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. 2025 Aug 18;9(8):e70197. doi: 10.1002/hem3.70197

Syntenin inhibition impairs stroma‐tumor communication in multiple myeloma and improves bortezomib treatment efficiency

Chenggong Tu 1,2, Raphael Leblanc 3, Arne Van der Vreken 1, Marnix Koops 4, Stephane Audebert 5, Lauriane Goullieux 3, Sofie Meeussen 2, Kim De Veirman 1, Elke De Bruyne 1, Karin Vanderkerken 1, Guido David 2,3, Tom Cupedo 4, Pascale Zimmermann 2,3,^,✉, Eline Menu 1,^,✉
PMCID: PMC12358804  PMID: 40832015

Abstract

Multiple myeloma (MM) remains incurable due to the development of drug resistance. We previously showed that communication between bone marrow stromal cells (BMSCs) and MM cells supports MM growth and triggers therapy resistance. This communication occurs through a plethora of mechanisms, including the release of cytokines and small extracellular vesicles (sEVs). The PDZ protein syntenin is a master regulator of intercellular communication, in particular via sEVs. In this study, we aimed to explore whether targeting syntenin, by genetic alteration or pharmacological inhibition, can disrupt BMSC–MM crosstalk, thereby rendering the MM cells more sensitive to therapy. We found that syntenin (SDCBP) is highly expressed in inflammatory BMSC of MM patients and that its expression in BM aspirates correlates with poor patient survival. Using in vitro models, we established that knockout of syntenin in BMSC alters their secretome and abolishes BMSC‐induced bortezomib resistance of MM cells via regulation of STAT3, MAPK, and AKT‐mTOR pathways. Pharmacological inhibition of syntenin decreases syntenin and IL‐6 sorting into BMSC sEVs and enhances bortezomib‐induced MM cell death. Finally, we validated the therapeutic added value of syntenin inhibition in combination with bortezomib in vivo, using the 5TGM1 MM mouse model. In conclusion, our findings show that syntenin supports the secretion of pro‐tumoral factors by BMSCs and qualifies as a possible novel therapeutic target in MM.

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INTRODUCTION

Multiple myeloma (MM) is a plasma cell malignancy that predominantly colonizes the bone marrow (BM). 1 , 2 , 3 Despite significant therapeutic advancements, including the application of drugs such as proteasome inhibitors (bortezomib and carfilzomib), MM remains incurable due to drug resistance (DR) and relapse. 4 This is, at least in part, mediated by the BM environment. 5

Bone marrow stromal cells (BMSCs) promote MM cell growth and survival, not only through direct interaction with myeloma cells but also via their secretome, which includes growth factors, such as interleukin (IL)‐6, IGF‐1, and VEGF, 6 and small extracellular vesicles (sEVs) 7 of 30–200 nm size, loaded with diverse and pleiotropic cargo. These sEVs can be internalized by MM cells, thereby facilitating MM progression and inducing DR to bortezomib. 8 , 9

Syntenin is a PDZ protein that controls the trafficking, cell surface abundance, and signaling of multiple proteins. 10 , 11 In adulthood, syntenin can act as a pro‐tumoral factor, activating several signaling pathways that promote tumor angiogenesis and the development and metastasis of cancers. 12 , 13 Syntenin is also involved in DR in solid cancer, where it induces protective autophagy in glioma cells 14 and enhances stemness. 15

Moreover, syntenin is known to be expressed in BMSCs 16 and is shown to be involved in the biogenesis and uptake of sEVs, thereby controlling intercellular communication. 17 , 18 , 19 , 20 The importance of such communication is noted in B‐ALL, where the transfer of stromal syntenin and syndecan via sEVs leads to a more immature B‐cell phenotype and increases the number of leukemia‐initiating cells. 21

Targeting syntenin might therefore interrupt the communication between the BM environment and MM cells and thereby influence therapy resistance. However, this hypothesis has never been explored, and, so far, the role of syntenin in myeloma biology has only marginally been investigated. 22

Using structure–function approaches, we have developed and characterized a small chemical compound targeting the PDZ2 domain of syntenin, termed SyntOFF. We found that SyntOFF alters the sorting of various cargoes into sEVs. 23 In the present study, we used both SyntOFF and genetic targeting to investigate the downstream effects of syntenin inhibition. Moreover, we evaluated the potential of syntenin as a possible therapeutic target and candidate for pharmacological inhibition in MM treatment.

METHODS AND MATERIALS

Public gene expression data

Syntenin expression was examined using microarray‐based gene expression data for 346 MM patients, treated with thalidomide and melphalan‐based tandem transplants, termed Total Therapy 2 (TT2). These data are provided by the University of Arkansas for Medical Sciences (GSE2658). Syntenin expression was correlated to patient outcome and compared between the different stages of MM based on ISS classification. Survival analysis was performed by GenomicScape. Syntenin RNA expression in MM cells from newly diagnosed MM patients treated with bortezomib, cyclophosphamide, and dexamethasone (n = 71) or bortezomib and dexamethasone (n = 58) was correlated with overall survival from CoMMpass datasets. The MMRF CoMMpass Trial (NCT01454297) is a longitudinal study of newly diagnosed MM patients in which genomic data are collected at diagnosis and subsequent relapse(s). The sequencing and clinical data, including survival information, are publicly available through the MMRF research gateway portal (https://research.themmrf.org). The analyses of the single‐cell RNA sequencing datasets for mesenchymal stromal cells (MSCs) in healthy individuals and MM patients were based on data from our previous study. 24

Cell culture

Human myeloma cell lines (XG2, RPMI8226, OPM2, LP1, and JJN3) and murine myeloma cell lines (5TGM1, 5T33MMvt) were cultured in RPMI1640 medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% heat‐inactivated fetal bovine serum (FBS) (Biochrom AG, Berlin, Germany) and 100 U/mL penicillin,100 μg/mL streptomycin, and 2 mM l‐glutamine (Thermo Fisher Scientific). BMSCs HS‐5 (human) and MS‐5 (murine) were cultured in Dulbecco's modified Eagle's medium supplemented with 10% FBS, 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM l‐glutamine. Cells were routinely tested for Mycoplasma contamination. All cells were maintained at 37°C in a humidified 5% CO2 incubator.

Syntenin knockout in HS‐5 and MS‐5 cell lines

To generate syntenin‐knockout (SyntKO) HS‐5 and MS‐5 cells, the syntenin gene was targeted using the CRISPR/Cas9 genome editing system as previously described. 25 Briefly, HS‐5 and MS‐5 cells were transfected using ViaFect reagent (Promega) the day after plating. Transfected cells were sorted by eGFP expression using BD FACSMelody™ Cell Sorter. Single‐cell clones were grown in 96‐well plates as one cell per well. After a few weeks, clones were collected and verified by western blot for syntenin expression. One individual clone has been used in the in vitro experiments.

Reagents and antibodies

A small molecule inhibitor, SyntOFF, was produced as described before 23 , 26 and dissolved in dimethyl sulfoxide at a concentration of 50 mM and stored at −20°C for further use. Bortezomib was purchased from Selleckchem (Munich, Germany) and dissolved in dimethyl sulfoxide at 10 mM for storage at −20°C. Antibodies to Flotillin‐1 (3253), HSP90 (4874), HSP70 (4872), Calreticulin (12238), Alix (2171), Rab27a (69295), Rab7 (2094), Rab11a (2413), β‐actin (4967), p‐STAT3 (9138), STAT3 (4904), p‐p53 (9281), p53 (2524), p‐44/42 (9106), p44/42 (9102), p‐p38 (9215), p‐38 (9212), p‐SAPK/JNK (9255), SAPK/JNK (9252), p‐c‐jun (2361), c‐jun (9165), c‐Myc (5605), p‐Akt (4056), Akt (9272), p‐mammalian target of rapamycin (mTOR) (Ser2448) (5536), mTOR (2983), p‐p70S6K (9205), p70S6K(9234), p‐PRAS40 (13175), PRAS40 (2691), p‐S6 (4858), S6 (2217), p‐4E‐BP1 (2855), 4E‐BP1 (9644 GAPDH (5174), and HRP‐linked anti‐mouse (7076) and ‐rabbit (7074) IgG were purchased from Cell Signaling Technology (Bioké, Leiden, the Netherlands). Antibodies to syntenin (ab19903) and specific for human syntenin (ab133267) were obtained from Abcam (Cambridge, MA, USA), and to tsg101 (sc‐7964) and CD81 (sc‐166029) from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Since C‐terminal fragments represent the major form of syndecan‐1 present in sEVs (SDC1‐CTF), a homemade mouse monoclonal antibody directed against the intracellular domain (ICD) of syndecan 1/3 (2E9) was used as described before. 17

Small EVs isolation

The serum used for sEVs purification experiments was depleted of all EVs, by centrifugation at 120,000 × g for 18 h. Small EVs were purified from cell culture supernatant by sequential centrifugation. Briefly, the individual supernatants from cells were serially centrifuged at 2000 × g for 10 min, and 10,000 × g for 30 min. Then, the supernatants were filtered using a 0.22‐µm syringe filter and centrifuged at 120,000 × g (Beckman Type SW28 or SW41 Ti) for 90 min. The pellet was resuspended and washed in PBS using ultracentrifugation at 120,000 × g for 90 min, at 4°C in a SW41 Ti rotor (Beckman Coulter). The small EVs pellet was resuspended in PBS and stored at −80°C until further use.

Human plasma‐derived small EVs isolation

Age‐matched fresh peripheral blood from healthy individuals and myeloma patients was obtained from the UZ Brussel. An informed consent form was obtained before sample collection (B.U.N. 143201838404). Peripheral blood was collected and centrifuged at 1000 × g for 10 min, and the supernatant (plasma) was collected and stored at −80°C. Approximately 1 mL of plasma was centrifuged at 4°C at 500 × g for 5 min, and 2000 × g for 10 min, and filtered in a 0.22‐μm membrane, followed by ultracentrifugation at 120,000 × g for 3 h at 4°C in a SW41 Ti rotor (Beckman Coulter). The sEVs pellet was resuspended in PBS and stored at −80°C until further use.

Transmission electron microscopy and nanoparticle tracking analysis

Purified sEVs were dripped onto a Formvar‐carbon‐coated grid for 30 min; the excess sEVs were carefully absorbed by a filter paper. Then grids were negatively stained with 1% uranyl acetate for 5 min and dried at room temperature for 15 min. Images were visualized and captured on a TECNAI 10 transmission electron microscope (Philips) at 80 kV by using iTEM software (Olympus). Nanoparticle concentration and size distribution were measured based on a Zetaview Nanoparticle Tracking Analyzer (Particle Metrix GmbH, Meerbusch, Germany).

Western blotting

Cells were lysed in lysis buffer containing protease and phosphatase inhibitors, while sEVs were resuspended in PBS. Next, the samples were mixed with 2X Laemmli Sample Buffer (Bio‐Rad) and boiled at 95°C for 5 min. Finally, they were separated in sodium dodecyl sulfate‐polyacrylamide gels and transferred to Immuno‐Blot PVDF Membranes (Bio‐Rad). Membranes were blocked for 1 h in 5% nonfat milk and incubated with primary antibodies overnight and subsequently HRP‐conjugated anti‐rabbit IgG (Cell Signaling Technology), or anti‐mouse IgG (Cell Signaling Technology) secondary antibodies for 1 h. The bands were visualized and captured using West Pico PLUS Chemiluminescent Substrate (Thermo Scientific) and Li‐Cor Odyssey Fc (Bad Homburg, Germany), respectively. The relative signals of proteins were quantified using Image Studio Ver 5.2 or ImageJ.

Cell viability and cell apoptosis

For conditioned medium (CM) assays 1.5 × 106 MS‐5 and HS‐5 cells were seeded in 10‐cm dishes, cultured overnight, and changed to medium supplemented with 5% sEVs‐depleted FBS for another 24 h. The resulting supernatant was collected and used for further experiments. MM cell lines were incubated with or without CM at a concentration of 20 × 104 cells/mL with or without bortezomib for 24 and 48 h. The viability was measured with the Cell Titer Glo Luminescent Viability assay (Promega, Madison, WI, USA). For apoptosis analysis, the myeloma cells were washed with binding buffer and stained with Annexin V‐APC (BD Biosciences, Erembodegem, Belgium) and 7‐aminoactinomycin D (BD Biosciences, Erembodegem, Belgium), and apoptotic cells were determined using an Accuri C6 plus flow cytometer (BD, USA).

Treatment of primary BM cells with SyntOFF

BM samples were collected, after informed consent was obtained, from newly diagnosed patients (BUN 143 201 838 414). Their characteristics are mentioned in Supporting Information S1: Table 1. BM samples were separated by density gradient centrifugation with Lymphoprep™ (STEMCELL™ Technologies, Grenoble, France). CD138+ BM cell fractions were isolated using magnetic activated cell sorting using human CD138 MicroBeads (Miltenyi Biotec, Gladbach, Germany). CD138− and CD138+ cells were mixed (in 1:1 or 2:1) and treated with SyntOFF (50 or 100 µm) alone or combined with bortezomib for 24 h. BM cells were stained with CD138‐APC (130‐091‐250) (Miltenyi Biotec, Gladbach, Germany) and detected by flow cytometry.

IL‐6 ELISA detection

BMSCs (HS‐5 or MS‐5; 1.5 × 10⁶) were cultured in medium supplemented with 5% sEV‐depleted FBS for 24 h. The CM was collected and stored at −20°C. SEVs were isolated from approximately 40 mL of CM through two rounds of ultracentrifugation. Equal amounts of sEVs (about 109 particles, as quantified by NTA) were used for sEVs‐derived IL‐6 detection. Human and mouse IL‐6 levels were measured using the Human IL‐6 Quantikine ELISA Kit or the Mouse IL‐6 Quantikine ELISA Kit, respectively (R&D Systems).

Proteomic analysis

Parental HS5 cells and SyntKO HS5 cells were used for the analysis. Each condition was tested in five biological replicates. On Day 0, 2.5 × 10⁶ cells were seeded in 10‐cm culture dishes to reach approximately 60%–70% confluence. On Day 1, cells were washed multiple times with serum‐free RPMI 1640 medium. Cells were then incubated in serum‐free RPMI 1640 medium for 24 h. On Day 2, culture supernatants were collected and centrifuged at 1200 × g for 20 min at 4°C. The resulting cleared supernatants were stored at −80°C before mass spectrometry analysis. The resulting cleared supernatants were further dialyzed and concentrated against 50 mM ammonium bicarbonate using Amicon4 Ultra, 3Kd MWCO (Millipore). Secretome extracts were dried using a SpeedVac vacuum concentrator, and each protein extract was stacked on NuPAGE™ in a single band to perform trypsin digestion before mass spectrometry analysis using an Orbitrap Fusion Lumos Tribrid (ThermoFisher Scientific, San Jose, CA) in data‐independent acquisition (DIA) mode. Protein identification and quantification were processed using the DIA‐NN 1.8 algorithm 27 and DIAgui package (https://github.com/marseille-proteomique/DIAgui). 28 See Supporting Information S1: Supplemental Methods for a detailed description. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 29 partner repository with the dataset identifier PXD064458. The statistical analysis was done with the Perseus program (version 1.6.15.0). 30 Differential proteins were detected using a two‐sample t‐test at 0.05 permutation‐based false discovery rate. Statistical analysis was performed using the standard two‐tailed Student's t‐test, and P‐value < 0.05 was considered significant. Volcano plots were created using Prism software.

Animal experiments

All animal experiments were approved by the ethics committees of the Vrije Universiteit Brussel (23‐281‐7). C57BL/KaLwRij mice were purchased from Envigo (Horst, the Netherlands). 5T33MM mouse model was generated and described before. 31 For the 5TGM1 mouse model, 6–8‐week‐old female immunocompetent C57BL/KaLwRij mice were inoculated via tail vein injection with 1.0 × 106 5TGM1‐eGFP cells. Mice were randomized into four groups, intraperitoneally injected with bortezomib (0.6 mg/kg, two times a week) alone or in combination with SyntOFF (50 mg/kg) five times a week. Vehicle‐treated mice served as controls. The treatment was terminated when the first mouse developed paralysis, and all mice were sacrificed. Tumor burden was evaluated by the percentage of 5TGM1 eGFP‐positive cells in the BM and M‐protein level in the plasma.

Statistical analysis

Statistical analyses were performed using GraphPad Prism 9.0 software (CA, USA) with one‐tailed unpaired Student's t‐tests for the comparison of two groups and one‐way analysis of variance (ANOVA) for the comparison of multiple groups. Results are shown as mean ± standard deviation (SD). P ≤ 0.05 was considered statistically significant.

RESULTS

Syntenin (SDCBP) is expressed in both BMSCs and MM cells, and its expression correlates with poor survival in MM patients

To explore the importance of syntenin expression in an MM context, we first examined whether its expression in patients correlates with prognosis at different stages of disease development in both the TT2 (GSE2658) and the CoMMpass dataset. In the TT2 dataset, patients with Stage II and Stage III MM disease have significantly worse progression‐free survival and overall survival when syntenin messenger RNA (mRNA) expression in BM plasma cells is high (Figure 1A,B). In contrast, in Stage I, an inverse correlation was seen (Supporting Information S1: Figure 1A). In the CoMMpass dataset, high SDCBP expression tended to correlate with worse patient survival under proteasome inhibitor‐containing treatment (Supporting Information S1: Figure 1B). Since syntenin is involved in sEV biogenesis, we next evaluated whether levels of circulating syntenin were increased in MM patients compared to normal subjects. Plasma‐derived sEVs were isolated from nine healthy donors and nine MM patients. Compared to healthy donors, more syntenin was detected in MM patients, while Flotillin‐1, another EV marker, remained unchanged (Figure 1C,D). The size, number, and total protein concentration of plasma sEVs showed no difference between normal subjects and MM patients (Supporting Information S1: Figure 1C). To further evaluate which cell types in the BM had the highest syntenin expression, we analyzed SDCBP levels in our scRNAseq dataset, comparing MM patients at diagnosis with healthy subjects. As previously reported, 24 an inflammatory MSC type appears in MM patients. We now found that these cells have higher SDCBP levels, compared to noninflammatory MSCs and the MM cells themselves (Figure 1E,F, Supporting Information S1: Figure 1D). Moreover, these inflammtory mesenchymal stromal cells (iMSCs) have higher IL‐6 levels (Figure 1F). These findings were further corroborated by WB data in preclinical models, comparing syntenin levels in different stromal and MM cell lines. We found a markedly high syntenin expression in both MS‐5 and HS‐5 stromal cells, compared to the MM cells 5TGM1, 5T33MMvt, XG2, RPMI8226, OPM2, LP1, and JJN3 (Figure 1G). Finally, we confirmed these results in an in vivo mouse model, whereby total BM cells were isolated from naïve and late‐stage (paralyzed) 5T33MM mice, containing 70% tumor cells. Western blot analysis showed that more syntenin and its direct interactor syndecan‐1 C‐terminal fragment (SDC1‐CTF) were expressed in the 5T33MM BM (Figure 1H). These data indicate that syntenin is highly expressed in the MM BM environment and is an indicator of poor prognosis.

Figure 1.

Figure 1

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Syntenin (SDCBP) expression is linked to survival in multiple myeloma (MM) patients. (A, B) Kaplan–Meier analysis of event‐free and overall survival of Stage II (n = 84) (A) and Stage III (n = 73) (B) MM patients. Figures generated through GenomicScape. GenomicScape ID: GS‐DT‐48. (C, D) Syntenin and Flotillin‐1 expression in plasma small extracellular vesicles (sEVs), detected by western blot and quantified using Image Studio Ver 5.2 software. Ponceau staining served as a loading control. HD: healthy donors (n = 9), MM: multiple myeloma patients (n = 9). (E) UMAP of mesenchymal stromal cells (MSCs) from healthy and MM patients showing inflammatory and noninflammatory MSC clusters. UMAP of the combined dataset of 19,983 stromal cells from 13 individuals with myeloma and 7038 cells from 7 non‐cancer control individuals. (F) Dot plot of MSC as mentioned in panel (E) and 32,412 CD38+SDC1+ myeloma cells of 13 myeloma patients. (G) Syntenin and SDC1‐CTF expression in BMSCs (MS‐5 and HS‐5) and MM cells (5TGM1, 5T33MMvt, XG2, RPMI8226, OPM2, LP1, and JJN3) were detected by western blot (n = 3). (H) Syntenin and SDC1‐CTF expression were detected in total bone marrow (BM) cells from four naïve and four late‐stage 5T33MM mice. Syntenin and SDC1‐CTF expression in mouse BM cells was quantified using Image Studio Ver 5.2 software. Error bar indicates mean ± SD; ns, not significant; **P < 0.01. EFS, event‐free survival; HR, hazard ratio; iMSC, inflammatory mesenchymal stromal cell; mRNA, messenger RNA; OS, overall survival.

SyntKO attenuates BMSCs‐induced bortezomib resistance of MM cells

Since BMSCs have high levels of syntenin, we next determined whether syntenin controls the release of an MM supportive BMSC secretome. We therefore investigated the impact of syntenin depletion in BMSCs on bortezomib (BTZ) resistance in MM cells. We constructed SyntKO HS‐5 and MS‐5 cells by CRISPR/Cas9 technology. Western blot analysis confirmed the lack of syntenin expression in SyntKO cells (see further Figure 4C,E). SyntKO did not impact BMSC growth for up to 2 days in culture (Supporting Information S1: Figure 2A,B). Murine 5TGM1 and 5T33MMvt cells were cultured with CM derived from wild type (WT) or SyntKO BMSCs and their chemosensitivity to BTZ was evaluated (Figure 2A and Supporting Information S1: Figure 3A). Without BTZ treatment, MS‐5 WT and SyntKO CM showed no significant effect on 5TGM1 and 5T33MMvt cell viability. However, under BTZ treatment (3.5, 4, or 5 nM), MS‐5 WT CM significantly increased 5TGM1 and 5T33MMvt cell viability at 24 and 48 h. At 48 h, SyntKO reduced the protective effect of MS‐5 CM in all conditions by up to 50% (Figure 2A and Supporting Information S1: Figure 3A). In apoptosis assays, neither MS‐5 WT CM nor SyntKO CM enhanced the proportion of live cells in the absence of BTZ. However, with BTZ, MS‐5 WT CM increased 5TGM1 live cells by 10% and 5T33MMvt by 20%. In contrast, when syntenin was depleted, the MS‐5 CM had limited effect (Figure 2B and Supporting Information S1: Figure 3B). Similar results were obtained with human XG2 cells cultured for 48 h with HS‐5 WT and SyntKO CM (Figure 2C,D). These findings suggest that BMSC syntenin supports the BTZ resistance of MM cells that is induced by exposure to BMSC‐derived CM.

Figure 4.

Figure 4

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Syntenin inhibition alters the bone marrow stromal cell secretome. (A) Interleukin (IL)‐6 was detectable in HS‐5, wild type (WT), and syntenin‐knockout (SyntKO) conditioned medium (CM); medium supplemented with 5% small extracellular vesicles (sEVs)‐depleted serum was used as a negative control (n = 3). (B) Volcano plot illustrating the proteins that are differentially expressed, with log2 levels (x‐axis) and –log (P‐value) (y‐axis), in the conditioned medium (without fetal bovine serum [FBS]) isolated from HS‐5 SyntKO versus HS‐5 WT cells (see dataset PXD064458 for raw data). sEVs‐related proteins are represented in blue, MM‐related proteins are represented in orange, and acute myeloid leukemia (AML)‐related proteins are in red. (C–F) The expression of syntenin and other sEVs‐related proteins in WT and SyntKO HS‐5 (C) and MS‐5 (E) cells and their sEVs as detected by western blot. The relative expression of syntenin and other sEVs‐related proteins in WT and SyntKO HS‐5 (D) and MS‐5 (F) derived sEVs was quantified using ImageJ (n = 3). (G, H) sEVs were isolated from HS‐5 (G) (n = 3) and MS‐5 (H) (n = 4) after treatment with SyntOFF (50, 100 μM) for 24 h after which sEV marker expression was detected by western blot. An equal number of sEVs were loaded for western blot analysis. GAPDH served as a loading control. The relative expression of syntenin and other sEV‐related proteins in sEVs was quantified using ImageJ. Error bar indicates mean ± SD. ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 2.

Figure 2

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Syntenin‐knockout (SyntKO) attenuates bone marrow stromal cells effects on bortezomib (BTZ) resistance of multiple myeloma cells. (A, C) 5TGM1 and XG2 cells were incubated with conditioned medium (CM) from wild type (WT) or SyntKO MS‐5/HS‐5 cells, or with 5% small extracellular vesicles (sEVs)‐depleted fetal bovine serum (FBS) medium, and treated with or without indicated concentrations of bortezomib, for 24 and 48 h, after which cell viability was measured, using a luminescent cell viability assay. 5TGM1 cells (n = 5); XG2 cells (n = 4). (B, D) Apoptotic cells were measured by 7‐AAD and Annexin‐V staining and flow cytometry. Representative flow cytometry plots are shown. The proportions of live, early apoptotic or late apoptotic and dead cells are represented by a stacked bar chart. The stars above the bars indicate significant differences in live cell population between 5% sEVs‐depleted FBS medium and SyntKO CM with or without BTZ. The stars inside the bars indicate differences compared to WT CM. 5TGM1 cells (n = 5); XG2 cells (n = 4). Error bars indicate mean ± SD; ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. DMSO, dimethyl sulfoxide.

SyntKO affects BMSC‐induced STAT3, MAPK, and protein kinase B‐mTOR signaling pathways in MM cells

To further understand how SyntKO in BMSCs may affect BTZ sensitivity in MM cells, MM cells were incubated in 5% sEVs‐depleted FBS medium, or in BMSC (WT/SyntKO) CM, with or without BTZ, after which key growth and survival pathways for MM cells were analyzed. After 24 h of incubation, WT BMSC CM increased the phosphorylation of STAT3 in 5TGM1, 5T33MMvt, and XG2 cells. SyntKO in BMSCs inhibited the activation of STAT3 in these cells, in the presence but also the absence of BTZ (Figure 3A and Supporting Information S1: Figure 4A).

Figure 3.

Figure 3

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Syntenin‐knockout (SyntKO) affects bone marrow stromal cells‐induced STAT3, MAPK, and protein kinase B (AKT)‐mammalian target of rapamycin (mTOR) signaling pathway activation in multiple myeloma cells. (A–D) 5TGM1 cells were treated with 5% small extracellular vesicles (sEVs)‐depleted fetal bovine serum (FBS) medium, MS‐5 wild type (WT) conditioned medium (CM), or MS‐5 SyntKO CM with or without bortezomib (BTZ; 3.5 nM) for 24 h. XG2 cells were treated with 5% sEVs‐depleted FBS medium, HS‐5 WT CM, and HS‐5 SyntKO CM with or without BTZ (3.5 nM) for 24 h. Expressions of (A) total and phosphorylated STAT3, p44/42, p38, and SAPK/JNK; (C) AKT, p70S6K, and 4E‐BP1 in 5TGM1 and XG2 cells were determined using western blot analysis and quantified by ImageJ. Error bar indicates mean ± SD; *P < 0.05.

SyntKO in BMSCs also countered the CM‐induced phosphorylation of the growth‐promoting p44/42 MAPK pathway in 5TGM1 and XG2 cells, while increasing phosphorylation of p38 MAPK in 5TGM1 and XG2 cells and SAPK/JNK MAPK in 5TGM1 cells under BTZ pressure (Figure 3A,B and Supporting Information S1: Figure 4A). On the other hand, SyntKO in BMSCs did not impact the phosphorylation of p53, c‐jun, and c‐Myc in 5TGM1, 5T33MMvt, and XG2 cells (Supporting Information S1: Figure 4B,C).

The phosphoinositide 3‐kinase (PI3K)/AKT/mTOR pathway is aberrantly activated in a large proportion of MM patients due to BM‐derived signals. 32 , 33 Interestingly, SyntKO in BMSCs reduced the phosphorylation of AKT in 5TGM1, 5T33MMvt, and XG2 cells and inhibited the phosphorylation of mTOR‐related proteins p‐70S6K in 5TGM1 and 5T33MMvt cells and 4E‐BP1 in XG2 cells under BTZ treatment (Figure 3C,D and Supporting Information S1: Figure 4A). These results indicate that syntenin in BMSCs regulates STAT3, MAPK, and AKT‐mTOR survival pathways in MM cells.

Inhibition of syntenin alters the secretory profile of BMSCs and the molecular composition of sEVs

To identify what could be altered in the BMSC secretome that could explain the effect on MM cells, we first focused on IL‐6 expression since it was upregulated in iMSC and it is a known growth factor for MM cells, which activates the JAK‐STAT signaling pathway. We measured IL‐6 in CM of both WT and SyntKO cells. Surprisingly, IL‐6 was not detected in the CM from MS‐5 cells, but it was highly abundant in HS‐5 cell‐derived CM. Intriguingly, a 50% reduction of IL‐6 was seen in CM of SyntKO cells, indicating that IL‐6 release is partially governed by syntenin (Figure 4A and Supporting Information S1: Figure 5A). Next, we analyzed the secretomes of WT and SyntKO HS‐5 cells by mass spectrometry. In total, 406 proteins were downregulated (with a threshold of 0.8 log difference) and 208 proteins were upregulated. Of the top 100 proteins that were downregulated, we checked which factors were known to be involved in MM or acute myeloid leukemia (AML, another hematological cancer growing in the BM). Potential candidates that were downregulated in the secretome of SyntKO HS‐5 cells included VEGFR1, EPHA4, IGF2BP1, and several MMPs (Figure 4B). However, none of these are key growth factors in MM.

Since sEVs are a component of BMSC‐derived CM, and syntenin plays a key role in sEV biology, we next assessed the effect of SyntKO on the composition of sEV, using EVs isolated from the CM of WT and SyntKO HS‐5 and MS‐5 cells by ultracentrifugation. As previously observed, 34 the shape and size of the particles released were comparable between WT and SyntKO HS‐5 and MS‐5 cells (Supporting Information S1: Figure 5B–D). Nanoparticle tracking analysis revealed a 15% reduction in particle number in HS‐5 SyntKO‐CM and a 40% reduction in MS‐5 SyntKO‐CM (Supporting Information S1: Figure 5E). When counting the particle number on isolated sEV fractions, no change was observed with HS‐5 SyntKO cells, but a 50% reduction was observed with MS‐5 SyntKO cells (Supporting Information S1: Figure 5F). Next, we analyzed whether SyntKO altered the composition of the sEVs in terms of biogenesis markers. MS‐5 SyntKO cells secreted sEVs showing a significant decrease in SDC1‐CTF, while in HS‐5 SyntKO sEVs, these were unaffected. HS‐5 SyntKO sEVs contained less Alix, HSP70, and TSG101, whereas MS‐5 SyntKO sEVs showed reductions in Alix, HSP70, and CD81. Rab27a was highly detectable in WT and SyntKO sEVs from both cell types, suggesting a syntenin‐independent sorting to sEVs. Rab11a levels significantly decreased in SyntKO sEVs, whereas Rab7 remained unaffected. These findings indicate that syntenin controls a Rab11a‐dependent sEV secretion route, more pronounced in MS‐5 cells. Calreticulin, a negative control, was absent in all sEVs (Figure 4C–F). These data were confirmed by proteomic analysis of the secretome of SyntKO HS‐5 cells as indicated in Figure 4B (blue dots).

We further examined the content of the sEVs to evaluate whether their cargoes are responsible for the effects seen with CM. We analyzed IL‐6 levels in the sEVs and found, similar to CM, that IL‐6 expression, albeit lower, was reduced by half in SyntKO HS‐5 sEVs (Supporting Information S1: Figure 5G). Since syntenin itself has been shown to activate STAT3, 15 we evaluated whether MM cells, treated for 4 h with either concentrated CM or sEVs from SyntKO HS‐5 or MS‐5 cells, would have altered syntenin levels. Syntenin was increased in 5TGM1 (Supporting Information S1: Figure 6A), 5T33MMvt (Supporting Information S1: Figure 6B), and XG2 (Supporting Information S1: Figure 6C) cells treated with WT sEVs, whereas no changes were observed with SyntKO sEVs. We next analyzed whether syntenin may be transferred from BMSC CM to MM cells. Therefore, we treated mouse 5TGM1 cells with concentrated CM of human HS‐5 WT cells and traced syntenin expression with an antibody specific for human syntenin. Thereby, we observed that syntenin can be transferred via sEVs (Supporting Information S1: Figure 6D).

To demonstrate that altered sEV cargo is responsible for the reduced pro‐tumoral effects of SyntKO stromal cells, we next treated 5TGM1 cells with similar amounts of sEVs (according to NTA) from MS‐5 WT or SyntKO cells. SyntKO sEVs failed to substantially protect MM cells from BTZ, whereas WT sEVs induced a 40% increase in viability (Supporting Information S1: Figure 6E). These data indicate that sEV cargo alteration in syntenin KO BMSCs reduces their pro‐survival effects on MM cells.

Therefore, we evaluated whether pharmacological inhibition of syntenin using the small molecule inhibitor SyntOFF would affect sEV secretion by stromal cells. SyntOFF was minimally toxic at concentrations up to 100 µM, in both HS‐5 and MS‐5 cells (Supporting Information S1: Figure 7A,B). Moreover, SyntOFF did not affect the cellular expression of syntenin, nor that of other sEV markers (Supporting Information S1: Figure 7C). SyntOFF (50 and 100 µM) also did not affect the number or the size of nanoparticles secreted from HS‐5 and MS‐5 cells (Supporting Information S1: Figure 7D), but it did influence marker protein expressions in sEVs. Indeed, western blot analysis revealed that the amounts of syntenin and Alix were significantly decreased in the sEV isolates when HS‐5 and MS‐5 cells were treated with 100 µM SyntOFF, whereas SDC‐CTF, HSP70, Flotillin‐1, and CD81 levels remained unaffected (Figure 4G,H). Syntenin reduction was also observed in sEVs from human primary BMSC treated with SyntOFF (2/3, Supporting Information S1: Figure 7E). Taken together, our data indicate that SyntOFF can regulate the loading of BMSC‐derived sEVs in vitro.

SyntOFF enhances the therapeutic efficacy of bortezomib against MM in an ex vivo BM microenvironment

Next, we evaluated whether SyntOFF could enhance MM sensitivity towards BTZ in the presence of BM cells. We first isolated BM cells from 5T33MM diseased mice having a tumor load of approximately 70%–80% MM cells, as detected by flow cytometry (Figure 5A). SyntOFF was found to decrease the viability of these BM cells at 2.5 nM bortezomib, but not when administered alone (Figure 5B). Additionally, apoptosis assays showed that SyntOFF reduces the percentage of viable cells (by nearly 17 units at 100 μM) when combined with BTZ (Figure 5C). Next, BM cells were isolated from MM patients. CD138‐negative (non‐tumor) BM cells were cocultured with CD138‐positive (tumor) cells, and SyntOFF was added for 48 hours. Under BTZ treatment, the number of cells expressing CD138 (used as a tumoral marker) was significantly decreased, as expected. Noteworthy, SyntOFF further reduced CD138‐positive cells, indicating an additional effect (Figure 5D). To determine whether SyntOFF has direct antitumor effects on the MM cells, we treated 5T33MMvt and RPMI8226 cells with 50 or 100 µM SyntOFF. We found that, at the highest concentration, SyntOFF enhances BTZ effects on viability (up to 50%) and on apoptosis (up to 40%), indicating that SyntOFF also has direct antitumor effects (Supporting Information S1: Figure 8A–D).

Figure 5.

Figure 5

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SyntOFF enhances the therapeutic efficacy of bortezomib (BTZ) against multiple myeloma in an ex vivo bone marrow microenvironment. (A–C) 5T33MMvv cells were isolated from 5T33MM mice (n = 4) and tumor load was detected by 3H2 staining (A). Cells were then treated with SyntOFF (50, 100 μM) for 24 h combined with or without BTZ (2.5 nM), cell viability was measured using a luminescent cell viability assay (B), and cell apoptosis (C) was detected by flow cytometry. Representative flow cytometry plots are shown. The proportions of live, early apoptotic or late apoptotic and dead cells were analyzed and represented by a stacked bar chart. The symbols above the bars compare live cell proportions in populations treated with 0 and 100 µM SyntOFF. (D) Bone marrow cells were isolated from myeloma patients, CD138‐positive cells were cocultured with CD138‐negative cells and incubated with SyntOFF (100 μM) for 48 h together with BTZ (5 nM). Next, cells were labeled with CD138 for flow cytometry analysis. Mann–Whitney test, error bar indicates mean ± SD. *P < 0.05 and **P < 0.01. BM, bone marrow; DMSO, dimethyl sulfoxide; MM, multiple myeloma.

Taken together, our data indicate that SyntOFF is capable of enhancing the therapeutic effect of BTZ, acting directly on tumor cells and counteracting the protective BM environment.

SyntOFF combined with bortezomib decreases tumor load in vivo

Finally, to investigate whether syntenin pharmacological inhibition may help to control MM in vivo, we tested SyntOFF in the preclinical 5TGM1 mouse model. The treatment scheme is provided in Figure 6A. We analyzed tumor burden in both BM and spleen, and plasma M spike levels as a measure of tumor volume. SyntOFF alone had no significant impact on tumor volume by any parameter. SyntOFF combined with BTZ, however, significantly reduced M‐protein levels in plasma compared to vehicle and single treatments (Figure 6B). Of note, while at the concentration used, BTZ as a single agent was already highly effective in lowering the tumor burden in both BM and spleen, SyntOFF further enhanced BTZ effect, almost eradicating the tumor completely (Figure 6C,D). Furthermore, a decrease in liver weight was observed in the combination treatment group (Supporting Information S1: Figure 9A), whereas SyntOFF had no additional effect on spleen weight compared to BTZ alone (Supporting Information S1: Figure 9B). These in vivo results indicate that SyntOFF may be of added value in combination with BTZ for the treatment of MM. A schematic depicting how syntenin inhibition might impair BMSCs‐MM communication and improve bortezomib treatment efficiency is shown in Figure 6E.

Figure 6.

Figure 6

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SyntOFF combined with bortezomib (BTZ) decreases M‐protein levels in plasma and tumor load in mice. (A) Overview of the design of the mouse experiment. Vehicle = 8 mice, SyntOFF = 9 mice, BTZ = 6 mice, and Combo = 9 mice. (B) M‐protein levels in the plasma were measured. (C, D) Bone marrow (BM) and spleen cells were isolated from 5TGM1 mouse model mice, and tumor load was detected by flow cytometry. Representative plots are shown. Error bar indicates mean ± SD. *P < 0.05, ***P < 0.001, and ****P < 0.0001. (E) Schematic illustrating that syntenin expression is high in iBMSCs, compared to multiple myeloma (MM) cells. The secretome of bone marrow stromal cells (BMSCs), which includes small extracellular vesicles (sEVs) and interleukin (IL)‐6, promotes MM growth and bortezomib resistance through activation of the mammalian target of rapamycin (mTOR), STAT3, and MEK/ERK pathways. Inhibiting syntenin in BMSCs can reverse these effects; created with BioRender.com. iBMSC, inflammatory bone marrow stromal cell.

DISCUSSION

We have shown previously that sEVs shuttle between BMSCs and MM cells, carrying proteins, microRNA, and other biological molecules that support MM growth, DR, and immunosuppression. 8 , 9 , 35 , 36 , 37 Syntenin is a protein that has broad effects on cell‐to‐cell communication and EV‐dependent signaling in particular. 17 , 19 , 25 , 34 , 38 Therefore, targeting syntenin could compromise such pro‐tumoral intercellular communication.

To determine whether syntenin would indeed be an interesting target in MM, we first analyzed different public datasets and identified a correlation between syntenin mRNA expression in myeloma cells and worse progression‐free and overall survival in patients with advanced MM. Remarkably, this was not the case in Stage I myeloma. Moreover, syntenin was only expressed at low levels in MM cells of newly diagnosed patients, with relatively higher expressions in the surrounding cells. This indicates that when tumor burden is low, MM syntenin mainly supports homeostasis and/or tumor‐suppressive responses in the BM environment. However, when tumor burden is high, MM syntenin‐supported processes such as angiogenesis 39 could become pro‐tumoral. Additionally, we observed elevated levels of syntenin in sEVs derived from the plasma of myeloma patients in contrast to normal subjects. Although we cannot exclude contaminating platelet‐derived sEVs, our data suggest that plasma syntenin is a good indicator of circulating MM sEVs, in contrast to Flotillin‐1, which remained unchanged. Meanwhile, when comparing MM patients to healthy individuals, we found that the MSC compartment becomes more inflammatory and that these iMSCs express more syntenin (potentially contributing to circulating syntenin). This is perhaps not surprising since the inflammatory cytokines interferon and tumor necrosis factor alpha can upregulate syntenin in melanoma cells. 40 Finally, syntenin and SDC1‐CTFs were also expressed at higher levels in the BM of MM diseased mice. Globally, the data suggest a potential role for syntenin in the progression of MM from Stage II on and in iMSC in particular. Moreover, syntenin may serve as a novel prognostic marker to differentiate low‐stage to high‐stage myeloma patients.

When examining BMSC cell lines, we found that these express more syntenin than MM cell lines. To evaluate the role of syntenin expression within the BM environment, we generated SyntKO BMSC and analyzed their impact on MM viability and apoptosis. Although we cannot exclude off‐target effects in the SyntKO cells, we noted that SyntKO attenuated BMSC‐induced BTZ resistance in both murine and human MM cell lines. To understand the mechanisms behind the syntenin‐mediated effects of BMSC, we further examined the impact of BMSC CM on multiple pathways that have been implicated in MM pro‐tumoral processes, including survival, protein synthesis, and DR. 33 , 41 , 42 , 43 We confirmed that BMSC CM was able to trigger STAT3 signaling in MM cells. By contrast, SyntKO in BMSC led to a reduction in STAT3 activation and inhibition of AKT, while also slightly decreasing the p44/42 MAPK, p70S6K, and 4E‐BP1 pathways, all of which have been implicated in MM DR. 43 , 44

To clarify which syntenin‐dependent cargo could explain the activation of pro‐survival pathways, we first focused on IL‐6, since iMSCs express higher levels of this cytokine, and IL‐6 can activate STAT3. In HS‐5 cells, IL‐6 levels were lower in CM from SyntKO cells. While IL‐6 is also a known pro‐survival factor for murine MM cells, we detected no IL‐6 secretion from MS‐5 cells. To get a broader view on the secretome controlled by syntenin, we scrutinized proteomics data, comparing the secretome of WT and SyntKO HS‐5 cells. Among the top 100 hits, we identified several factors that have been associated with either MM or AML progression. These included MMPs 1, 3, and 10, 45 receptors such as VEGFR1 46 and EPHA4 47 and several intracellular factors such as ACSL1, 48 CREBBP, 49 and SEC. 61B. 50 These data further underscore that specific integral and both internal and external peripherally membrane‐associated proteins can be secreted via syntenin. To evaluate the impact of the exposure or transfer of each of these different proteins to MM cells falls outside the scope of this paper. Syntenin itself also has been shown to regulate the expression of the STAT3 pathway in prostate and breast cancer, thereby regulating multiple DR and stemness. 15 , 51 Moreover, inhibition of syntenin reduced breast cancer metastasis in syngeneic animals by inhibiting tumor cell‐derived IL‐1β secretion through deactivation of STAT3 and reducing infiltration of immune suppressor cells in the metastatic niche. 51 Therefore, we wondered whether syntenin might be transferred from syntenin‐expressing BMSC to MM cells. Indeed, we found an increase of syntenin of human origin in murine MM cells, treated with human BMSC CM, which was not observed when BMSC were SyntKO.

To further evaluate whether sEV contributed to the CM effects, we investigated sEV biogenesis in BMSCs knockout cells. A reduction in the number of sEVs was observed as well as in the levels of syntenin, Alix, and HSP70 in sEVs, consistent with our previous reports. 17 Moreover, CD81 and SDC1‐CTF (both composing sEV cargo, directly binding to syntenin) 17 , 20 , 52 were also downregulated in MS‐5 SyntKO sEVs, whereas in HS‐5, these were unaffected. CD81 and SDC1 have a role in MM pathogenesis and are linked to disease severity. 53 , 54 , 55 Thus, syntenin has pleiotropic effects on sEV compositions. Why syntenin regulates (specific) sEV (cargo) secretion differently in different cell types/species remains to be determined but is probably related to its ability to directly interact with various scaffold proteins implicated in EV biology and present at different concentrations in different cell types. 20 Modulation of the syntenin PDZ domain interactions via the signaling‐dependent differential phosphorylation of PDZ domain‐binding motifs in potential cargo proteins 56 exemplifies one of the other multiple possibilities.

To evaluate whether sEVs generated via the syntenin pathway are pro‐tumoral, we assessed their impact on DR of MM cells. We found that sEVs from SyntKO MS‐5 cells were not able to protect MM cells from BTZ, compared to sEVs from WT MS‐5 cells. Moreover, we also measured IL‐6 levels in SyntKO and WT sEVs and found that sEVs from WT HS‐5 cells did carry IL‐6, which was reduced by half by SyntKO. Which cargoes are responsible for the observed effects remains an open question and may include several key players. 23 For example, one paper suggests that syntenin sEVs contain the onco‐miR‐494‐3p, which promotes cell growth, migration, and angiogenesis in lung cancer cells 57 and could therefore also activate tumor supportive pathways. Whether there are differences in micro‐RNA (miRNA) content within SyntKO BMSC‐derived sEVs warrants further investigation.

Nevertheless, a huge body of evidence has illustrated that syntenin is an abundant protein in both cancer cells and sEVs. A few inhibitors are available that target the PDZ1 domain of syntenin 58 , 59 ; however, this domain plays a relatively minor role in the syntenin interaction with syndecans, 60 emerging as key players of sEV biology. 20 Therefore, we developed a syntenin inhibitor, SyntOFF, which specifically binds to the syntenin‐PDZ2 domain. 23 SyntOFF had some direct effects on MM cells, indicating that syntenin most likely also plays an (oncogenic) role in the MM cells themselves; for example, we have shown that (by modulating syndecan) syntenin is also involved in sEV uptake, 18 , 20 which has not been examined here. More importantly, in a BMSCs and MM coculture environment, SyntOFF significantly decreases cell viability and induces apoptosis consequent to BTZ exposure, in both primary murine and human patient samples.

Consistent with our previous findings, we observed that SyntOFF also affected the level of syntenin and Alix in secreted BMSC sEVs, indicating that SyntOFF can alter the type (or loading) of sEVs that are secreted, mimicking the KO effects. Furthermore, the SyntOFF‐BTZ combination significantly reduced tumor burden in the 5TGM1 mouse model. Noteworthy, SyntOFF outperforms the ceramide inhibitor GW4869 (used as an alternative blocker of sEV secretion) or endocytosis inhibitors in combination with BTZ, 37 , 61 suggesting that syntenin is a more pertinent target. It is possible that in vivo, SyntOFF also impacts other processes such as antitumor immunity, next to altering the BMSC secretome. For example, it has been shown in a model of colorectal cancer that targeting syntenin with zinc pyrithione repolarized M2 macrophages to M1, thereby enhancing immunotherapy using anti‐PD1 antibodies. 62 Thus, syntenin represents a novel and potentially more effective target to inhibit MM progression.

In conclusion, our study reveals the importance of syntenin for the MM protective effect of the BM environment. Moreover, it shows that the syntenin‐PDZ2 domain inhibitor SyntOFF modifies the BMSC secretome and their sEVs in particular, enhancing the anti‐myeloma effect of BTZ both in vivo and ex vivo using patient material. These findings provide valuable insights into the potential synergistic effects of using SyntOFF to overcome BTZ resistance in MM.

AUTHOR CONTRIBUTIONS

Chenggong Tu: Conceptualization; writing—original draft; investigation; formal analysis. Raphael Leblanc: Conceptualization; writing—original draft; investigation; formal analysis. Arne Van der Vreken: Investigation; formal analysis. Marnix Koops: Investigation; formal analysis. Stephane Audebert: Investigation; formal analysis. Lauriane Goullieux: Investigation; formal analysis. Sofie Meeussen: Investigation; formal analysis. Kim De Veirman: Writing—review and editing. Elke De Bruyne: Writing—review and editing. Karin Vanderkerken: Writing—review and editing. Guido David: Writing—review and editing. Tom Cupedo: Writing—review and editing. Pascale Zimmermann: Conceptualization; writing—original draft; supervision; funding acquisition. Eline Menu: Conceptualization; writing—original draft; supervision; funding acquisition.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

FUNDING

This research received financial support from the Vrije Universiteit Brussel under the strategic research program (SRP84), Kom op Tegen Kanker BE, and Fonds Willy Gepts UZ Brussels. C.T. was supported by a China Scholarship Council‐Vrije Universiteit Brussel scholarship. A.V.d.V. received funding from the Fonds Wetenschappelijk Onderzoek Vlaanderen (V435824N).

Supporting information

Supporting Information.

HEM3-9-e70197-s001.docx (3MB, docx)

Supporting Information.

HEM3-9-e70197-s002.xlsx (7.6MB, xlsx)

ACKNOWLEDGMENTS

The authors thank Carine Seynaeve and Charlotte Van De Walle for expert technical assistance.

Contributor Information

Pascale Zimmermann, Email: pascale.zimmermann@kuleuven.be.

Eline Menu, Email: eline.menu@vub.be.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in ProteomeXchange at https://www.proteomexchange.org/, reference number PXD064458.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information.

HEM3-9-e70197-s001.docx (3MB, docx)

Supporting Information.

HEM3-9-e70197-s002.xlsx (7.6MB, xlsx)

Data Availability Statement

The data that support the findings of this study are openly available in ProteomeXchange at https://www.proteomexchange.org/, reference number PXD064458.

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