, differential expression of PCs indicated an increase in
saturated species in all cells, but an overall reduction in unsaturated
classes, regardless of AR expression or androgen sensitivity
([163]Figure 6B). PCs can be synthetized from phosphatidylethanolamines
by a process of trimethylation on the primary amine in a dense network
of enzymatic activities [[164]43,[165]44]. Real-time PCR analysis
showed downregulation of phosphatidylethanolamine N methyltransferase
(PEMT) mRNA levels after 24 h of MC3324 induction in both PCa and CRPC
models, suggesting the impairment in PC production, which can affect
tumorigenic progression to the extent of inducing an arrest in
proliferation and cell death ([166]Figure 6C). Furthermore, metabolomic
analysis of selected PCa and CRPC cell lines ([167]Supp. Figure 15)
orthogonally confirmed the statistically significant reduction in PC
lipid species upon MC3324 treatment ([168]Supp. Figure 16). PCa is
reported to modulate PC content by increasing the production of
unsaturated fatty acids [[169]45]. The study described three
unsaturated lipids, PC(38:5), PC(40:3), and PC(42:4), which when
overexpressed are associated with PCa. Our lipidomic analysis revealed
downregulation of all these three lipid species after MC3324 treatment,
when expressed ([170]Supp. Figure 17). Taken together, our results show
that LSD1/UTX inhibition regulates PC expression in PCa and CRPC cell
lines by inducing metabolic plasticity in lipid acquisition. Dual
targeting of lipid supply may be a valid treatment approach to overcome
therapeutic resistance associated with AR expression or androgen
sensitivity.
Figure 6.
[171]Figure 6
[172]Open in a new tab
Lipid content alteration. A) Multiple pie chart showing relative lipid
class content in prostate cell lines. The six rings correspond to
different PCa cell lines: 22RV1 (red), C4-2 (blue), C4–2 B (yellow),
DU145 (violet), LNCaP (green), PC3 (orange); for each cell line,
relative lipid class subgroups are highlighted with different colors
inside the circle. B) Phosphatidylcholine (PC) species based on
saturation degree (x axis) and length of fatty acid (y axis) grouped by
saturated (yellow) and unsaturated (red) matrix. C) Enzymes involved in
phosphatidylserine (PS) synthesis from phosphatidylethanolamines (PE)
or phosphatidylcholines (PC). Relative expression of PTDSS1, CEPT1,
PEMT, and CHPT1 genes from real-time PCR data after MC3324 treatment
for 24 h in the indicated PCa cell lines. Experiments were performed in
triplicates.
3.6. MC3324 treatment modulates lipid classes associated with cell death
Acyl carnitine is reported to interfere with key functional properties
of progression and angiogenesis in PCa both in vitro and in vivo
[[173]46]. This lipid class was shown to decrease acetyl-CoA inside
mitochondria, with reduced ATP production in the tricarboxylic acid
cycle, reducing cell proliferation through apoptosis and suggesting a
protective role against PCa. In our lipidomic dataset, acyl carnitine
was found upregulated after MC3324 treatment in LNCaP, DU145, and PC3
cell lines ([174]Supp. Figure 18). Several studies describe a strong
interconnection between lipids, mitochondrial activity, and cell death.
The sphingolipid class is known to play a role in cell death, cell
survival, and therapy resistance in many cancers, including PCa
[[175]47,[176]48], and ceramide, a key molecule in sphingolipid
metabolism, is generally associated with antiproliferative effects and
mitochondrial-mediated apoptosis. In our dataset, the sphingomyelin
class was mainly upregulated in all six cell lines after MC3324
treatment ([177]Supp. Figure 19). In addition, we observed a
substantial difference in the number of species found differentially
expressed. AR-positive cell lines showed a large number of
sphingomyelins (LNCaP = 33, C4-2 = 23, C4–2 B = 22, 22RV1 = 11)
compared to the small number found in AR-null cell lines (DU145 = 3,
PC3 = 4) ([178]Supp. Figure 19). Other members of the sphingolipid
class were also altered, suggesting that their modulation could
contribute to mitochondrial degeneration and favor cell death.
3.7. MC3324 treatment stiffens PC3 cells
It is widely acknowledged that cells can be described as mechanical
systems with properties orchestrated by biochemical cues [[179]49].
Tumorigenesis induces substantial alterations in both structure and
composition of the actin network [[180]50,[181]51] resulting in a
general loss of cell rigidity, which, in turn, provides critical
advantages for matrix infiltration and migration [[182]52,[183]53]. We
therefore investigated whether MC3324 is able to increase the stiffness
of PCa cells, potentially lowering their metastatic potential. We
measured the mechanical properties of PC3 cells using acoustic force
spectroscopy, a high-throughput force spectroscopy technique that has
only recently been adapted to probe single-cell mechanics
[[184]54,[185]55]. PC3 cells were confined between silica beads (7.9 μm
in diameter) and the glass surface of the AFS microfluidic chip.
Acoustic forces were applied via a piezo element, which generates
standing acoustic waves that push the beads up toward the acoustic
node, instantaneously stretching the cells. From the resulting
stress–strain curves, the creep compliance (the total load strain per
unit of stress) was determined, from which the viscoelastic properties
of the PC3 cells were obtained. Cells were measured before the addition
of MC3324 followed by rapid solution exchange for exposure to the
compound. Upon MC3324 exposure, cells underwent a drastic change in
morphology, taking on a distinctly flatter appearance ([186]Figure 7A).
Example traces for creep compliance at each time point of MC3324
exposure are shown in [187]Figure 7B. MC3324 caused significant
stiffening of PC3 cells in a time-dependent manner ([188]Figure 7C).
Cell stiffness is associated with the apoptotic response of cells
[[189]56,[190]57], which our data revealed to be upregulated after
addition of MC3324. Inhibition of ACC activity with soraphen A is
reported to increase the saturation of fatty acids in LNCaP cells
[[191]58], and we also found upregulated saturated PC levels in PCa
after KDM inhibition, which caused an increase in the elastic modulus
of the lipid bilayer. The viscous modulus associated with the
cytoskeleton also showed a significant time-dependent increase
([192]Figure 7D). The increase in cell stiffness was accompanied by a
significant time-dependent increase in cytoplasmic viscosity
([193]Figure 7E). Cytoplasm rheology critically depends on the spatial
and temporal scale of the deformation. In our experimental conditions,
the cytoplasm response was more similar to that of a viscous fluid,
which was previously linked to the effect of ATP depletion [[194]59], a
process also induced by MC3324. Taken together, these results indicate
that PCa mechanics and metabolism can be molecularly and epigenetically
targeted.
Figure 7.
[195]Figure 7
[196]Open in a new tab
Mechanical and physical properties. MC3324 alters the viscoelastic
properties of PC3 cells. A) PC3 culture inside the AFS chip at
different time points of MC3324 exposure. B) Example traces of PC3
creep compliance at different time points of MC3324 exposure. C) Graph
showing that MC3324 significantly increases the average elastic modules
Ea in a time-dependent manner (p < 0.0001). D) Graph showing that
MC3324 significantly increases the average viscosity ηa in a
time-dependent manner (p < 0.0001). E) Graph showing that the average
background viscosity ηc of PC3 cells significantly increases upon
stimulation by MC3324 in a time-dependent manner (p < 0.0001).
4. Discussion
CRPC continues to be a major burden for public healthcare systems, and
overcoming treatment resistance requires the identification of
alternative therapeutic targets/strategies. In PCa and CRPC, the KDMs
LSD1 and UTX are overexpressed and co-localize with both AR and
promoters of its transcription, triggering a self-sustaining loop
[[197][60], [198][61], [199][62]]. This functional relation suggests a
potential role for epigenetic compounds in modulating oncogenic
signaling and highlights their possible use in breaking this loop. The
present study reveals that the indirect downregulation of AR through
LSD1/UTX inhibition induces histone methylation on residues H3K4me2,
H3K9me2, and H3K27me3, thereby promoting cell death and a metabolic
shift in PCa and CRPC, as well as an alteration in cellular mechanics.
We investigated the effect of the LSD1/UTX inhibitor MC3324 at
metabolomic, proteomic, lipidomic, and phenotypic level, showing that
epigenetic code, receptor signaling, lipids, and mechano-metabolic
features are tightly connected and pathologically compromised in PCa
and CRPC. Regardless of androgen sensitivity and AR expression, our
data indicate a correlation between epigenetic activity and tumorigenic
processes, suggesting that LSD1 and UTX play a fundamental role in
tumor growth in PCa and CRPC and that both epi-enzymes can be
considered promising targets for therapy. AR is one of the major
drivers of both cancer types and is supported by a plethora of partners
[[200]63], while its downregulation is associated with a positive
patient outcome [[201]61]. Although inhibitors of LSD1 and UTX were
previously reported to limit PCa advancement [[202]64], this is to the
best of our knowledge the first study to show that simultaneous
LSD1/UTX inhibition is able to modulate androgen signaling in PCa and
CRPC. Our findings suggest that this approach may overcome ADT
resistance associated with the aggressive phenotype. A reduction in AR
transcription leads to a block of proliferation with induction of cell
death in forms of PCa such as AR-positive CRPC. The absence of AR
impacts with an avalanche-like effect on the transcription of thousands
of genes that directly or indirectly regulate cell proliferation and
death, affecting the entire cellular organization. However, in our
study LSD1/UTX inhibition also reduced cancer growth in AR-null CRPC.
The dual inhibition of these enzymes induced a block of ATP production
through downregulation of the main actors in glycolysis, such as GLUT1,
the first enzyme responsible for glucose transport inside cells
reported to be overexpressed in different types of human cancers
[[203]65], and in oxidative phosphorylation in PCa and CRPC phenotype,
as shown by proteomic analysis, thereby disrupting pivotal energy
pathways recognized as drivers of CRPC, including those involved in
fatty acid metabolism. Lipids are essential molecules for cellular
structure, energy, and communication in lipid-enriched tumors, such as
PCa [[204]66]. Bioactive lipids are in fact also used as physiological
signaling molecules involved in PCa progression until onset of the CRPC
phenotype [[205]35]. Recent reports revealed that epigenetic
modifications play a pivotal role in energy metabolism [[206]67]
through the regulation of lipid and other metabolic pathways.
Metabolism and epigenetic processes regulate each other in an
interacting system to create a complex network promoting malignant
growth and conferring treatment resistance [[207]68]. Our lipidomic
analysis reveals changes in the architecture of tumor membrane leading
to tumor cell death. Lipid species share one or more fatty acids in
their chemical structures linked to a scaffold characterizing each
class, and PCs are one of the main components of the bilayer cell
membrane [[208]43]. Saturation degree classifies fatty acids as either
saturated (without double bonds) or unsaturated (with one or more
double bonds). The presence/absence of double bonds influences PC
spatial arrangement and geometry, substantially modifying the
biological function of the molecule [[209]69]. In unsaturated PCs, the
double bond forces the fatty acid tail to fold, giving it a spatial
conformation able to increase membrane fluidity due to the space
created by tails [[210]70]. This conformation makes the membrane more
elastic and more easily prone to communication with other cells and
with microenvironment, thus facilitating vesicle trafficking and the
cellular duplication phase [[211]71]. In contrast, a membrane
constituted mostly of saturated fatty acid species loses this fluidity
because PCs are more compact and thus loses all the advantages
described above. Maintaining physiological cell membrane fluidity is
required for its correct function and is associated with cell viability
[[212]72]. Our lipidomic and metabolomic results indicate an increase
in saturated PCs and their downregulation in the whole class,
respectively, suggesting a membrane structure evolution from fluid to
more rigid with the loss of mechanical properties for cancer cells
following LSD1/UTX inhibition. Indeed, our data reveal a stiffening of
PCa cells following LSD1/UTX inhibition. By linking biometabolic
functions with physical–mechanical properties, we show that PCa
mechanics and metabolism can be epigenetically regulated. Membrane
functionality is related to lipid composition, and interfering in
cancer progression through modulation of cell membrane components may
therefore represent a valid strategy to avoid cell communication and
trafficking within the tumor microenvironment. In CRPC, inactivation of
the tumor suppressor phosphatase and tensin homolog (PTEN) by deletion
or mutation [[213]65] is a common genomic aberration and leads to an
alteration of the PI3K–AKT axis directly involved in controlling mTORC1
signaling and the related autophagy process, with adverse oncological
outcomes [[214]73]. AKT regulates the phosphorylation state of eNOS,
whose activity is linked to autophagic block via SQSTM1 accumulation,
compromising clearance of misfolded proteins [[215]74]. In addition,
the acetylated form of tubulin is required for fusion of autophagosomes
with lysosomes, driving autophagolysosome formation and degradation
[[216]75]. Downregulation of mTORC1 pathway observed in our proteomic
analysis of the CRPC cell lines and in the further analysis of a
selected CRPC cell line, C4-2, suggests that LSD1/UTX blocking may also
disrupt this alternative energy path. This finding was supported by
combining proteomic data with lipidomic analysis, which highlighted a
variation in the phosphatidylinositol species regulating upstream the
autophagic axis. CRPC progression is driven by an accelerated
metabolism and the acquisition of a lipid metabolic phenotype where the
radically altered lipogenesis/lipolysis cycle supports proliferation
and increases resistance to other forms of treatment, such as
radiotherapy. The strong dependence on an enriched lipid compartment
was already identified as a possible targetable weakness for this type
of tumor [[217]76]. FASN inhibition results in cancer cell death, a
reduction in tumor size [[218]19], and sensitization of
androgen-dependent and -independent CRPC to radiotherapy
[[219]77,[220]78]. Furthermore, FASN inhibitor compounds have reached
clinical studies in solid tumor patients, including a phase 2 trial
that very recently started in 2021 investigating taxane therapy in
combination with FASN inhibition in CRPC patients ([221]NCT04337580).
The purpose of the trial is to study this class of pharmacological
agents for the treatment of specific tumors via disruption of cancer
metabolism. Identifying tumor types susceptible to FASN inhibitors will
represent a crucial step toward their possible therapeutic use and the
identification of criteria for patient eligibility. In line with this
new strategy for PCa therapy, the multi-omics approach we used here
shows that simultaneous inhibition of LSD1 and UTX could be beneficial
in the treatment of PCa and CRCP phenotypes by directly and indirectly
triggering a biological cascade that epigenetically blocks energy
production, thus interfering with metabolism, lipid content, and
cellular mechanics.
5. Conclusions
Exploiting an epigenetic approach to control cancer mechanics and
metabolism and to interrupt the accelerated lipid evolution observed in
CRPC may represent an alternative strategy to treat this highly
aggressive disease.
Funding
This research was funded by V:ALERE 2019 EPI-MS; V:ALERE CIRCE;
Campania Regional Government Technology Platform “Lotta alle Patologie
Oncologiche”: iCURE; Campania Regional Government FASE2: IDEAL; MIUR,
Proof of Concept POC01_00043; POR Campania FSE 2014–2020 ASSE III. PON
RI 2014/2020 “Dottorati Innovativi con Caratterizzazione Industriale”.
Nuovi Farmaci e Biomarkers di Risposta e Resistenza Farmacologica nel
Cancro del ColonRetto – NABUCCO no. 1682, MISE. TMJE and AM acknowledge
support from Nederlandse Organisatie voor Wetenschappelijk Onderzoek
(NWA-IDG: NWA.1228.192.309).
Authors' contributions
Conceptualization, U.C., C.P.; formal analysis, U.C., C.P., T.M.J.E.,
M.B.; funding acquisition, R.B., L.A. and A.M.; investigation, U.C.,
C.P., T.M.J.E., T.D.M., V.C. and N.I.; methodology, E.P., A.T., M.M.N.,
N.D.G., S.C., T.M.J.E., A.M.; project administration, R.B., L.A., and
A.M.; resources, L.A., A.M.; software, M.B., T.D.M., E.N., D.R. and
A.M.; writing—original draft, U.C., C.P, T.M.J.E.; writing—review and
editing, R.B., L.A, and A.M.
Availability of data and materials
Data are available on request from the authors. The data that support
the findings of this study are available from the corresponding author,
(RB), upon reasonable request. MS/MS data are available at the
ProteomeXchange Consortium via the PRIDE partner repository with the
dataset identifier PXD029249 (interactome) and PXD029525 (whole
proteomics).
Ethics approval and consent to participate
Not applicable
Consent to participate
Not applicable
Consent to publish
Each author has approved the submitted version of the manuscript.
Acknowledgments