Graphical abstract
graphic file with name fx1.jpg
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Highlights
* •
Intracellular IL-32 is an endogenous growth factor for malignant
plasma cells
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IL-32 interacts with components of the electron transport chain
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IL-32 promotes oxidative phosphorylation
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IL-32 is expressed by immature, CD45 + highly proliferating
malignant plasma cells
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Immunology ; Cell biology; Cancer
Introduction
Multiple myeloma (MM) is a cancer of terminally differentiated plasma
cells in the bone marrow. Similar to normal plasma cells, the malignant
cells are dependent on the bone marrow microenvironment for survival.
Most MM cell growth factors are produced by cells of stromal and
hematopoietic origin, and interleukin-6 (IL-6), APRIL, and BAFF are key
survival factors. Only a small number of pro-survival or proliferative
factors may be produced by the cancer cells themselves ([60]Bianchi and
Munshi, 2015).
Reprogramming of cell metabolism has emerged as a central player in
cancer progression, dissemination, and drug resistance. The bone marrow
is characterized by areas of low oxygen levels, and the master
regulator of hypoxic metabolism, HIF1α, is highly expressed in MM cells
in hypoxic niches ([61]Azab et al., 2012; [62]Colla et al., 2010;
[63]Maiso et al., 2015). Hypoxic MM cells may exhibit a glycolytic
phenotype ([64]Ikeda et al., 2018; [65]Maiso et al., 2015) but several
studies have demonstrated that aerobic metabolism, and thus oxidative
phosphorylation (OXPHOS), is fully functional in MM cells. The
OXPHOS/glycolysis ratio is dynamic and possibly regulated by
microenvironmental cues and state of dormancy ([66]Birsoy et al., 2014;
[67]Marlein et al., 2019; [68]Tevebaugh et al., 2017). Furthermore,
high level of aerobic metabolism may contribute to drug resistance and
disease progression in MM ([69]Soriano et al., 2016; [70]Zhan et al.,
2017).
IL-32 is a pluripotent pro-inflammatory cytokine involved in a range of
diseases including cancer, infections, and autoimmunity ([71]Kim
et al., 2005; [72]Ribeiro-Dias et al., 2017; [73]Aass et al., 2021).
IL-32 has no sequence homology with other cytokine families, and an
IL-32 receptor has not been identified. IL-32 is intriguingly regulated
by two different oxygen sensing systems, HIF1α ([74]Zahoor et al.,
2017) and cysteamine (2-aminoethanethiol) dioxygenase (ADO) ([75]Masson
et al., 2019), indicating that this protein has an important function
in response to low oxygen tension. We have previously shown that IL-32
is highly expressed in a subgroup of MM patients and that expression of
IL-32 in MM cells is increased in response to hypoxia in a
HIF1α-dependent manner ([76]Zahoor et al., 2017). The roles and
mechanisms of action of IL-32 in plasma cells is however not known.
A hallmark of multiple myeloma is the great genetic and phenotypic
heterogeneity of the cancer cells. To determine the molecular function
of IL-32 in malignant plasma cells, we therefore generated IL-32 KO
cells from three different cell lines and characterized them by
functional assays and high-throughput transcriptomic and MS-metabolomic
profiling. We further identified novel binding partners to IL-32 by
immunoprecipitation followed by mass spectrometry. Finally, we
determined the gene expression signature of high IL-32-expressing
primary MM cells from patients. We found that endogenous intracellular
IL-32 promoted survival and proliferation of myeloma cells in vitro and
in vivo. IL-32 interacted with components of the mitochondrial
respiratory chain and acted as an important regulator of myeloma cell
metabolism. Moreover, IL-32 expression in patient samples was
associated with poor prognosis and an immature, proliferative plasma
cell profile. Our data demonstrate a metabolic function of IL-32 and
support that IL-32 is a potential prognostic biomarker and a treatment
target in MM.
Results
IL-32 is important for myeloma cell proliferation in vitro and tumor
engraftment in vivo
We have previously demonstrated that IL-32 is expressed by a subgroup
of MM cells ([77]Zahoor et al., 2017). Moreover, bone marrow plasma
cells obtained from healthy donors express IL-32 at higher levels
relative to other B cell subsets ([78]Figure S1A). The function of
IL-32 in plasma cells is however unknown. To investigate the role of
IL-32 in MM cells we depleted IL-32 using CRISPR/Cas9 from three
IL-32-expressing cell lines, JJN-3, INA-6, and H929 ([79]Figure S1B).
These cell lines have different IgH translocations, t(14;16), t(11;14),
and t(4;14), respectively, and also differ in terms of p53 and RAS
mutations ([80]Burger et al., 2001; [81]Gooding et al., 1999).
Strikingly, for all three cell lines, loss of IL-32 significantly
reduced proliferation as assessed by automated cell counting
([82]Figures 1A–1C) and as assessed by % live cells incorporating
5-bromo-2′-deoxyuridine (Brdu) ([83]Figures 1D, 1F and 1H). On the
other hand, depletion of IL-32 significantly reduced viability of the
INA-6 KO cells compared with mock (wild-type [WT]) cells
([84]Figure 1E) but did not affect survival of JJN-3 ([85]Figure 1G)
and H929 cells ([86]Figure 1I). IL-6 is an important survival factor
for myeloma cells ([87]Klein et al., 1995). INA-6 is one of a few
IL-6-dependent MM cell lines ([88]Burger et al., 2001) and is also
quite similar to primary myeloma cells when examined by transcriptomic
correlation analysis ([89]Sarin et al., 2020). The reduction in cell
survival upon IL-32 depletion may indicate that INA-6 cells are
dependent on IL-32 as a pro-survival signal in addition to IL-6.
Figure 1.
[90]Figure 1
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IL-32 is important for myeloma cell proliferation in vitro and tumor
engraftment in vivo
(A–C) INA-6, H929, and JJN-3 IL-32 KO cells were generated by
CRISPR/Cas9. Proliferation of IL-32 KO and WT mock cells was assessed
by automated cell counting every day for 4 days. Mean ± SD of 3
technical replicates of one representative experiment of ≥3 independent
experiments are shown. Significance was evaluated by calculating mean
for each day and performing multiple t tests.
(D) % 5-bromo-2′-deoxyuridine(brdu)-positive live INA-6 KO and WT mock
cells after 4 h. Data shown are mean ± SEM ≥3 independent experiments.
Statistical significance was determined by paired Student's t test.
(E) Viability of INA-6 IL-32 KO and WT mock cells was evaluated by flow
cytometry using annexin/PI staining. Data shown are mean ± SEM ≥3
independent experiments. Statistical significance was determined by
paired Student's t test.
(F) % 5-bromo-2′-deoxyuridine(brdu)-positive live H929 KO and WT mock
cells after 4 h. Data shown are mean ± SEM ≥3 independent experiments.
Statistical significance was determined by paired Student's t test.
(G) Viability of H929 IL-32 KO and WT mock cells was evaluated by flow
cytometry using annexin/PI staining. Data shown are mean ± SEM ≥3
independent experiments. Statistical significance was determined by
paired Student's t test.
(H) % 5-bromo-2′-deoxyuridine(brdu)-positive live JJN-3 KO and WT mock
cells after 4 h. Data shown are mean ± SEM ≥3 independent experiments.
Statistical significance was determined by paired Student's t test.
(I) Viability of JJN-3 IL-32 KO and WT mock cells was evaluated by flow
cytometry using annexin/PI staining. Data shown are mean ± SEM ≥3
independent experiments. Statistical significance was determined by
paired Student's t test.
(J) IL-32 was reintroduced into INA-6 KO cells by transduction with an
IL-32 lentiviral vector and proliferation of INA-6 KO/IL-32 rescue
cells, and INA-6 KO/control rescue cells was assessed by cell counting.
Mean ± SD of 3 technical replicates of one representative experiment of
≥3 independent experiments are shown. Significance was evaluated by
calculating mean for each day and performing multiple t tests.
(K) Viability of INA-6 KO/IL-32 rescue cells and INA-6 KO/control
rescue was evaluated by flow cytometry using annexin/PI staining. Data
shown are mean ± SEM ≥3 independent experiments. Statistical
significance was determined by paired Student's t test.
(L) 1 × 106 iRFP labelled INA-6 IL-32 KO and WT mock cells were
implanted on humanized bone scaffolds on the flanks of RAG −/−
[MATH: y :MATH]
c−/− BALB/c mice, and tumor burden was assessed every week. The figure
shows representative images of tumor burden mice injected with WT mock
and KO cells. WT: N = 9, KO: N = 10. The scale bar shows the intensity
of fluorescence in the 700 white channel.
(M) Tumor development quantified by the pooled iRFP signal of all
scaffolds. Figure shows mean ± SEM of WT: 27 scaffolds, KO: 30
scaffolds.
(N) Blood was collected at the end of the experiment described in (G),
and serum human kappa light chain was quantified.
(O) 1 × 10^5 JJN-3 WT (N = 5) or KO (N = 5) cells were injected into
the tibia of male RAG2−/−GC−/− mice. After 20 days blood was collected,
and serum human kappa light chain was quantified. ∗p ≤0.05, ∗∗p ≤0.01,
∗∗∗p ≤0.001, ∗∗∗∗p ≤0.0001.
The significant reduction in proliferation upon IL-32 depletion in all
three cell lines support that IL-32 has a proliferative effect on
myeloma cells. IL-32 has different isoforms ([92]Aass et al., 2021) and
based on RNA sequencing several isoforms are expressed in the three
cell lines. INA-6 cells express IL-32β and IL-32γ, with the highest
expression of the β-isoform ([93]Figure S1C). To further confirm that
the reduced proliferation was due to loss of IL-32 we re-introduced
IL-32β in an INA-6 KO clone (INA-6 KO/IL-32 rescue) by lentiviral
transduction and subsequent puromycin selection for IL-32 positive
cells. INA-6 KO/IL-32 rescue cells had significantly increased
proliferation compared with the INA-6 KO/control rescue cells
([94]Figure 1J), supporting that the KO phenotype was due to lack of
IL-32. Re-introduction of IL-32 did not significantly improve viability
of the INA-6 KO cells ([95]Figure 1K). Expression of IL-32 in the
knock-in cells was confirmed by qPCR and western blotting ([96]Figures
S1D and E). Treating the cells with rhIL-32β and γ had no effect on
survival or proliferation of INA-6 cells ([97]Figures S1F and G), nor
did it induce proliferation of JJN-3 cells ([98]Figure S1H). rhIL-32
was biological active because it induced TNFα production in macrophages
([99]Figure S1I). Thus, intracellular IL-32, rather than exogenous
IL-32, is responsible for the proliferative effect of IL-32 in plasma
cells.
Myeloma cell growth and survival are aided by factors secreted from
cells in the BM microenvironment. To address if the loss of IL-32
affected the cells' abilities to establish tumors in vivo we performed
two experiments. We first explored if the reduction in proliferation
and survival of IL-32 in INA-6 KO cells in vitro could be compensated
by factors produced by a human bone-marrow-like environment. Thus, we
implanted 1 × 10^6 INA-6 iRFP-labelled IL-32 KO and WT cells into
humanized bone scaffolds in immune compromised female RAG2^−/−
GC^−/−mice and followed tumor growth by imaging ([100]Groen et al.,
2012; [101]Westhrin et al., 2020). Cell injections were successful for
all mice because fluorescence was detected in all scaffolds at day 0,
but only cells expressing IL-32 engrafted ([102]Figures 1L and 1M).
Immunoglobulin kappa light chain is secreted from the tumor cells, and
levels of kappa light chain in serum are commonly used as a tumor
marker. In line with the imaging data, kappa light chain was
undetectable in mice implanted with INA-6 KO cells ([103]Figure 1N). We
next explored if depletion of IL-32 from the more aggressive and robust
cell line JJN-3 affected tumor growth in vivo. In contrast to INA-6
cells, which are dependent on a human bone marrow microenvironment,
JJN-3 cells do engraft in murine bone marrow ([104]Hjorth-Hansen
et al., 1999). Thus, we injected 1 × 10^5 JJN-3 IL-32 WT or KO cells
into the tibiae of male RAG2^−/−GC^−/− mice. After 20 days blood was
collected, and serum human kappa light chain was quantified. Human
kappa light chain was detected in all mice, but it was significantly
reduced in mice injected with IL-32 KO cells ([105]Figure 1O). Hence,
loss of IL-32 in the MM cells cannot be compensated by
microenvironmental-derived factors, and myeloma cells lacking IL-32
have reduced tumorigenic potential in vivo.
IL-32 is localized to the mitochondria and interacts with components of the
mitochondrial respiratory chain
An IL-32 receptor is not identified, and it is not entirely clear how
IL-32 acts at the molecular level ([106]Aass et al., 2021). Thus, to
identify IL-32 binding partners we performed co-immunoprecipitation of
endogenously expressed IL-32 followed by mass spectrometry analyses of
the precipitates. Pull-down was performed on lysates from cells
cultured for 24 h in hypoxic conditions (2% O[2]) to increase IL-32
protein expression ([107]Zahoor et al., 2017). IL-32 KO cells were used
as pull-down control to increase the specificity of the analysis.
Intriguingly, 7 of 33 proteins identified to bind to IL-32 were
mitochondrial proteins ([108]Table 1). Considering the proportion of
mitochondrial proteins in the human proteome, this is more than could
be expected by chance (chi square test with yate's correction p =
0.0005). The interacting proteins included a subunit of the ATP
synthase (ATP5D), a subunit of the NADH:ubiquinone oxidoreductase
(NDUFA12), which is part of the respiratory complex (RC) I subunit, and
a subunit of RC III, ubiquinol-cytochrome c reductase (UQCR11). IL-32
also interacted with dihydroorotate dehydrogenase (DHODH), which
associates with RC III in the inner mitochondrial membrane ([109]Fang
et al., 2013). IL-32 also pulled down the mitochondrial transporters
(ABCB6 and ABCB10), involved in heme synthesis and oxidative stress
response ([110]Bayeva et al., 2013; [111]Krishnamurthy et al., 2006).
Interactions of IL-32 with ATP5D and NDUFA12 were verified by IP
western blotting for the INA-6, H929, and JJN-3 cells ([112]Figure 2A),
supporting results from the IP-MS analysis. Due to lack of suitable
antibodies reverse IP with NDUFA12 and ATP5D was not possible. We were,
however, able to pull down IL-32 using an antibody toward the ATP
synthase complex ([113]Figure S2), further supporting an association
between IL-32 and the ATP synthase. Localization of IL-32 to the
mitochondria was confirmed by the presence of IL-32 in the
mitochondrial fraction of cell lysates ([114]Figure 2B). IL-32 was also
found colocalized with mitochondria at distinct sites by confocal
microscopy (colocalization rate for JJN3: 40.43% ± SD 10.87, INA-6:
39.98 ± SD 20.63, and H929: 39.13 ± SD 5.39) ([115]Figure 2C).
Table 1.
Proteins identified as interaction partners for IL-32
ProteinAcc Entrez ID Organism Full name and gene symbol[116]^a
[117]O00308 11060 Homo sapiens WW domain containing E3 ubiquitin
protein ligase 2 (WWP2)
[118]O14957 10975 Homo sapiens Ubiquinol-cytochrome c reductase,
complex III subunit XI (UQCR11)
[119]O43752 10228 Homo sapiens Syntaxin 6 (STX6)
[120]O75844 10269 Homo sapiens Zinc metallopeptidase STE24 (ZMPSTE24)
[121]O76094 6731 Homo sapiens Signal recognition particle 72 (SRP72)
[122]P10321 3107 Homo sapiens Major histocompatibility complex, class I
C (HLA-C)
[123]P20645 4074 Homo sapiens Mannose-6-phosphate receptor, cation
dependent (M6PR)
[124]P24001 9235 Homo sapiens Interleukin 32 (IL32)
[125]P30049 513 Homo sapiens ATP synthase, H+ transporting,
mitochondrial F1 complex, delta subunit (ATP5D)
[126]P30460 3106 Homo sapiens Major histocompatibility complex, class
I, B (HLA-B)
[127]P33908 4121 Homo sapiens Mannosidase alpha class 1A member 1
(MAN1A1)
[128]P51795 1184 Homo sapiens Chloride voltage-gated channel 5 (CLCN5)
[129]Q02127 1723 Homo sapiens Dihydroorotate dehydrogenase (quinone)
(DHODH)
[130]Q08188 7053 Homo sapiens Transglutaminase 3 (TGM3)
[131]Q09470 3736 Homo sapiens Potassium voltage-gated channel subfamily
A member 1 (KCNA1)
[132]Q15904 537 Homo sapiens ATPase H+ transporting accessory protein 1
(ATP6AP1)
[133]Q5VTU8 432369 Homo sapiens ATP synthase, H+ transporting,
mitochondrial F1 complex, epsilon subunit pseudogene 2 (ATP5EP2)
[134]Q68DH5 92255 Homo sapiens LMBR1 domain containing 2 (LMBRD2)
[135]Q9BW60 64834 Homo sapiens ELOVL fatty acid elongase 1 (ELOVL1)
[136]Q9BXS4 9528 Homo sapiens Transmembrane protein 59 (TMEM59)
[137]Q9NP58 10058 Homo sapiens ATP binding cassette subfamily B member
6 (Langereis blood group) (ABCB6)
[138]Q9NPD3 54512 Homo sapiens Exosome component 4 (EXOSC4)
[139]Q9NRK6 23456 Homo sapiens ATP binding cassette subfamily B member
10 (ABCB10)
[140]Q9UI09 55967 Homo sapiens NADH:ubiquinone oxidoreductase subunit
A12 (NDUFA12)
[141]Q9Y2Q5 28956 Homo sapiens Late endosomal/lysosomal adaptor, MAPK
and MTOR activator 2 (LAMTOR2)
[142]Q9Y5U9 51124 Homo sapiens Immediate early response 3 interacting
protein 1 (IER3IP1)
O43861-1 374868 Homo sapiens Probable phospholipid-transporting ATPase
IIB
P25788-1 5684 Homo sapiens Proteasome subunit alpha type-3
Q08554-1 1823 Homo sapiens Desmocollin-1
Q86VZ5-1 259230 Homo sapiens Phosphatidylcholine:ceramide
cholinephosphotransferase 1
Q93050-1 57130 Homo sapiens V-type proton ATPase 116 kDa subunit a
isoform 1
Q9BXP2-1 56996 Homo sapiens Solute carrier family 12 member 9
Q9ULH0-1 57498 Homo sapiens Kinase D-interacting substrate of 220 kDa
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^a
Proteins detected in MS-analysis of pull-down of endogenous IL-32 from
hypoxic JJN-3 cells. Interaction partners were identified by excluding
all MS target proteins that were not detected in all of 5 IL-32
pull-down replicates and corresponding pull-downs of IL-32 in IL-32 KO
cells. Mitochondrial proteins highlighted in bold.
Figure 2.
[144]Figure 2
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IL-32 is localized to the mitochondria and interacts with components of
the mitochondrial respiratory chain
(A) CO-IP was performed by pull-down of endogenous IL-32 in INA-6,
JJN-3, and H929 cells. Representative immunoblots of ATP5D, NDUFA12,
and IL-32 are shown. The vertical lines in the IL-32 lanes are to
indicate that to improve visualization contrast/brightness were
adjusted differently for the total cell lysate (2 lanes to the left)
and for the IP samples (4 lanes to the right).
(B) Representative immunoblot of IL-32 in the mitochondrial and
cytosolic fraction of JJN-3 cells cultured in normoxia (20% oxygen) and
hypoxia (2% oxygen).
(C) Representative confocal image of hypoxic JJN-3 cells stained for
IL-32 (magenta, Alexa 647), mitochondria (TOMM20, green, Alexa 488),
and nucleus (blue, Hoechst). Imaging was performed with a Leica SP9,
using a 63 × 1.4 (oil) objective and LAS X software and deconvoluted
using Huygens. Scale bar: 5μM. Arrows indicate areas of colocalization
of TOMM20 and IL-32. Correlation rate (CR, in %) is the mean ± SD
calculated from N = 4 images analyzed in Leica Application Suite X.
IL-32 enhances mitochondrial respiration
To investigate if IL-32 regulates mitochondrial respiration we measured
oxidative phosphorylation (OXPHOS) by quantifying the oxygen
consumption rate (OCR). OCR was significantly reduced in all three
IL-32 KO cell lines ([146]Figure 3A). The IL-32-expressing cells
respired significantly more than KO cells both in basal culture
conditions ([147]Figure 3B) and when maximum respiration was triggered
by FCCP ([148]Figure 3C), supporting that IL-32 promotes OXPHOS in MM
cells. Glycolysis, as measured by extracellular acidification rate
(ECAR), was also significantly reduced in KO cells compared with WT
cells ([149]Figure 3D). Thus, aerobic glycolysis did not seem to be
increased to compensate for the lack of aerobic respiration. In line
with the reduction in OCR, intracellular ATP was reduced in IL-32-KO
cells compared with WT cells ([150]Figure 3E). The mitochondria in
JJN-3 and INA-6 KO cells appeared rounded and small, compared with the
more elongated, fused mitochondria of WT cells ([151]Figure 3F).
Indeed, individual mitochondria in JJN-3 and INA-6 WT cells were
significantly longer than mitochondria in the KO cells
([152]Figure 3G). However, neither the amount mitochondria
([153]Figure S3A) nor the mitochondria membrane potential was changed
([154]Figure S3B). Thus, the reduction in OCR and ATP production was
due to less efficient OXPHOS in the mitochondria rather than a general
depolarization of mitochondria or reduced amount mitochondria in the KO
cells. Transfection of IL-32β into INA-6 KO cells led to expression of
IL-32β in both cytosol and mitochondria ([155]Figure S3C). The INA-6
KO/IL-32 rescued cells had improved metabolic capacity as both OCR and
ECAR were increased ([156]Figures 3H and 3I), supporting that the
metabolic phenotype was due to lack of IL-32.
Figure 3.
[157]Figure 3
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IL-32 enhances mitochondrial respiration
(A) Representative Seahorse Mito Stress Test assay measuring OCR in
INA-6, H929, and JJN-3 KO and WT mock. Four first measurements show
basal OXPHOS, after injection of oligomycin: non-ATP oxygen consumption
(proton leak), after FCCP injection: maximal OCR, after injection of
rotenone and antimycin: nonmitochondrial respiration. Data show mean ±
SD of 20 technical replicates. The differences between KO and WT mock
cells were significant using two-way ANOVA and Sidàk's multiple
comparison test (p ≤0.0001).
(B) Mean basal respiration (basal OCR) in INA-6, H929, and JJN-3 KO and
WT mock cell lines. Data shown are mean ± SEM of 3 independent
experiments.
(C) Mean maximal respiration (max OCR) in INA-6, H929, and JJN-3 KO and
WT mock cell lines. Data shown are mean ± SEM of 3 independent
experiments.
(D) Mean basal glycolysis (±SEM) in IL-32 KO and WT cell lines analyzed
by Seahorse Glycolysis Stress Test measuring ECAR. Data shown are
mean ± SEM of 3 independent experiments.
(E) Relative ATP levels in INA-6, H929, and JJN-3 KO and WT mock cells
quantified by CTG-assay. Data shown are mean ± SEM of 3 independent
experiments.
(F) Representative confocal images of mitochondria of IL-32 JJN-3 KO
and WT mock cells stained for TOMM20 (green, Alexa 488) and nuclei
(Hoechst, blue). Imaging was performed with a Leica SP9, using a 63 ×
1.4 (oil) objective and LAS X software and deconvoluted using Huygens.
Scale bar: 5μM. Arrows indicate areas of colocalization of TOMM20 and
IL-32.
(G) Length of mitochondria in INA-6, H929, and JJN-3 IL-32 KO and WT
mock cells analyzed in Fiji Software. Data are presented as mean length
(
[MATH: u :MATH]
m) ±SEM of mitochondria imaged with the same staining as in (F) in 3
independent experiments (see [159]STAR Methods for details).
(H) Representative graph showing OXPHOS in INA-6 KO/IL-32 rescue cells
and IL-6 KO/rescue control (mean ± SD of more than 20 technical
replicates). The difference between INA-6 control rescue and INA-6
IL-32 rescue was significant using two-way ANOVA (P ≤0.0001). Bar plot
shows mean basal OCR (±SEM) of 3 independent experiment. INA-6 WT mock
cells were included for comparison.
(I) Representative graph showing glycolysis in INA-6 KO/IL-32 rescue
cells and INA-6 KO/control rescue cells (mean ± SD) of more than 20
technical replicates. The difference between INA-6 KO/control rescue
cells and INA-6 KO/IL-32 rescue cells was significant using two-way
ANOVA and Sidàk's multiple comparison test (P ≤0.0001). The bar plot
shows mean basal glycolysis (ECAR) (±SEM) of 3 independent experiment.
INA-6 WT mock cells were included for comparison.
(J) Membrane potential in isolated mitochondria from IL-32 KO and WT
mock cells quantified by Mitotracker Orange CMTMRos fluorescence. The
bar plots show mean ± SEM of 3 independent experiments.
(K) Mean basal respiration (basal OCR) in isolated mitochondria from
INA-6, H929, and JJN-3 KO and WT mock cell lines. Data are shown as
mean ± SEM of 3 independent experiments.
(L) Mitochondrial ROS in INA-6, H929, and JJN-3 KO and WT mock cell
lines quantified by Mitosox Red staining. Figure shows Mitosox
fluorescence of KO and WT cells normalized to WT for each independent
experiment (N >3). Data are shown as mean ± SEM.
(M) INA-6, H929, and JJN-3 IL-32 KO and WT mock cells were grown in
medium supplemented with IACS-10759 (10 nM), and number of cells was
determined by automated counting after 4 days of culture. Data shown
are mean total number of cells ±SEM of 3 independent experiments.
Difference in proliferation between untreated control and
inhibitor-treated samples was assessed for KO and WT mock cells by RM
one-way ANOVA followed by Sidak's multiple comparison test. ns, not
significant; ∗p ≤0.05, ∗∗p ≤0.01, ∗∗∗p ≤0.001, ∗∗∗∗p ≤0.0001.
To further explore the effect on IL-32-depletion on mitochondrial
function we isolated mitochondria from WT and KO cells. In line with
the results performed on whole cells ([160]Figure S3B) the membrane
potential did not differ in mitochondria isolated from WT and KO cells
in the three cell lines ([161]Figure 3J). Importantly, however,
isolated mitochondria from KO cells showed reduced OCR
([162]Figure 2K). These findings support that the reduced OCR was
related to a reduced efficiency of mitochondrial respiration and not a
result of reduced availability of TCA substrate (pyruvate) from
glycolysis or other anaplerotic substrates. Despite reduced OCR
([163]Figure 3B), a significant increase in mitochondrial ROS (mtROS)
was measured in whole cells ([164]Figure 3L). There were also more ROS
in isolated mitochondria ([165]Figure S3D). Increased ROS may be due to
electron leak in the mitochondrial electron transport chain when
electrons exit prior to the reduction of oxygen to water at cytochrome
c oxidase ([166]Guo et al., 2016). Increased mtROS, despite of reduced
OCR/ATP synthesis, may thus be due to dysfunctional/suboptimal ETC.
To assess the importance of OXPHOS for myeloma cell proliferation, we
treated KO and WT cells with the OXPHOS inhibitor IACS-10759
([167]Molina et al., 2018). Inhibiting OXPHOS significantly reduced
proliferation of all cell lines, supporting that efficient OXPHOS is
needed for maximum myeloma cell proliferation. It also appeared that
the KO cells were slightly less affected by the OXPHOS inhibitor,
supporting that IL-32-expressing cells have higher OXPHOS activity
([168]Figure 3M).
Loss of IL-32 leads to perturbations in metabolic pathways
To further characterize how IL-32 influences mitochondrial function and
cancer cell metabolism, we characterized the metabolome and
transcriptome of INA-6 WT and KO cells from two different clones using
mass spectrometry and next-generation RNA-sequencing, respectively. The
INA-6 cell line was chosen because it is IL-6 dependent and relatively
similar to primary cells ([169]Sarin et al., 2020). We observed major
differences in metabolites between KO and WT cells ([170]Figure 4A).
There was a striking accumulation of polyunsaturated triglycerides
(TAGs) in the KO cells; 36 of the 89 significant upregulated
metabolites were TAGs ([171]Figure 4B and [172]Table S1). D-fructose
and citrate were also on top of the list of metabolites increased in KO
cells ([173]Figure 4B). On the other hand, 85 of 220 downregulated
metabolites in KO cells were membrane lipids ([174]Table S1), including
phosphatidylethanolamines (PE), phosphatidylcholines (PC),
diacylglycerols (DAG), and saturated TAGs ([175]Figure S4A). This
indicates that fatty acid synthesis is skewed in KO cells and that
fatty acids are used for synthesis of unsaturated triglycerides rather
than membrane lipids. Indeed, when staining for neutral lipids in JJN-3
and INA-6 KO cells, we observed a striking accumulation of lipid
droplets not present in the WT cells ([176]Figures 4C and [177]S4B).
According to the Metabolite Set Enrichment Analysis (MSEA), the
metabolic pathways aspartate metabolism, urea cycle, purine metabolism,
the citric acid cycle, PC biosynthesis, the mitochondrial electron
transport chain, and Warburg effect were downregulated in KO cells
([178]Figure S4C).
Figure 4.
[179]Figure 4
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Loss of IL-32 leads to perturbations in metabolic pathways
(A) PCA plot of metabolomes from two clones of INA-6 KO cells and WT
mock cells.
(B) Volcano plot showing significant different metabolites (p <0.05)
between KO cells and WT mock cells (metabolite expression from
replicates from two KO clones were merged) See also [181]Table S1.
Significance was determined by two-sided Student's t test using
MetaboAnalyst 4.0 software.
(C) Representative image of lipid droplets in INA-6 IL-32 KO and WT
mock cells, stained with Nile Red and Hoechst. Polar lipids (red) were
excited at 590 nm (600–700 nm) and neutral lipids (green) at 488 nm
(500–580 nm). Confocal imaging was performed with a Leica TCS SP8 STED
3X, using a 63 × 1.4 (oil) objective and LAS X software. Scale bar:
10 μM. See [182]Figure S4 for overview images.
(D) Two INA-6 KO cell lines and WT mock cells were subjected to RNA
sequencing, and the PCA plot shows the overall differences in gene
expression between KO cells and WT mock cells.
(E) Volcano plot showing the most significantly upregulated and
downregulated genes in INA-6 KO cells (2 clones) versus WT mock cells.
Statistical significance analyzed by Linear Models for Microarray
Analysis (limma) in R with Benjamini-Hochberg-adjusted p values. See
also [183]Table S2 for complete gene list.
(F) Joint pathway analysis (SMPDB pathways, MetaboAnalyst 4.0) of
transcriptomic and metabolomic data from 2 INA-6 IL-32 KO clones and WT
mock cells. The inverse logged p-value of the different pathways is
shown on the y-axis, and the size and color on the dots (increased size
and increasingly red) correspond to the increased inverse log p-value.
Significance was determined by two-sided Student's t test using
MetaboAnalyst 4.0 software. The joint pathway analysis is based on
metabolites in [184]Table S1 and genes (fold change >0.5 or < −0.5 and
adjusted p value <0.05) in [185]Table S2.
(G) Significantly (p <0.05) altered citric acid cycle intermediates in
two KO clones (KO1, KO2) versus WT mock cells (See also [186]Table S1).
Data are presented as mean peak intensity ± SD of 4 replicates.
(H) Illustration of significantly differentially expressed genes and
metabolites from the most enriched pathways in the joint pathway
enrichment analysis in (F) Significance was determined by two-sided
Student's t test using MetaboAnalyst 4.0 software.
RNA sequencing of the INA-6 cell line also demonstrated marked
differences in gene expression between IL-32 WT and KO cells
([187]Figures 4D and 4E, [188]Table S2). Downregulated genes resulting
from loss of IL-32 included genes involved in biological processes such
as “cotranslational protein targeting to the membrane,”
“nuclear-transcribed mRNA catabolic process, nonsense-mediated decay,”
and “translational initiation.” In addition, terms related to immune
cell proliferation, immune activation, and leukocyte differentiation
were higher expressed in the WT cells ([189]Figure S5A). In contrast,
biological pathways that were upregulated in INA-6 KO cells included
“translational termination,” mitochondrion organization,” and
“regulation of G2/M transition of mitotic cell cycle.” Overall, genes
related to processes in the mitochondria, cell division, and protein
synthesis/turnover were prominently upregulated in KO cells
([190]Figure S5B). Considering the phenotype of the KO cells, it is
likely that some of the genes involved in these biological processes
are upregulated in the KO cells as a compensatory mechanism.
By combining the metabolomics and transcriptomics data for INA-6 cells
in a joint pathway analysis, we found arginine biosynthesis, citric
acid cycle and alanine, aspartate, and glutamate metabolism to differ
the most between WT and KO cells ([191]Figure 4F). For the individual
metabolites, citrate was the only upregulated intermediate in the
citric acid cycle in the KO cells, whereas α-ketoglutarate, succinate,
fumarate, and malate were all downregulated ([192]Figure 4G and
[193]Table S1), indicating that the citric acid cycle is disrupted at
this point in IL-32 KO cells. Limited oxidation in the electron
transport chain may lead to enhanced transport of citrate out from the
mitochondria and used for synthesis of fatty acids ([194]Martínez-Reyes
and Chandel, 2020) ([195]Figure 4H). Supporting our experimental data,
ATP was reduced in the KO cells ([196]Table S1), and NAD was the most
significantly downregulated metabolite in the KO cells
([197]Figure 4B), indicative of less active mitochondrial metabolism in
the KO cells. Taken together, changes in citric acid cycle
intermediates, arginine biosynthesis, and fatty acid accumulation
indicate dysfunctional mitochondrial OXPHOS in IL-32 KO cells.
IL-32 expression in primary MM cells is associated with inferior survival,
cell division, and oxidative phosphorylation
We have previously shown that a subgroup of 10% to 15% of MM patients
express IL-32 and that high IL-32 expression in patients associates
with reduced progression-free survival ([198]Zahoor et al., 2017)
([199]Figures S6A and S6B). To further validate IL-32 as a prognostic
factor, we analyzed overall survival of IL-32-expressing patients
(upper 10th percentile, N = 80) and IL-32 nonexpressors (lower 90th
percentile, N = 712) in the MMRF-CoMMpass IA13 dataset. Indeed, IL-32
expressors live significantly shorter (1005 days median survival)
compared with nonexpressors (median survival not reached, P = 8.9e-5)
([200]Figure 5A). IL-32 expression also retained prognostic information
when adjusting for ISS stage ([201]Figure S6C). Moreover, when
analyzing paired diagnosis and progression samples from the same
dataset, IL-32 was significantly increased upon relapse in individual
patients ([202]Figure 5B).
Figure 5.
[203]Figure 5
[204]Open in a new tab
IL-32 expression in primary myeloma cells is associated with inferior
survival, cell division, and oxidative phosphorylation
(A) Overall survival of IL-32 expressing patients (10th percentile)
compared with nonexpressing patients (90th percentile) in the IA13
CoMMpass dataset P = 8.9e-5, using Cox proportional-hazards regression
model.
(B) IL-32 expression in individual patients at diagnosis and first
relapse in RNA-sequenced CD138^+ cells from CoMMpass IA13. Significance
was determined by Wilcoxon signed-rank test.
(C) GO-analysis of the upregulated genes (Benjamini-Hochberg-adjusted p
value <0.05; log2 fold change >0 for up-regulated genes) in
IL-32-expressing patients (10th percentile) compared with IL-32
nonexpressing patients (90th percentile). Top significantly enriched
biological processes upregulated in IL-32 expressing patients are
shown. The GO terms are ordered by the Benjamini-hochberg adjusted p
values. See also [205]Tables S3 and [206]S4.
(D) Correlation between IL32 and a proliferative index gene signature
(calculated as the sum of expression values of the gene set as
described in [207]Hose et al. (2009).
(E) GO-analysis of the downregulated genes (Benjamini-Hochberg-adjusted
p value <0.05; log2 fold change <0 for down-regulated genes,
respectively) in IL-32-expressing patients (10th percentile) compared
with IL-32 nonexpressing patients (90th percentile). Top significantly
enriched biological processes downregulated in IL-32 expressing
patients are shown. The GO terms are ordered by the Benjamini-Hochberg
adjusted p values. See also [208]Tables S3 and [209]S4.
We next examined the characteristics of IL-32-expressing primary MM
cells in terms of gene expression. In the MMRF-CoMMPass IA13 dataset,
there were 4,548 significantly differentially expressed genes between
IL-32-expressing (upper 10th percentile) and nonexpressing patients
(lower 90th percentile) ([210]Table S3). Interestingly, the GO
enrichment analysis of differentially expressed genes revealed changes
in similar GO biological processes as associated with expression of
IL-32 in the cell lines: the most upregulated genes in IL-32-expressing
patients were associated with cell division ([211]Figure 5C and
[212]Table S4), indicating that this is indeed a signature of IL-32,
both in cell lines and in primary cells. Moreover, IL-32 expression
correlated with expression of genes associated with a high
proliferative index in myeloma ([213]Hose et al., 2009)
([214]Figure 5D). ATP metabolic processes and oxidative phosphorylation
were also significantly enriched in IL-32-expressing cells, supporting
that IL-32-expresssing cells have active OXPHOS as compared with
nonexpressing cells. In line with previous published data ([215]Zahoor
et al., 2017), we found IL-32 expression to be highly correlated with
HIF1α expression ([216]Figures S6D and S6E). Genes downregulated in
IL-32-expressing patients were associated with protein handling and
endoplasmic reticulum stress, biological processes related to the high
immunoglobulin secretion from terminally differentiated plasma cells
([217]Figure 5E).
To investigate the distribution of IL-32 gene expression within the
malignant plasma cell population and to see if the highly
proliferating, respiratory phenotype is directly linked to IL-32
expression within the same cell, we analyzed a publicly available
single-cell dataset of MM cells sampled from bone marrow and
extramedullary tumors ([218]Ryu et al., 2020). We identified nine
distinct clusters across the 12 patient samples with a total of 488
single cells of which IL-32 was mainly expressed in three of the
clusters and in four of the samples ([219]Figure 6A–6C). IL-32 was
expressed in about 70% of the cells from sample MM33 and at
intermediate levels in most cells from MM17 as well as in a few cells
from MM36 ([220]Figures 6B and 6C). In patients MM02 IL-32 was not
expressed in the bone marrow sample taken at diagnosis (MM02) but
highly expressed in all the cells of the extramedullary tumor sample
(MM02EM) obtained 18 months later. Importantly, genes involved in “ATP
synthesis coupled electron transport,” “assembly of ETC complexes,” and
“cell-cycle progression” were significantly upregulated in single cells
expressing IL-32 compared with nonexpressing cells ([221]Figure 6D).
These data support that the same MM cell that expresses IL-32 has high
OXPHOS and proliferation.
Figure 6.
[222]Figure 6
[223]Open in a new tab
Single cell transcriptome analysis of IL-32-expressing myeloma cells
(A) Uniform manifold approximation and projection (UMAP) plot colored
by the identified cell clusters from a single-cell dataset
([224]GSE106218) with primary myeloma cells. Analyzed with Seurat
package in R.
(B) UMAP plot colored by the level of IL32-expression per cell.
(C) UMAP plot colored by patient sample.
(D) Top 20 gene ontology terms (biological processes) for genes
enriched in IL-32 expressing patient cells. The GO terms are ordered by
the Benjamini-Hochberg adjusted p values. The data were obtained from
Ryu et al. ([225]Ryu et al., 2020).
IL-32 expression promotes a more immature plasma cell phenotype
To gain further knowledge of the transcriptional programs associated
with IL-32 in malignant plasma cells, we investigated which genes were
more highly expressed in WT compared with INA-6 KO cells and at the
same time upregulated in IL-32-expressing primary cells in the
CoMMpasss IA13 dataset ([226]Table S5, [227]Figure 7A). We identified
230 genes to be significantly differently expressed in both
comparisons, and these genes are likely to be functionally related to
IL-32 expression. The top 3 genes, when sorting for the most
downregulated genes in KO and upregulated genes in IL-32-expressing
patients on the shared signature list, were MME, encoding CD10, and the
transcription factors IRF8 and SORL1, encoding the sortilin-related
receptor 1 ([228]Figures 7B and 7C). SORL1 plays a role in lipid
metabolism and IL-6 signaling ([229]Larsen and Petersen, 2017;
[230]Mortensen et al., 2014; [231]Patel Kevin et al., 2015), and MME
and IRF8 are both important in early stages of B-cell development
([232]Kikuchi et al., 2018; [233]Wang et al., 2008). MME and SORL1 were
also downregulated in H929 KO cells compared with WT cells
([234]Figure S7A). IRF8 was not expressed by this cell line.
Figure 7.
[235]Figure 7
[236]Open in a new tab
IL-32 expression promotes a more immature plasma cell phenotype
(A) Venn-diagram of overlapping significant genes (p <0.01) that were
more highly expressed in WT cells compared with KO cells (comparing two
INA-6 KO clones [KO1, KO2] with WT mock cells) and upregulated in IL-32
patients (comparing IL-32- expressing patients versus nonexpressing
patients). See also [237]Table S5.
(B) Gene expression of MME, IRF8, and SORL1 in patients expressing
IL-32 (10th percentile) compared with nonexpressing (90th percentile)
patients. Significance determined by limma in R.
(C) Gene expression of MME, IRF8, and SORL1 in INA-6 IL-32 KO1, KO2,
and WT mock cells. Significance determined by limma in R with
Benjamini-Hochberg-adjusted p-values. Data presented are mean cpm ± SD
of two replicates.
(D) Evaluation of gene expression of markers associated with less
differentiated stages of B cell maturation in CoMMpass IA13, comparing
IL-32 expressing patients (upper 10th percentile) with nonexpressing
patients (lower 90th percentile). Significance determined by limma in
R. Boxplots show the median and 25th/75th quantiles and
smallest/largest value within the 1.5 times interquartile rang.
(E) Scatterplot of genes associated with less differentiated stages of
B cell maturation in single cells with (N = 142) and without (N = 346)
IL32-expression (from single cell transcriptomics). p values were
calculated using the FindMarkers function in Seurat by comparing the
high and low IL32 groups.
(F) Surface expression of CD45 and CD38 in INA-6 KO and WT cells. Data
are presented as median fluorescence intensity (MFI) from 3 independent
experiments and significance determined by unpaired student's t test.
Bare plots show mean ± SEM.
(G) Concentration of kappa light chain/cell detected in conditioned
media from WT and KO cells as indicated. p values were calculated by
the ratio paired t test. ns, not significant; ∗p ≤0.05, ∗∗p ≤0.01, ∗∗∗p
≤0.001, ∗∗∗∗p ≤0.0001.
Other genes associated with earlier stages of B cell differentiation
([238]Guo et al., 2018; [239]Matthias and Rolink, 2005; [240]Sanz
et al., 2019; [241]Scheeren et al., 2005; [242]Vogel et al., 2014;
[243]Wilmore et al., 2017), such as BCL6, CIITA, EZH2, STAT5B, PTPRC
(CD45), MKI67, and several genes encoding MHC II, including HLA- DPB1,
were upregulated in IL-32-expressing patients ([244]Figure 7D).
Importantly, we also found expression of immature genes to be
significantly upregulated in IL-32-expressing cells in the single-cell
sequencing dataset ([245]Figure 7E). In accordance, genes associated
with mature plasma cells were slightly, but significantly,
downregulated in IL-32-expressing patients, including CD38, CD27,
CXCR4, ERN, PRDM1, IRF4, and FOXO1 ([246]Figure S7B). CD45 is as marker
of proliferating, immature myeloma cells, whereas CD38 is a marker of
terminally differentiated plasma cells and expressed by most MM cells
([247]Paiva et al., 2017). INA-6 is a MM cell line with an immature
phenotype, with high expression of CD45 and relatively low expression
of CD38. Strikingly, loss of IL-32 led to a reduction in CD45 and
re-expression of CD38 in INA-6 cells as examined by flow cytometry
([248]Figure 7F). Other genes associated with immature plasma cells
were also downregulated in the KO cells ([249]Figure S7C). ER stress is
an Achilles heel of MM cells partly due to the production of large
amounts of monoclonal antibodies. IL-32-expressing primary cells seemed
less affected by ER stress ([250]Figure 5E), so we asked whether this
could be related to less production of immunoglobulins. Strikingly,
INA-6 and H929 KO cells secreted more kappa light chain than WT mock
cells ([251]Figure 7G), supporting our hypothesis that IL-32 expression
promotes an immature plasma cell phenotype and that IL-32-expression
may relieve the cells from immunoglobulin-related ER stress. Taken
together these results suggest that IL-32 is involved in regulation of
transcriptional programs that induce a more immature and less
ER-stressed plasma cell.
Discussion
We have identified IL-32 as a novel, endogenously expressed growth and
survival factor for malignant plasma cells. IL-32 interacts with
components of the respiratory chain, and expression of IL-32 is
important for efficient OXPHOS in MM cells. A subgroup of MM patients
expresses IL-32, and these patients have reduced OS. Furthermore, the
malignant plasma cells of these patients have distinct phenotypical
characteristics, resembling an immature or less differentiated plasma
cell.
Based on gene expression data in CoMMpass, 10% to 15% of MM patients
express IL-32 at diagnosis. Moreover, analyses of paired samples from
diagnosis and relapse in individual patients suggest that IL-32
expression increases upon relapse in about 20% of the patients. The
strong, independent association of IL-32 with inferior survival in
patients, the reduced proliferative rate, and the reduction of OXPHOS
in three phenotypically different cell lines when depleting IL-32
strongly suggest that IL-32 plays a role in MM disease progression. The
IL-6-dependent cell line INA-6 was most dependent on IL-32 expression,
because loss of IL-32 reduced not only proliferation but also cell
survival. In fact, these cells were not able to form tumors in vivo,
even in a supportive, humanized bone marrow microenvironment. This can
possibly be explained by IL-32-depleted cells being less able to adapt
to the more challenging metabolic conditions in vivo, where there may
be limited access to oxygen and changed composition of nutrients
([252]Muir et al., 2018; [253]Sullivan et al., 2019). It is also worth
noting that IL-32 is expressed by regulatory or senescent/exhausted
T cells in MM, although if/how this affects the function of the T-cells
and if it contributes to disease progression is not known ([254]Bailur
et al., 2019; [255]Zavidij et al., 2020).
In contrast to most MM cells, which in general are slow growing and
have low Ki-67, ([256]Gastinne et al., 2007; [257]Wilson et al., 2001),
IL-32-expressing primary MM cells have a gene signature related to cell
division and an immature plasma cell phenotype. This was evident both
from the large CoMMPass dataset and from the small number of
single-cell RNA-sequenced patient samples. And strikingly,
IL-32-depleted MM cells had downregulated expression of the same
“immaturity” genes as compared with WT cells. We cannot conclude
whether the changes in proliferation and gene expression are related to
the metabolic effects of IL-32 or an independent effect of IL-32 on
transcription. Metabolites may, however, play a central role in
regulating gene expression. For example, availability of acetyl-CoA can
modify extent of histone acetylation, whereas metabolites such as
succinate and a-ketoglutarate may regulate DNA and histone methylation
([258]van der Knaap and Verrijzer, 2016). Although additional
experiments are needed to conclude, it could be that IL-32 expression
and subsequent changes in metabolism may lead to plasma cell
de-differentiation or maturation arrest. The profound changes in ATP
and core metabolites such as citrate, a-ketoglutarate, succinate,
fumarate, and malate upon IL-32 depletion as shown here makes this a
possible scenario. Supporting this notion, GO terms related to histone
modifications were also differently expressed between IL-32-expressing
and nonexpressing primary cells. Of note, MM cells with an immature
phenotype have previously been linked with more aggressive tumors
([259]Leung-Hagesteijn et al., 2013; [260]Paiva et al., 2017) and
compounds such as ATRA and 2-methoxyestrodiol (2-ME2) that promote
differentiation of MM cells render the cells more sensitive to
bortezomib ([261]Gu et al., 2012). Thus, IL-32 could potentially be a
marker for patients that may benefit from such combined treatment.
Oxygen is a key regulator of aerobic respiration and metabolism, and it
is striking that IL-32 expression is regulated by two different oxygen
sensing systems, HIF1α ([262]Zahoor et al., 2017) and cysteamine
(2-aminoethanethiol) dioxygenase (ADO) ([263]Masson et al., 2019). ADO
is an enzymatic O[2] sensor and was shown to catalyze dioxygenation of
IL-32 in the presence of O[2], leading to proteasomal degradation
([264]Masson et al., 2019). Correspondingly, hypoxia leads to
stabilization of HIF1α, which induces IL-32 mRNA and protein expression
([265]Zahoor et al., 2017). These data support that IL-32 has an
important role in cellular responses to alterations in oxygen levels.
Hypoxia is known to cause changes in the composition of ETC complexes
and the changes help keep the mitochondria intact under low oxygen
conditions and to prevent excessive ROS formation ([266]Fuhrmann and
Brüne, 2017). Indeed, we found that 7 of 33 proteins that bound to
IL-32 in hypoxia were located in the mitochondrion and that 5 of these
were subunits of different components of the mitochondrial respiratory
chain. Respirasome supercomplexes, where the respiratory chain
components are assembled in close vicinity to each other, lead to
higher catalytic activity of the individual components, to increased
efficiency of electron transfer, and to less production of ROS
([267]Guo et al., 2016; [268]Lenaz and Genova, 2012). The IL-32 KO
cells had reduced capacity for mitochondrial respiration and ATP
formation; still, cells lacking IL-32 had significantly higher levels
of mitochondrial ROS, in line with suboptimal respiration. Thus, we
propose that IL-32 by binding components of the respiratory chain
enhances the efficiency of the ETC, enabling the cells to maintain
OXPHOS even under conditions of low O[2] and also to keep mtROS at a
level compatible with cell survival. Exactly how IL-32 is transported
to and acts in the mitochondria to enhance OXPHOS needs to be further
explored.
IL-32-depleted cells had dramatic alterations in the composition of
lipids with a profound accumulation of unsaturated TAG. This could be
due to reduced oxidation in the electron transport chain, leading to
enhanced transport of citrate out from the mitochondria and used for
synthesis of fats ([269]Moen et al., 2016). It is also possible that
the accumulation of neutral fats is a result of cellular stress
([270]Petan et al., 2018). A third possibility is that IL-32 plays a
more direct role in lipid metabolism. For example, we found that SORL1
was highly expressed in IL-32-expressing patients and downregulated in
the IL-32 KO cells. SORL1 encodes the sortilin-related receptor 1, a
multifunctional intracellular sorting protein belonging to the sortilin
and LDL-receptor families implicated in the regulation of intracellular
lipid pools ([271]Klinger et al., 2011). How IL-32 acts at the
molecular level to regulate lipid synthesis, lipid metabolism, or lipid
transport is however unclear.
In conclusion, we have shown that intracellular IL-32 promotes OXPHOS
and provides a survival benefit for malignant plasma cells. The
interaction of IL-32 with components of the respiratory chain and its
regulation by two different oxygen sensing system indicate that IL-32
has an important role in cellular responses to O[2] fluctuations.
Besides identifying IL-32 as a potential prognostic biomarker and
treatment target in MM, our results provide insight into the metabolic
functions of IL-32, which may be further exploited in other cancers and
inflammatory diseases where IL-32 is known to play a central role.
Limitations of the study
Our in vitro findings from cell lines on the importance of IL-32 for
proliferation, OXPHOS, and plasma cell maturation are supported by gene
expression analyses of primary cells from patients. Here we observe
that IL-32-expressing myeloma cells have a gene signature indicative of
highly proliferating cells with active OXPHOS and an immature
phenotype. Ideally, we should have verified the link between OXPHOS,
proliferation, and IL-32 expression in patient samples by genetic
manipulation of primary plasma cells. However, that is challenging
because primary myeloma cells have very poor viability ex vivo. We also
demonstrate that IL-32 binds to components of the mitochondria electron
transport chain, and we hypothesize that IL-32 may increase the
efficiency of the electron transport chain by direct protein
interactions. However, how this happens at the molecular level needs to
be investigated further. The link between IL-32 expression and plasma
cell maturation could be strengthened by performing flow cytometric
analyses of bone marrow aspirates from patients. Such experiments would
however require access to freshly obtained bone marrow aspirates from a
large number of patients.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
__________________________________________________________________
CD38 PE-Cy^TM7 BD Biosciences #335825 (clone HB7);
RRID:[272]AB_2868688)
FITC Mouse anti-human CD45 BD Biosciences #555482(clone HI30);
RRID:[273]AB_395874
β-actin antibody Cell Signaling Technology #4967; RRID:[274]AB_330288
Human IL-32 antibody R&D Systems #AF3040; RRID:[275]AB_2124022
β-Tubulin (9F3) Rabbit mAb Cell Signaling Technology #2128;
RRID:[276]AB_823664
COX IV (3E11) Rabbit mAb Cell Signaling Technology #4850;
RRID:[277]AB_2085424
Anti-TOMM20 antibody produced in rabbit Sigma Aldrich #HPA011562;
RRID:[278]AB_1080326
Anti-NDUFA12 antibody produced in rabbit Sigma Aldrich #HPA039903;
RRID:[279]AB_10795516
Anti-GAPDH antibody(6C5) Abcam #ab8245; RRID:[280]AB_2107448
Anti-ATP5D antibody Abcam #ab97491; RRID:[281]AB_10681010
anti-ATP synthase immunocapture antibody (12F4AD8AF8) Abcam #ab109867;
RRID:[282]AB_10866627
Alexa Fluor® 488 Anti-BrdU antibody [BU1/75 (ICR1)] Abcam #ab220074
Chicken anti-goat IgG (H+L) Cross-adsorbed Secondary Antibody Alexa
Fluor 647 Thermo Fisher # A21469; RRID:[283]AB_1500603
Donkey anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary
Antibody Alexa Fluor 488 Thermo Fisher #A21206; RRID:[284]AB_2535792
__________________________________________________________________
Chemicals, peptides, and recombinant proteins
__________________________________________________________________
Recombinant human IL-32 gamma protein/CF R&D Systems #6769-IL-025
Recombinant human IL-32 gamma protein/CR R&D Systems #4690-IL-025/CF
Oligomycin from Streptomyces diastatochromogenes Merck #O4876
2-deoxy-d-glucose Sigma Aldrich # D8375
Rotenone Sigma Aldrich #R8875
Antimycin A1 Sigma Aldrich #A0149
FCCP (Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone) Sigma
Aldrich #C2920
Nile Red Thermo Fisher #N1142
BrdU (5-bromo-2'-deoxyuridine), Thymidine analog Abcam #ab142567
eBioscience™ Fixable Viability Dye eFluor™ 450 Thermo Fisher
#65-0863-14
Hoechst 33342 solution Thermo Fisher #62249
MitoTracker® Orange CMTMRos Thermo Fisher #M7510
Tetramethylrhodamine, methyl ester (TMRM) Thermo Fisher # T668
MitoTracker Green FM Thermo Fisher #M7514
IACS-10759 Axon Medhcem #Axon2909
__________________________________________________________________
Critical commercial assays
__________________________________________________________________
Human Kappa ELISA Kit Bethyl Laboratories #E88-115
MitoSOX^TM Red Mitochondrial Superoxide Indicator Thermo Fisher
#[285]M36008
Corning™ Cell-Tak Cell and Tissue Adhesive Fisher Scientific #10317081
Seahorse XFe96 FluxPak and Seahorse XF base medium Agilent # 102416-100
# 102353-100
CellTiter-Glo® Luminescent Cell Viability Assa Promega #G7570
Lonza™ Cell Line Nucleofector™ Kit R Lonza #VCA-1001
Dynabeads™ Antibody Coupling Kit Thermo Fisher #14311D
Apoptest Annexin A5- FITC kit VPS Diagnostics #A700
TruSeq Stranded mRNA library preparation kit Illumina #20020595
__________________________________________________________________
Deposited data
__________________________________________________________________
Metabolomics INA-6 KO and WT cells Mendeley Data
[286]10.17632/dyndvz5vfn.1
RNA-seq INA-6 KO and WT cells SRNA PRJNA769223
Raw images for all western blots Mendeley Data
[287]10.17632/hcdmjrjhft.1
R-codes Github [288]https://github.com/MjelleLab/IL32.git
__________________________________________________________________
Experimental models: Cell lines
__________________________________________________________________
INA-6 CLPUB00090 RRID:CVCL_5209
NCI-H929 ATCC #CRL-9068
JJN-3 ([289]Hamilton et al., 1991) RRID: CVCL-2078
__________________________________________________________________
Experimental models: Organisms/strains
__________________________________________________________________
RAG2^−/− γC^−/− BALB/c ([290]Groen et al., 2012) N/A
__________________________________________________________________
Oligonucleotides
__________________________________________________________________
IL32 (Hs00992441_m1) Thermo Fisher # 4331182
TBP (Hs00427620_m1) Thermo Fisher # 4331182
__________________________________________________________________
Recombinant DNA
__________________________________________________________________
pLV-U6-IL32/EF1a-puro-2A-CAs9-2A-GFP. Sigma-Aldrich # HS0000050421
Sigma lenti CRISPR Non-Targeting Control Sigma-Aldrich CRISPR12V-1EA
PsPAX2 Gift from Didier Trono (unpublished) Addgene plasmid #12260
pMD2.G Gift from Didier Trono (unpublished) Addgene plasmid #12259
IL-32 CRISPR/Cas9 KO plasmid (h2) Santa Cruz #sc-406489-KO-2
OmicsLink™ ORF lentiviral IL-32β vector GeneCopoeia #EX-M0733-Lv121
OmicsLink™ ORF lentiviral control vector GeneCopoeia #EX-EGFP-Lv121
pIRFP ([291]Filonov et al., 2011) Addgene plasmid #31857
__________________________________________________________________
Software and algorithms
__________________________________________________________________
Image Studio Software version 3.1 LI-COR
[292]https://www.licor.com/bio/image-studio/resources
RRID: SCR_015795
Fiji ([293]Schindelin et al., 2012)
[294]https://imagej.net/software/fiji/RRID: SCR_002285
FlowJo v10.7 FlowJo [295]https://www.flowjo.com/solutions/flowjo
RRID: SCR_008520
GraphPad Prism software GraphPad RRID: SCR_002798
R-studio software RStudio RRID: SCR_000432
Leica Application Suite X 3.55.19976 Leica Microsystems RRID:
SCR_013673
__________________________________________________________________
Other
__________________________________________________________________
Corning™ Cell-Tak Cell and Tissue Adhesive Fisher Scientific #10317081
[296]Open in a new tab
Resource availability
Lead contact
Further information and requests for resources and reagents should be
directed to the lead contact Therese Standal
([297]therese.standal@ntnu.no).
Materials availability
This study did not generate new unique reagents.
Experimental model and subject details
In vivo animal studies
RAG2^−/− γC^−/− BALB/c were obtained from Dr. Anton Martens (University
Medical Center, Utrecht, the Netherlands) ([298]Groen et al., 2012).
The mice lack B, T and NK cell immunity and were kept in specific
pathogen free (SPF) unit. The mice were housed in IVC-cages, with free
access to bedding material, nesting material and enrichment objects.
Mice were given sterile food (RM1 #801002, Special Diets Services,
Essex, UK) and water ad libitum, and were caged in groups of 3 to 5
mice. The mice were kept at a room temperature of 21°C to 22°C and 55%
humidity with a 12-h light/dark cycles including 1 hour dusk/dawn.
For the INA-6 in vivo experiment 20 female mice RAG2^−/− γC^−/− BALB/c
(12–16 weeks old) were used. Human bone marrow-derived mesenchymal
stromal cells (hMSC) from healthy donors were seeded on biphasic
calcium phosphate (BCP) scaffolds and differentiated toward osteoblasts
for 1 week in vitro before implanted subcutaneously on the back of the
mice (4 scaffold on each mice) and left for 2 months for establishment
of a differentiated human bone cell microenvironment as described
([299]Groen et al., 2012). 3 scaffolds on each mouse were injected with
10ˆ6 iRFP labelled INA-6 KO or WT cells and imaging was performed after
injection (timepoint 0) and then every week for 5 weeks. Images were
acquired at 700 nm using Pearl Impulse imaging system and data was
analyzed with Image Studio Software (both from Licor Biosciences). iRFP
signal from each scaffold on each time point was normalized to the
signal at day 0. For the JJN3 in vivo experiment JJN3 1 × 10^5 IL-32 KO
(N = 5) or IL-32 WT (N = 5) cells were injected into the tibiae of 9 to
12-week-old RAG2^−/− γC^−/− BALB/c. After 20 days, the mice were
euthanized. Blood was collected for quantification of human
immunoglobulin kappa light chain by ELISA (Bethyl Laboratories). Animal
handling and procedures were approved by the Norwegian food safety
authority (FOTS10517).
Cell culture
MM cell lines INA-6 and JJN-3 were kind gifts from Dr. Martin Gramatzki
(University of Erlangen-Nuremberg, Erlangen, Germany), and Dr. Jennifer
Ball (University of Birmingham, UK), respectively. NIC-H929 cells
(named H929 throughout the paper) were obtained from ATCC. H929 and
JJN-3 were cultured in 10% heat inactivated fetal calf serum (FCS) in
RPMI-1640 (RPMI) medium. INA-6 cells were cultured in 10% FCS in RPMI
with the addition of 1 ng/mL recombinant human (rh) interleukin (IL)-6.
MM cell lines were maintained at 37°C in a humidified atmosphere with
5% CO[2]. HEK293T cells (Open Biosystems, Thermo Fisher Scientific),
were cultured in 37°C in a humidified atmosphere with 8% CO[2] in DMEM
supplemented with 10% FCS.
Generation of IL-32-depleted cells
IL-32 KO cell lines were generated by lentiviral transduction (H929
cells) or plasmid transfection (JJN-3 and INA-6 cells). H929 cells were
transduced using lentiviral CRISPR IL-32 KO vector
pLV-U6-gRNA/EF1a-puro-2A-CAs9-2A-GFP and Sigma lenti CRISPR
Non-Targeting Control virus particles. The IL-32 KO vector were
packaged into viral particles in HEK293T cells using second generation
packaging plasmids psPAX2, and pMD2.G. After 48 h, virus particles in
supernatants were transduced into target cells incubated with polybrene
(8
[MATH: μ :MATH]
g/mL). Ready to use control virus particles were delivered to the cells
by spin transduction with the same concentration of polybrene. The H929
IL-32 KO and control vector cells (H929 WT) were then subjected to
negative selection using puromycin (0.5
[MATH: μ :MATH]
g/mL) followed by single cell cloning. JJN-3 and INA-6 cells were
electroporated with IL-32 CRISPR/Cas9 KO plasmid containing green
fluorescent protein (GFP) for selection, using buffer R (Amaxa
Nucleofector Kit R, Lonza) and program R-001. Cells were then sorted
for GFP positivity on a FACSAria Fusion flow cytometer (BD Biosciences)
and single cell cloned. Clones were screened for IL-32 expression by
flow cytometry and immunoblotting. For both cell lines: clones that had
been transfected with the plasmid (GFP^+), but still expressed IL-32
were used as control cells (IL-32 wild type [WT]). IL-32 protein
knockout was regularly confirmed by immunoblotting to ensure
homogeneity of the KO cell lines.
Generation of IL-32 knock-in cells
INA-6 IL-32 knock in cells were generated by lentiviral transduction of
INA-6 IL-32 KO using OmicsLink ORF lentiviral expression system with
vector for knock in of IL-32β (transcript variant 8) EX-M0733-Lv121 and
control vector: EX-EGFP-Lv121. Lentiviral packaging and transduction of
cells were performed as described for IL-32 depletion of H929 followed
by puromycin selection.
Generation of iRFP-labelled cells
iRFP labelling of INA-6 KO and WT cells was performed by lentiviral
transduction. For generation of iRFP plasmid, piRFP and pENTR4
(Invitrogen) were cut with SalI and NotI (Fermentas) and ligated using
a T4 DNA ligase (Fermentas) to achieve a gateway compatible iRFP entry
clone. The iRFP-ENTR4 was then recombinated into the
pLenti-CMV-Puro-Dest by a LR (Invitrogen) gateway reaction. iRFP
lentivirus was made by transfecting HEK293T using iRFP plasmid, psPAX2
and pMD2.G packaging plasmids followed by transduction of target cells
with polybrene. After transduction, iRFP-positive cells were sorted by
fluorescence-activated cell sorting giving a pure iRFP-labelled
population for in vivo studies.
Method details
Assessment of proliferation and survival
Proliferation of IL-32 KO and WT mock cells was assessed by cell
counting every day for 4 days using cell coulter (Beckman Coulter
Diagnostics). Proliferation was also examined by
brdu(5-bromo-2′-deoxyuridine) incorporation to quantify % of live cells
in S-phase cell cycle progression.
Brdu incorporation was performed with the following procedure:
The cells were seeded out at a concentration of 333,000 cells/mL and
incubated for 4 h with 10 μM BrdU in the culture media. The cells were
washed once in PBS and subsequently stained with Fixable Viability Dye
eFluor 450 for 30 min on ice. The cells were washed in PBS and
thereafter fixed in methanol. The cells were stored in methanol at 4°C
for up to three days before they were spun down and resuspended in PBS
before vortexed while slowly adding 1 mL of 2N HCl/0.05 %Triton x-100
to denature the DNA. The cells were then incubated at room temperature
for 30 min followed by centrifugation to remove supernatant. The cells
were then resuspended in 1 mL of 0.1 M Na2B4O7 (Borax), pH 8.5 for
neutralization before the cells were spun down to remove the
supernatant. Cells were then stained with Alexa Fluor 488 Anti-BrdU in
PBS with 1% BSA/0.1% Tween and analyzed using LSRII FACS Flowcytometer
(BD, USA). Gating of live cells, single cells and quantification of
brdu + cells were performed with Flow Jo version 10.
Cell viability was assessed with Annexin/PI flow cytometry. Cells were
sampled directly from cell culture flasks, stained with Apoptest
Annexin A5- FITC kit following manufacturer's instructions and analyzed
by flow cytometry using LSR II (BD Biosciences) with FACS Diva software
(BD Biosciences).
For experiments using the OXPHOS inhibitor IACS-10759 INA-6, H929 and
JJN-3 IL-32 KO and WT mock cells were seeded at a density of 33,000
cells/mL and grown in medium supplemented with IACS-10759 (10 nM).
Number of cells was determined by counting after 4 days of culture
using cell coulter (Beckman Coulter Diagnostics).
To examine the effect of rhIL-32 β and rhIL-32
[MATH: y :MATH]
on cell proliferation and viability, the cells were treated with
100 ng/mL recombinant IL-32 (R&D Systems). Proliferation was assessed
either by cell counting using cell coulter (Beckman Coulter
Diagnostics) or by CTG assay (Promega). For cell counting experiments
cells were counted every day for 3 days. For CTG assay 2500 cells/well
were seeded in 96-well optical plates and signal measured every day for
3 days. Data was normalized to the luminescent signal from day 0 when
cells were seeded in equal number. Viability after treatment with
rhIL-32 isoforms β and γ overnight was evaluated by trypan blue
staining.
Real-time quantitative PCR
Total RNA was isolated using RNeasy Mini Kit (Qiagen) and complementary
DNA (cDNA) was synthesized from total RNA using the High Capacity
RNA-to-cDNA kit (Applied Biosystems). qPCR was performed using StepOne
Real-Time PCR System and Taqman Gene Expression Assays (Applied
Biosystems) with standard settings (2′ 50°C, 10′ 95°C, 40 cycles at
95°C for 15 sec, 1′ 60°C). The comparative Ct method was used to
estimate relative changes in IL-32 gene expression using TBP as
housekeeping gene. Probes were as follows: human IL-32 (Hs00992441_m1)
housekeeping gene TATA binding protein (TBP; Hs00427620_m1)
Immunoblotting
Cells were lysed in lysis buffer (50 mM Tris–HCl, 1% NP40, 150 mM NaCl,
10% glycerol, 1 mM Na[3]VO[4], 50 mM NaF and Complete protease
inhibitor (Roche Diagnostics) and the lysates were denatured in 1×
NuPage LDS sample buffer supplemented with 0.1 mM DTT for 10 min at
70°C before they were separated on 10% Bis-Tris polyacrylamide gel.
Proteins were transferred to a nitrocellulose membrane using the iBlot
Dry Blotting System (Invitrogen). Membranes were blocked using 5%
bovine serum albumin (Sigma–Aldrich) in Tris-buffered saline with 0.01%
Tween followed by overnight incubation with the primary antibodies
previously described. Detection was performed using horseradish
peroxidase (HRP) conjugated antibodies (DAKO) and developed with Super
Signal West Femto Maximum Sensitivity Substrate (Thermo Scientific).
Images were obtained with LI-COR Odyssey Fc and analyzed using Image
Studio Software (LI-COR).
Surface markers
For assessment of CD38 and CD45 on IL-32 KO and WT cells, live cells
were stained with anti- CD38 PE-Cy7 and CD45-FITC antibodies and
assessed by flow cytometry. Samples were analyzed with FlowJo V10.
ELISA
For confirmation of biological activity of recombinant human
recombinant IL-32, peripheral blood mononuclear cells (PBMCs) were
isolated from blood from healthy human donors were provided by the
blood bank as St. Olavs Hospital (REK#2009/2245) with a density
gradient using Lympoprep (Alere). CD14^+ cells were isolated from the
PBMCs using CD14 magnetic beads (CD14 MicroBeads UltraPure, Human,
Miltenyi Biotech). The CD14^+ cells were seeded out 30,000 cells/well
in a 96-well plate in RPMI 1640 (Sigma) supplemented with 10% human
serum, 10mM HEPES (Gibco) and 10 ng/mL M-CSF (R&D Systems). At day 3,
half the medium was changed. At day 6, the cells were rested in medium
without M-CSF. At day 8, the cells were stimulated with 100 ng/mL
rhIL-32 beta and IL-32 gamma in RPMI 1640 (Sigma) supplemented with 2%
FCS, 10mM HEPES (Gibco). After 24 h of stimulation the supernatant was
harvested. TNF
[MATH: a :MATH]
in the supernatant was quantified using Human TNF-alpha DuoSet ELISA
(R&D systems) according to the manufacturer's instructions.
For quantification of immunoglobulin kappa light chain secretion from
myeloma cell lines 1∗10^6 cells were seeded in 1 mL RPMI, 0.1% BSA with
the addition of 1 ng/mL IL-6 for INA-6 cells and incubated for 24 h in
5% O[2,] 37°C. The cells were counted, and Ig kappa light chain
concentration were quantified in the cell culture supernatant by ELISA
(Human Kappa ELISA, Bethyl Laboratories).
Co-immunoprecipitation and mass spectrometry
IL-32 antibody (RnD) was conjugated to M-270 beads according to the
manufacturer's instructions, using Dynabeads Antibody Coupling Kit and
10 mg beads on 500 μL (100ug) resuspended IL-32 antibody. For CO-IP MS
analysis JJN-3 KO and WT cells were incubated in hypoxia for 24 h
before lysis with 4x pellet volume RIPA buffer (1% CHAPS, 50 mM Tris,
150 mM NaCl, Complete protease inhibitor (Roche Diagnostics) and
phosphatase inhibitor cocktails 2 and 3 (Sigma-Aldrich), for 1 h at 4°C
on rotation. Lysate was pre-cleared by adding 40μL beads to ∼400 μL
lysate for 1 h at 4° on rotation, before incubated with IL-32 antibody
conjugated beads for 2 h at 4°C. After washing 4 times with PBS
repelleted beads were subjected to MS-digestion protocol. Beads were
resuspended 150 μL 50 mM NH4HCO3, followed by addition of 7.5 μL 200 mM
DTT, 55°C for 30 min. Samples were cooled to RT before addition of
15 μL 200mM IAA and incubation in RT for 30 min in the dark. Then
samples were treated 1.5 μg trypsin (MS-grade) at 37°C over-night,
before beads were removed and liquid sample was dried using Speedvac
(Thermo Fischer Scientific).
After tryptic digestion peptides were desalted using STAGETIP as
previously described ([300]Rappsilber et al., 2003). After desalting,
peptides were dried down in a SpeedVac centrifuge and resuspended in
0.1% formic acid. The peptides were analyzed on a LC-MS/MS platform
consisting of an Easy-nLC 1200 UHPLC system (Thermo Fisher Scientific)
interfaced with an QExactive HF orbitrap mass spectrometer (Thermo
Fisher Scientific) via a nanospray ESI ion source (Proxeon). Peptides
were injected into a C-18 trap column (Acclaim PepMap100, 75 μm i. d. ×
2 cm, C18, 3 μm, 100 Å, Thermo Fisher Scientific) and further separated
on a C-18 analytical column (Acclaim PepMap100, 75 μm i. d. × 50 cm,
C18, 2 μm, 100 Å, Thermo Fisher Scientific) using a multistep gradient
with buffer A (0.1% formic acid) and buffer B (80% CH3CN, 0.1% formic
acid): From 2% to 10% B in 10 min, 10% to 50% B in 130 min, 50% to 100%
B in 20 min and 20 min with 100% buffer B. The HPLC were
re-equilibrated with 2% buffer B before next injection. The flow rate
was 250 nL/min. Peptides eluted were analyzed on QExactive HF mass
spectrometer operating in positive ion- and data dependent acquisition
mode using the following parameters: Electrospray voltage 1.9 kV, HCD
fragmentation with normalized collision energy 32, automatic gain
control target value of 3E6 for Orbitrap MS and 1E5 for MS/MS scans.
Each MS scan (m/z 300–1600) was acquired at a resolution of 12,000
FWHM, followed by 15 MS/MS scans triggered for AGC targets above 2E3,
at a maximum ion injection time of 50 ms for MS and 100 ms for MS/MS
scans.
5 replicates each of KO and WT cells were used for mass spectrometry
analysis. The IL32-specific proteins were detected by subtracting the
peptides detected in the KO-cells from the peptides detected in the
WT-cells. We required the peptides to be detected in all 5 WT
replicates. To assess the probability of detecting a frequency of 7
mitochondrial out of 36 IP target proteins we based the expected
frequency of 1100 mitochondrial proteins ([301]Calvo and Mootha, 2010)
and 20,000 proteins as the total number of proteins in the human
proteome.
For validation of interaction partners in the mitochondrial electron
transport chain we chose two candidates for validation, ATP synthase
subunit delta (ATP5D) a subunit in the ATP-synthase complex (complex
IV) and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 12
(NDUFA12), a subunit of the NADPH dehydrogenase (complex I). IL-32 was
pulled down following the same protocol as earlier, in INA-6, IH-1 and
JJN-3 cells, and ATP5D and NDUFA12 were detected by western blotting.
Metabolomics
Targeted metabolomics (GC-MS and LS-MS) of INA-6 WT and two KO cell
lines were performed by MetaSysX (Potsdam-Golm, Germany). The sample
preparation was performed according to the company's standard
procedure, a modified protocol from Salem et al. ([302]Salem et al.,
2016). 10ˆ8 cells/replicate was used for metabolite extraction.
LC-MS Measurements (Hydrophilic and Lipophilic Analytes) were performed
using Waters ACQUITY Reversed Phase Ultra Performance Liquid
Chromatography (RP-UPLC) coupled to a Thermo-Fisher Exactive mass
spectrometer. C8 and C18 columns were used for the lipophilic and the
hydrophilic measurements, respectively. Chromatograms were recorded in
Full Scan MS mode (Mass Range [100–1500]). All mass spectra were
acquired in positive and negative ionization modes. Extraction of the
LC-MS data was accomplished with the software REFINER MS 11.1
(GeneData, [303]http://www.genedata.com). Alignment and filtration of
the LC-MS data were completed using metaSysX in-house software. After
extraction from the chromatograms, the data was processed, aligned and
filtered for redundant peaks. The alignment of the extracted data from
each chromatogram was performed according to the criteria that a
feature had to be present in at least 3 out of 4 replicates from one
group. At this stage, the average RT and m/z values was given to the
features. The alignment was performed for each type of measurement
independently, followed by the application of various filters in order
to refine the dataset, which included the removal of isotopic peaks,
in-source fragments of the analytes (due to the ionization method), and
redundant peaks like additional less intense adducts of the same
analyte and redundant derivatives, to guarantee the quality of the data
for further statistical analyses. The in-house metaSysX annotation
database of chemical compounds was used to match features detected in
the LC-MS polar and lipophilic platform. The annotation of the content
of the sample was performed by database query of mass-to-charge ratio
and the retention time of detected features within certain criteria.
The metaSysX in-house database contains mass-to-charge ratio and
retention time information of 7500 reference compounds available as
pure compounds and measured in the same chromatographic and
spectrometric conditions as the measured samples. In addition, 1500
lipids and sugar esters were putatively annotated based on the
precursor m/z, fragmentation spectrum and elution patterns. The
matching criteria for the polar and non-polar platforms were 5 ppm and
0.085 min' _deviation from the reference compounds mass-to-charge ratio
and retention time, respectively. Coeluting compounds with the same
mass-to-charge ratio were all kept and the names are separated with
"|". Lipid annotation was additionally performed and confirmed by MS/MS
fragmentation spectrum using the metaSysX developed-R-based algorithm.
This information is combined with the information from the annotation
after the query of the MSX database.
GC-MS measurements were performed on an Agilent Technologies GC coupled
to a Leco Pegasus HT mass spectrometer which consists of an EI
ionization source and a TOF mass analyzer (column: 30 meters DB35;
Starting temp: 85°C for 2min; Gradient: 15°C per min up to 360°C).
NetCDF files that were exported from the Leco Pegasus software were
imported to "R". The Bioconductor package TargetSearch
([304]Cuadros-Inostroza et al., 2009) was used to transform retention
time to retention index (RI), to align the chromatograms, to extract
the peaks, and to annotate them by comparing the spectra and the RI to
the Fiehn Library and to a user created library. The annotation of
peaks was manually confirmed in Leco Pegasus. Analytes were quantified
using a unique mass. Metabolites with an RT and a mass spectra that did
not result in a match in the database were kept as not assigned
metabolites. Statistical analysis of significantly upregulated and
downregulated metabolites in INA-6 KO clones compared to WT, metabolite
enrichment, and joint pathway analysis for metabolomics and
transcriptomics (RNA-seq) data was performed using Metaboanalyst 4.0
software. Specifically, in Metaboanalyst 4.0, normalized metabolite
values with Peak IDs as provided by metaSysX were uploaded together
with significant gene symbols (Fold change >0.5 or < −0.5 and adjusted
p value <0.05) with corresponding logFC values from the INA-6 cell
line. The significant genes for the INA-6 cell line were determined
using limma in R using the script provided in the section “Data and
code availability”.
Confocal imaging
For IL-32/mitochondria colocalization and mitochondrial morphology
studies cells were cultured in hypoxia (2% O[2], 5% CO[2]) overnight
before seeded in poly-L-lysine coated 96 well glass bottom plates
(In Vitro Scientific) and left to attach for 20 min at 37°C before
fixed with 4% PFA, 10 min at RT. After quenching for 10 min with 50 mM
NH[4]Cl, permeabilization was performed using 0.05% saponin in PBS.
Primary antibody cocktail (anti-IL-32 and anti-TOMM20 or anti-TOMM20
only, 2 μg/mL) diluted in 1% HS, 0.05% saponin was left on overnight at
4°C. Next day, secondary antibody (Donkey anti-Rabbit IgG (H + L) Alexa
Fluor 488 or/and Chicken anti-goat IgG (H + L) Antibody Alexa Fluor
647, 2 μg/mL) in 1% HS, 0.05% saponin was added for 30 min, before
leaving cells in Hoechst (Thermo Fisher) in PBS (2 μg/mL) for imaging.
For lipid droplet staining with Nile Red cells were attached to
poly-L-lysine coated 96-well glass bottom plates, before fixed with 4%
PFA, 10 min at RT and stained with 500 nM Nile Red (Thermo Fisher)
for 10 min at 37°C. Polar lipids were excited at 590 nm (600–700 nm)
and neutral lipids at 488 nm (500–580 nm) as described previously
([305]Schnitzler et al., 2017).
All confocal imaging was performed on a Leica TCS SP8 STED 3X confocal
laser scanning microscope (Leica Microsystems, Wetzlar, Germany) using
a 63×/1.40 oil objective. Images of mitochondria (colocalization and
morphology) were deconvoluted using Huygens Professional software
(Hilversum, the Netherlands).
Quantification of mitochondrial length was performed in Fiji Software,
using ROI manager to measure the length of each individual mitochondria
in 3 cells/image in 5 different images for each independent experiment.
Mean length of mitochondria in IL-32 KO and WT mock cell lines was
calculated based on 3 independent experiments.
Quantification of the colocalization between IL-32 and mitochondria was
performed using Leica Application Suite X, with a threshold for
colocalization and background at 30% on both IL-32 and TOMM20 channels.
With this settings, colocalization rate (CR) were analyzed for 4 images
with 1 to 3 cells/image from each of the cell lines (hypoxic H929,
INA-6 and JJN-3 WT cells) and mean CR±SD were calculated. The
colocalization rate value is the ratio of the area of colocalizing
fluorescence signals and the area of the image foreground.
Seahorse metabolic assays on cells
Oxygen consumption rates (OCR) and extracellular acidification rates
(ECAR) were measured using Seahorse XF96 bioscience extracellular flux
analyzer (Agilent). Seahorse XF Cell culture microplates were treated
with Cell Tak according to the manufacturer's instructions and number
of viable cells was determined using Countess Automated cell coulter
with trypan blue stain before plated in 22 or more replicates at a
density of 25,000 cells/well. For measurement of basal and maximal OCR
the cells were analyzed by mito stress assay. For mito stress assay
cells were incubated in XF assay base medium supplemented with 10 mM
Na-Pyruvate, 2 mM glutamine (both from Sigma) and 10 mM glucose (Merck)
followed by injections of oligomycin, carbonyl cyanide
p-trifluoro-methoxyphenyl hydrazone (FCCP); and antimycin A+ rotenone
at final concentrations of 1
[MATH: μ :MATH]
M, 1
[MATH: μ :MATH]
M, and 2
[MATH: μ
:MATH]
M + 2
[MATH: μ :MATH]
M, respectively. Basal and maximum OCR and were calculated according to
manufacturer's instructions. Glycolysis stress test was used for
evaluation of glycolysis. Cells were incubated in XF assay medium with
2 mM glutamine, followed by injections of glucose, oligomycin and
2-deoxy-glucose (2-DG) at final concentrations of 10 mM, 1
[MATH: μ :MATH]
M and 50 mM, respectively. Basal ECAR was calculated by subtracting the
acidification rate after 2-DG injection from the acidification rate
after glucose injection. For IL-32 rescue cells, dead cells were
removed by Optiprep (Stemcell Technologies, Vancouver, Canada)
according to manufacturer's instructions and viable cells counted with
the Countess Automated Cell Coulter before basal OCR and ECAR were
evaluated by combining the mito stress and glycolysis stress test. The
same medium supplements as for mito stress assay were used, except
glucose, which was added as the first injection in the assay (final
concentration of 10 mM) before injections with oligomycin, FCCP and
Rotenone + antimycin+ 2-DG. Combining the injections for the two stress
tests enable evaluation of both glycolysis and OXPHOS in the same cells
at the same time.
Mitochondrial membrane potential in whole cells
For measurement of mitochondrial membrane potential IL-32 KO and WT
myeloma cell lines from basal culture conditions were co-stained with
50 nM mitotracker green (Thermo Fisher) and 20 nM tetramethylrhodamine,
methyl ester, perchlorate (TMRM) in serum free RPMI for 30 min at 37°C.
10 min before end of incubation FCCP was added to negative control
samples (background staining) at a final concentration of 3 μM for
depolarization of mitochondrial membrane. The dye was removed by two
washes at 448 × g with PBS at 4°C, before resuspended in 2% FCS/PBS and
analyzed by flow cytometry using LSR II (BD Biosciences) with FACS Diva
software (BD Biosciences). TMRM was detected at excitation/emission
548/574 nm and mitotracker green in the 490/516 nm. The relative
mitochondrial membrane potential was calculated by calculating ratio of
the median fluorescence intensity (MFI) of TMRM (membrane potential)
and mitotracker green (mitochondrial mass) to adjust for potential
differences in mitochondrial mass between KO and WT cell lines. Further
the same ratio was calculated for the FCCP (depolarized) treated
sample,and was subtracted to remove the signal conferred by unspecific
TMRM staining. The following formula was used:
[MATH:
Nontreated(M<
mi>FITMRM
MFImitotracker green
)−FCCP(MFITMRM
MFImitotracker green
).
:MATH]
Mitochondrial ROS and mitochondrial mass in whole cells
For mitochondrial ROS samples were stained mitoSOX (5 μM) for 15 min.
The dye was removed by two washes at 448 × g with PBS at 4°C, before
resuspended in 2% FCS/PBS and analyzed by flow cytometry. MitoSoX was
detected at excitation/emission 510/580 nm using LSR II (BD
Biosciences) with FACS Diva software (BD Biosciences). MFI of
mitochondrial ROS was normalized with WT as reference for each separate
experiment. For analysis of mitochondrial mass in whole cells, cells
were stained with 50 nM mitotracker green (Thermo Fisher) in serum free
RPMI for 30 min at 37°C. The dye was removed by two washes at 448 × g
with PBS at 4°C, before resuspended in 2% FCS/PBS and analyzed by flow
cytometry at 490/516 nm using LSR II (BD Biosciences) with FACS Diva
software (BD Biosciences).
ATP quantification
For ATP measurements, 40,000 cells were seeded/well and ATP levels were
measured by CellTiter-Glo Luminescent (CTG) Cell Viability Assay
(Promega) following manufacturer's instructions. Luminescence was
recorded using a Victor3 plate reader and Wallac 1420 Work Station
software (PerkinElmer Inc.).
Isolation of mitochondria
Mitochondria from IL-32 KO and WT cells were isolated according to the
protocol previously described ([306]Lampl et al., 2015). 70–100∗10^6
IL-32 KO and WT cells were harvested and pelleted for 8 min at 700 × g
at 4°C before resuspended in PBS. Samples were then transferred to
Eppendorf tubes and pelleted using a bench top centrifuge at 700 ∗g for
5 min at at 4°C. Each pellet was resuspended in 1 mL of mitochondrial
isolation buffer (MIB; 200 mM sucrose, 10 mM tris/mops (PH 7.4), and
1 mM EGTA/Tris) and transferred to a small glass vessel on ice for
homogenization. Cells were homogenized with a pestle for 20 passes.
Homogenate was then drawn into a syringe with 26 gauge∗1/2-inch needle
and expelled 5 times against the inside wall of the tube as to utilize
the force for cell membrane disruption, before transferred back to the
vessel and homogenized with the pestle for 20 more passes. The solution
was then centrifuged for 5 min at 600 × g, 4°C in a table top
centrifuge to remove cell debris and mitochondria in supernatant was
pelleted with a second spin at 10,000 × g, 4°C for 5 min. Mitochondrial
pellets were resuspended in MIB on ice and concentration assayed by
Bradford assay followed by downstream applications.
Seahorse metabolic assay on isolated mitochondria
Isolated mitochondria from 70-100∗10^6 IL-32 KO and WT cells were
quantified by Bradford assay and resuspended to desired concentration
in mitochondrial assay buffer (MAS) (70 mM sucrose, 220 mM mannitol,
10 mM KH[2]PO[4], 5 mM MgCL[2], 2 mM HEPES, 1 mM EGTA and 0.2% fatty
acid free BSA) which had been supplemented with 2 mM malate, 10 mM
Na-pyruvate and 5 mM glutamic acid and preadjusted to pH 7.2 at 37°C.
20
[MATH: u :MATH]
g mitochondria were plated in each well of an XFe96 seahorse plate and
the plate was spun at 2000 × g for 20 min at 4°C for attachment of the
mitochondria. After centrifugation pre-varmed (37°C) MAS + substrates
were added to each well to a final volume of 180 mL incubated in a
non-CO[2] incubator for 20 min, before basal oxygen consumption rate
(OCR) was measured using Seahorse XF96 bioscience extracellular flux
analyzer (Agilent, CA, US).
Mitochondrial ROS and membrane potential in isolated mitochondria
Isolated mitochondria were quantified by Bradford assay and stained for
10 min at 37°C in a 20% O[2], 5% CO[2] incubator with 5 mM MitoSox Red
(Thermo Fisher) in MAS (70mM sucrose, 220 mM mannitol, 10 mM
KH[2]PO[4], 5 mM MgCL[2], 2 mM HEPES, 1 mM EGTA and 0.2% fatty acid
free BSA) which had been supplemented with 2 mM malate, 10 mM
Na-pyruvate and 5 mM glutamic acid and preadjusted to pH 7.2 at 37°C.
After staining the mitochondria were washed once in MAS and resuspended
in MAS. 10
[MATH: u :MATH]
g mitochondria/well were plated in 96-well optical plates and incubated
for 60 min in 37°C (20% O[2], 5% CO[2] incubator). The fluorescence was
assessed at with excitation 531 nm and emission 590 nm using Victor3
plate reader and Wallac 1420 Work Station software. For assessment of
membrane potential, the isolated mitochondria were prepared in MAS
buffer, stained with 500 nM Mitotracker Orange CMTMRos for 30 min 37°C
in a 20% O[2], 5% CO[2] incubator before washed with MAS and seeded in
10 mg/well in 96-well optical plates and assessed immediately with
excitation 531nm and emission 590 nm using Victor3 plate reader and
Wallac 1420 Work Station software.
RNA-sequencing of IL-32 KO and WT cell lines
INA-6 IL-32 KO and WT cells were harvested from basal culture
conditions and RNA was isolated using RNeasy kit (Qiagen). Samples were
sequenced (18 million reads per sample) using the TruSeq Stranded mRNA
library preparation kit from Illumina followed by 75bp single read
sequencing on the Illumina Hiseq 4000 next machine.
RNA-sequencing data analyses in INA-6 KO and WT cells
The RNA-seq data were first aligned to the human genome with STAR
aligner using the genome version GRCh38.p7 and the primary assembly
(Homo_sapiens.GRCh38.DNA.primary_assembly.fa).
We used the following parameters for STAR aligner: STAR --genomeDir
GRCh38.p7/star --readFilesIn --readFilesCommand zcat
--outFileNamePrefix --chimSegmentMin 30 --runThreadN 20
--outFilterMultimapNmax 20 --alignSJoverhangMin 8
--alignSJDBoverhangMin 1 --outFilterMismatchNmax 10
--outFilterMismatchNoverLmax 0.04 --alignIntronMin 20 –alignIntronMax
1000000.
Following the alignment, the sam files were used as input into
htseq-count to create the count table using the GFF file corresponding
to the GRCh38.p7 genome assembly. For htseq-count we used the following
parameters: htseq-count -s no -i gene_id -t exon.
Principle component analysis (PCA) was carried out using the stats
package with the prcomp function in R and visualized using the ggbio
package. Differentially expressed genes were calculated using
limma-voom in R. Scripts for PCA and limma analysis are provided in
“Data and code availability”. We required genes to be expressed with at
least 1 cpm in 20% of the samples. We used TMM normalization for
calculating the “calcNormFactors” in limma. Differentially expressed
genes between KO and WT were determined by setting a contrast in
limma-voom such that the average of the two KO-clones were subtracted
from the WT-clone: KO1KO2_vs_WT=(0.5∗(KO1+KO2))-WT. p values were
adjusted using Benjamini-Hochberg.
The GO-analyses for the IL32- KO and WT sequencing data were performed
as described below for the CoMMpass data (see figure texts for
filtering of data). IL-32 isoform analysis was performed using
Kallisto (v0.43.0). The Kallisto analysis were performed on the
fastq-files using the following parameters: kallisto quant -i
Homo_sapiens.GRCh38.cdna.all.release-94_k31.idx -o output -b 100 -L 200
-s 20 --single -t 20 input.fastq.gz Kallisto index was created using
the following command: kallisto index -k31
Homo_sapiens.GRCh38.cdna.all.fa.gz.
RNA-sequencing data analyses in MMRF CoMMpass
RNA sequencing data (MMRF_CoMMpass_IA13a_E74GTF_Salmon_Gene_Counts) and
clinical data were downloaded from the Multiple Myeloma Research
Foundation CoMMpass IA13 release. RNA sequencing data from CD138^+cells
were available for 795 baseline samples from patients with MM. Data on
overall survival and progression-free survival were available for all
these patients. RNA-sequenced CD138^+ cells from longitudinal samples
were available for 47 samples in IA13. We analyzed IL-32 expression in
47 patients at diagnosis and first relapse time point. For survival
analyses, patient samples taken at diagnosis were divided into high and
low IL-32 expression based on the upper 10th percentile (n = 54; counts
per million (cpm,log2)>1.52) and lower 90th percentile (n = 741,
(cpm,log2)<1.52). Differentially expressed genes between high (10th
percentile) and low IL32 (90th percentile) were assessed using the same
percentiles, and expression-requirement of 1cpm in at least 20% of the
samples. Differentially expressed genes were calculated using
limma-voom in R using the script provided in “Data and code
availability”. GO analyses were performed using R package
ClusterProfiler (v3.14.3) using expressed genes as background. Cutoff
for significance of genes implemented in GO was p = 0.01.
Survival-analysis was performed in R, using the package “survival”.
High and low IL-32 was defined as previously described and “Time” and
“Status/Sensoring” were collected from the clinical data in ComMMpass.
Survival curves were plotted using “ggsurvplot” in R using the package
“survminer”.
All analyses were run using R version 3.6.2 (2019-12-12). Used packages
with version number includes:packageVersion ("survminer")‘0.4.6’;
packageVersion
("biomaRt")‘2.41.4’; packageVersion("clusterProfiler")‘3.12.0’;
packageVersion("org.Hs.eg.db")‘3.8.2’; packageVersion("limma")‘3.40.6’;
packageVersion("edgeR")‘3.26.8’; packageVersion("survival")‘3.1.8’;
packageVersion("pheatmap")‘1.0.12’; packageVersion("ggplot2″) ‘3.2.1’.
Single-cell transcriptome analysis
The single cell data was download from Gene Expression Omnibus (GEO)
using the accession number [307]GSE106218. The data was analyzed as
described by Ryu et al. ([308]Ryu et al., 2020) using the Seurat
package in R ([309]Satija et al., 2015). Specifically, dimension
reduction was performed using uniform manifold approximation and
projection (UMAP) by using the 10 first principal components from the
FindNeighbors and FindClusters functions in Seurat. IL-32-expressing
cells were defined as cells for which at least one read for IL-32 was
detected and IL-32 non-expressing cells were defined as cells for which
no reads for IL32 were detected. High and low IL32 expression is
defined similarly. High and low IL32 was defined within each patient
for all analyses. Differentially expressed genes between
IL-32-expressing cells and IL-32 non-expressing cells were detected
using the FindMarkers function in Seurat. For each of the three
patients that express IL-32, we performed differential expression
analysis between cells expression high levels of IL32 (top 10%
quantile) and low IL32 levels (bottom 90% quantile) to remove
patient-specific biases in the analysis. The three lists of
differentially expressed genes were then merged and used as input in
the gene-ontology analysis. Gene ontology (GO) analysis was performed
using the package clusterProfiler Yu ([310]Yu et al., 2012) and the
enrichGO function in R. The p values were adjusted using
Benjamini-Hochberg method and a q-value cutoff of 0.05 was used. Genes
with log2 fold change above 0.5 was used as input in the gene ontology
analysis and genes expressed in at least one cells were used as
background. The simplify function in clusterProfiler was used to merge
similar GO-terms. The GO-terms were order by q-value and the top 20
terms were plotted using ggplot in R.
Quantification and statistical analysis
Statistical analyses were performed using GraphPad Prism version 9
(GraphPad Software) unless otherwise stated. Paired or paired ratio or
unpaired Student's t test or Wilcoxon signed-rank test were used to
compare two groups. For comparison of two groups, and more than two
groups with measurements over time, multiple t-tests and two-way ANOVA
followed by Sidàk's or Dunnett's multiple comparisons test were used,
respectively. Statistical details, including value of N (which
represents independent experiments), definition of statistical
significance (asterisk representing p value and cutoff values) and how
data were quantified, including error bars (SD or SEM) can be found in
the figure legends. p values indicated with asterisk as follows: ∗p
≤0.05, ∗∗p≤ 0.01, ∗∗∗p ≤0.001, ∗∗∗∗p ≤0.0001.
Acknowledgments