Decision letter: Differentiated glioma cell-derived fibromodulin activates integrin-dependent Notch signaling in endothelial cells to promote tumor angiogenesis and growth

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Cancer stem cells (CSCs) alone can initiate and maintain tumors, but the function of non-cancer stem cells (non-CSCs) that form the tumor bulk remains poorly understood. Proteomic analysis showed a higher abundance of the extracellular matrix small leucine-rich proteoglycan fibromodulin (FMOD) in the conditioned medium of differentiated glioma cells (DGCs), the equivalent of glioma non-CSCs, compared to that of glioma stem-like cells (GSCs). DGCs silenced for FMOD fail to cooperate with co-implanted GSCs to promote tumor growth. FMOD downregulation neither affects GSC growth and differentiation nor DGC growth and reprogramming in vitro. DGC-secreted FMOD promotes angiogenesis by activating integrin-dependent Notch signaling in endothelial cells. Furthermore, conditional silencing of FMOD in newly generated DGCs in vivo inhibits the growth of GSC-initiated tumors due to poorly developed vasculature and increases mouse survival. Collectively, these findings demonstrate that DGC-secreted FMOD promotes glioma tumor angiogenesis and growth through paracrine signaling in endothelial cells and identifies a DGC-produced protein as a potential therapeutic target in glioma. Editor's evaluation The authors shed light on the role that differentiated glioma cells exerts in promoting cancer progression, revealing that the secreted fibromodulin by differentiated glioma cells is crucial in mediating angiogenesis in glioma via integrin-dependent Notch signaling. The results are important for gaining insight into the less concerned differentiated glioma cells in promoting cancer and would potentially enrich the treatment strategy for glioma. https://doi.org/10.7554/eLife.78972.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Tumors and their microenvironment form an ecosystem with many cell types that support tumor growth. The key constituents of this ecosystem include cancer stem cells (CSCs), non-cancer stem cells (non-CSCs) representing the differentiated cancer cells, and various other cell types present in the tumor stroma (Prager et al., 2019). It is well established that the tumor-initiating capacity lies solely with CSCs, thereby making them the crucial architects of tumor–stroma interactions that favor tumor growth and progression (Rheinbay et al., 2013). CSCs have a dichotomous division pattern as they are capable of self-renewal and give rise to differentiated cells that form the bulk of the tumor (Olmeda and Ben Amar, 2019). The indispensable role of CSCs, which usually constitute only a minority population within tumors, is well documented in many solid tumors (Galli et al., 2004; Ignatova et al., 2002; Singh et al., 2004; Yang et al., 2020a). The tumor microenvironment is a vital driver of plasticity and heterogeneity in cancer (Carnero and Lleonart, 2016; Heddleston et al., 2010). The presence of hypoxic and necrotic regions is the hallmark of very aggressive tumors like glioblastoma (GBM), which have a highly vascular niche that supplies nutrients to cancer cells and makes a conducive environment for the tumor cells to thrive (Hambardzumyan and Bergers, 2015; Huang et al., 2016). Paracrine signaling mediated by proteins secreted from tumor cells, particularly glioma stem-like cells (GSCs), helps acquire this highly vascular phenotype by attracting blood vessels and inducing pro-angiogenic signaling in endothelial cells through extracellular matrix (ECM) remodeling (Dittmer and Leyh, 2014; Rupp et al., 2016). A reciprocal relationship exists between GSCs and endothelial cells by which endothelial cells induce stemness phenotype in cancer cells through activation of Notch, sonic hedgehog (SHH), and nitric oxide synthase signaling pathways (Jeon et al., 2014; Yan et al., 2014; Zhu et al., 2011), while GSCs drive vascularization of the tumor via endogenous endothelial cell stimulation, vascular mimicry, and GBM-endothelial cell transdifferentiation (Hardee and Zagzag, 2012; Soda et al., 2011). Recent reports have shown that CSCs induce high vascularization of tumors like GBM by migrating along blood vessel scaffolds to invade novel vascular niches, thereby ensuring surplus and continuous blood supply at their disposal (Prager et al., 2020). In GBM, CD133+ and Nestin+ cells (representing GSCs) are located in close proximity to the tumor microvascular density (MVD), whereas a lower number of CD133- and Nestin- cells (representing differentiated glioma cells [DGCs]) are located in the vicinity of the blood vessels. It has also been reported that the depletion of brain tumor blood vessels causes a decrease in the number of tumor-initiating GSCs (Calabrese et al., 2007). While CD133 marker expression was reported to be associated with GSCs initially (Singh et al., 2004; Galli et al., 2004), later reports documented CD133- cells exhibiting GSC-like properties (Beier et al., 2007; Chen et al., 2010; Joo et al., 2008; Ogden et al., 2008; Wang et al., 2008). CXCR4-dependent SHH-GLI-NANOG signaling promotes stemness in GSCs. This study also showed that the miR302-367 cluster could suppress stemness and promote differentiation by targeting CXCR4/SDF1 (Fareh et al., 2012). The above group subsequently showed that miR18A* promotes GSC stemness by activating Notch-dependent SHH-GL1-NANOG signaling, targeting DLL3, an autocrine inhibitor of Notch 1 signaling (Turchi et al., 2013). In contrast to these observations, Dirkse et al. showed the existence of stem cell-associated heterogeneity in GBM, which results in tumor plasticity and is orchestrated by the microenvironment (Dirkse et al., 2019). Besides CSC self-renewal, their differentiation to form the bulk cancer cells also plays a crucial role in tumor growth and maintenance (Jin et al., 2017). Epigenome unique to CSCs compared to differentiated cancer cells has been documented (Suvà et al., 2014; Zhou et al., 2018). Reciprocally, a set of four reprogramming transcription factors, POU3F2, SOX2, SALL2, and OLIG2, is identified in GBM that is sufficient to reprogram DGCs and create the epigenetic landscape of native GSCs, thus creating ‘induced’ CSCs (Suvà et al., 2014). The epigenetic regulation forms the basis of cellular plasticity, which creates a dynamic equilibrium between CSCs and differentiated cancer cells (Safa et al., 2015). Oncogene-induced dedifferentiation of mature cells in the brain was also reported using a mouse model of glioma, and the reprogrammed CSCs were proposed to contribute to the heterogeneous cell state populations observed in malignant gliomas (Friedmann-Morvinski et al., 2012; Friedmann-Morvinski and Verma, 2014). Lineage-tracing analyses revealed the reprogramming of DGCs to GSCs that act as a reservoir for initiating relapse of the tumors upon temozolomide chemotherapy (Auffinger et al., 2014; Chen et al., 2012). Hypoxia has also been reported to reprogram differentiated cells to form CSCs in glioma, hepatoma, and lung cancer (Prasad et al., 2017; Wang et al., 2017). Spontaneous conversion of differentiated cancer cells to CSCs has also been reported in breast cancer (Klevebring et al., 2014; Zhou et al., 2019). Collectively, these studies highlight the crucial role of CSCs in cellular crosstalk in the tumor niche and establish CSCs as critical drivers of tumorigenesis. However, the massive imbalance in the proportions of CSCs and non-CSCs or differentiated cancer cells in tumors raises several important questions. Considering that differentiated cancer cells constitute the bulk of tumors, do they have specific functions, or do they only constitute the tumor mass? Do they contribute to the complex paracrine signaling occurring within the tumor microenvironment? Do they support tumor growth by promoting CSC growth and maintenance? It has been recently shown in GBM that DGCs cooperate with GSCs through a paracrine feedback loop involving neurotrophin signaling to promote tumor growth (Wang et al., 2018). While this study suggests a supporting role for differentiated cancer cells in tumor growth, the large proportion of them in tumors suggests a role in paracrine interactions with other stromal cells in the tumor niche. We used quantitative proteomics to identify DGC-secreted proteins that might support their paracrine interactions within the tumor microenvironment. We show an essential role of fibromodulin (FMOD) secreted by DGCs in promoting tumor angiogenesis via a crosstalk with endothelial cells. FMOD promotes integrin-dependent Notch signaling in endothelial cells to enhance their migratory and blood vessel-forming capacity. These findings indicate that DGCs are crucial for supporting tumor growth in the complex tumor microenvironment by promoting multifaceted interactions between tumor cells and the stroma. Results DGC and GSC secretomes have distinct proteomes revealed by tandem mass spectrometry While GSCs alone can initiate a tumor, the overall tumor growth requires functional interactions between GSCs and DGCs (Singh et al., 2004; Wang et al., 2018). To further understand the respective roles of GSCs vs. DGCs in tumor growth, we compared the conditioned medium (CM) derived from three patient-derived human GSC cell lines (MGG4, MGG6, and MGG8) (Wakimoto et al., 2009) and their corresponding DGCs, using a quantitative proteomic strategy. Proteins in CMs were systematically analyzed by nano-flow liquid chromatography coupled to Fourier transform tandem mass spectrometry (nano-LC-FT-MS/MS), and their relative abundance in DGC vs. GSC CM was determined by label-free quantification. We found that 119 proteins are more abundant in GSC CM, while 185 proteins are more abundant in the DGC CM (p<0.05, Figure 1A; Supplementary file 1). Analysis of overrepresented functional categories among proteins exhibiting differential abundances in GSC vs. DGC CMs using Perseus with a p-value <0.05 revealed that the DGC CM is enriched in proteins known to exhibit extracellular or cell surface localization, such as proteins annotated as ECM organization while terms related to DNA replication and many signaling pathways are enriched in GSC CM (Figure 1; Figure 1—figure supplement 1). Figure 1 with 3 supplements see all Download asset Open asset Quantitative proteomics shows a higher abundance of fibromodulin under the control of TGF-β signaling in the differentiated glioma cell (DGC) secretome. (A) Volcano plot depicting relative protein abundance in glioma stem-like cell (GSC) (MGG4, MGG6, and MGG8) vs. their corresponding DGC conditioned media (CM). The black dots represent the nonsignificant proteins (p>0.05), while the red (higher abundance in GSC CM) and green (lower abundance in GSC CM) dots represent the significant ones (p<0.05) with a log2 fold change cutoff of >0.58 or <−0.58. (B) Venn diagram showing the relationship between proteins upregulated in DGC CM and annotated extracellular matrix (ECM) proteoglycans. Of the common proteins shown below, fibromodulin (FMOD) exhibits the highest DGC/GSC ratio (indicated by the more intense red color). (C) Label-free quantification (LFQ) of FMOD, expressed as log2 fold change in GSCs vs. DGCs CM. (D) RT-qPCR analysis shows upregulation of FMOD transcript in DGCs (red bars) compared to GSCs (blue bars). (E) Western blotting shows the presence of higher amounts of intracellular FMOD in DGCs compared with corresponding GSCs. (F) Western blotting shows the presence of higher amounts of FMOD in the DGC CM compared to GSC CM (top panel). Equal loading of the proteins assessed by Ponceau Red staining (bottom panel). (G) RT-qPCR analysis shows a reduction of FMOD transcript level in DGCs, but not in GSCs, upon treatment with SB431542 (10 μM), a TGF-β inhibitor. Red bars indicate FMOD expression, and blue bars represent TGM2 (a bonafide TGF-β pathway target gene) expression. (H) Western blotting shows the reduction of FMOD protein level in DGCs, but not in GSCs, upon treatment with SB431542 (10 μM) (intracellular, top, and secreted, bottom). Equal loading of the secreted proteins assessed by Ponceau Red staining. (I) Western blotting shows higher expression of pSAMD2 in DGCs than in GSCs, which is reduced by SB431542 treatment. (J) RT-qPCR shows significantly higher fold enrichment of pSMAD2 in the FMOD promoter, which is inhibited upon SB431542 treatment (10 μM). For panels (C), (D), (G), and (J), n=3, and p-value is calculated by unpaired t-test with Welch’s correction. p-Value <0.05 is considered significant with *, **, and *** representing p-values <0.05, 0.01, and 0.001, respectively. Figure 1—source data 1 Source data used to generate Figure 1A. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data1-v3.zip Download elife-78972-fig1-data1-v3.zip Figure 1—source data 2 Source data used to generate Figure 1B. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data2-v3.zip Download elife-78972-fig1-data2-v3.zip Figure 1—source data 3 Source data used to generate Figure 1C. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data3-v3.zip Download elife-78972-fig1-data3-v3.zip Figure 1—source data 4 Source data used to generate Figure 1D. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data4-v3.zip Download elife-78972-fig1-data4-v3.zip Figure 1—source data 5 Source data used to generate Figure 1E, F, H, I. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data5-v3.zip Download elife-78972-fig1-data5-v3.zip Figure 1—source data 6 Source data used to generate Figure 1G. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data6-v3.zip Download elife-78972-fig1-data6-v3.zip Figure 1—source data 7 Source data used to generate Figure 1J. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data7-v3.zip Download elife-78972-fig1-data7-v3.zip Figure 1—source data 8 TGF-β is activated in glioblastoma (GBM) over normal samples in multiple datasets. The table shows the gene set enrichment analysisGene Set Enrichment Analysis (GSEA) output, indicating significant positive enrichment of multiple TGF-β-related gene sets in GBM over normal in multiple publicly available datasets. Darker to lighter red indicates the highest to lowest normalized enrichment score (NES), while the symbol '%' and green color indicate the significant gene sets p-Value <0.05 is considered significant. NA, not available. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data8-v3.zip Download elife-78972-fig1-data8-v3.zip Figure 1—source data 9 Source data used to generate Figure 1—source data 8. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data9-v3.zip Download elife-78972-fig1-data9-v3.zip Figure 1—source data 10 Mesenchymal gene expression signature and TGF-β signaling pathway are enriched in differentiated glioma cells (DGCs). Gene Set Variance Analysis (GSVA) output indicates the molecular subtypes of the glioma stem-like cells (GSCs) and DGCs of MGG4, MGG6, and MGG8 (each in triplicates). The darkest red indicates the highest GSVA value for a subtype indicating the highest enrichment in that particular sample, with decreasing color intensity indicating a lower enrichment of the subtypes. The table also shows the GSVA score for the TGF-β hallmark gene set from MSigDb across the multiple data sets. The darkest red depicts the highest enrichment, and gradual lighter colors indicate gradually decreasing enrichment. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data10-v3.zip Download elife-78972-fig1-data10-v3.zip Figure 1—source data 11 Source data used to generate Figure 1—source data 10. https://cdn.elifesciences.org/articles/78972/elife-78972-fig1-data11-v3.zip Download elife-78972-fig1-data11-v3.zip TGF-β signaling controls the expression of FMOD in DGCs The enrichment of the ECM annotation among proteins exhibiting higher abundance in DGC secretome prompted us to focus on ECM proteoglycans in line with their critical role in facilitating cancer cell signaling through their interaction with growth factor receptors, extracellular ligands and matrix components, and in promoting tumor–microenvironment interactions (Winkler et al., 2020). Six ECM proteoglycans were found to be more abundant in DGC CM compared with GSC CM (Figure 1B). The role of five of them (LAMB2, SERPINEE1, ITGB1, TNC, and LAMA5) in tumor growth has been well established (Angel et al., 2020; Bartolini et al., 2016; Long et al., 2016; Wang et al., 2021; Yang et al., 2020b). We thus focused on FMOD, which exhibited the highest DGC CM/GSC CM protein ratio. FMOD is a small leucine-rich repeat proteoglycan upregulated in GBM due to the loss of promoter methylation orchestrated by TGF-β1-dependent epigenetic regulation (Mondal et al., 2017). FMOD promotes glioma cell migration through actin cytoskeleton remodeling mediated by an integrin-FAK-Src-Rho-ROCK signaling pathway but does not affect colony-forming ability, growth on soft agar, chemosensitivity, and glioma cell proliferation (Mondal et al., 2017). We first confirmed the higher abundance of FMOD seen in DGC CM compared to GSC CM (Figure 1C) both at the transcript level (Figure 1D) and at the protein level (Figure 1E and F) in three GSC cell lines (MGG4, MGG6, and MGG8). In line with our previous findings indicating that TGF-β signaling controls FMOD expression in glioma (Mondal et al., 2017), we next explored the possible role of this pathway in FMOD overexpression in DGCs. Gene set enrichment analysis (GSEA) of differentially regulated transcripts in GSC vs. DGC showed significant depletion of several TGF-β signaling pathway genes (Figure 1—figure supplement 2; Supplementary file 2), suggesting an enhanced TGF-β signaling in DGCs. Likewise, GSEA revealed an enrichment of several TGF-β signaling pathway genes in most GBM transcriptome datasets (Figure 1—source data 8), further supporting the activation of TGF-β signaling in DGCs that represent the bulk of GBMs. In addition, the DGCs used in this study that express a high level of FMOD showed enrichment in mesenchymal signature compared to GSCs (Figure 1—source data 10), consistent with the elevated TGF TGF-β signaling and FMOD levels we observed in the mesenchymal GBM subtype (Figure 1—figure supplement 3A B). Moreover, treating MGG8-DGCs with the TGF-β inhibitor (SB431542) significantly decreased luciferase activity of SBE–Luc (a TGF-β-responsive reporter and contains Smad-binding elements) and FMOD Promoter-Luc reporters (Figure 1—figure supplement 3C D). We also found higher levels of FMOD and TGM2 (a bonafide TGF-β target gene) transcripts and FMOD and pSMAD2 (an indicator of activated TGF-β signaling) proteins in MGG8-DGCs than MGG8-GSCs (Figure 1G–I). The addition of a TGF-β inhibitor (SB431542) significantly decreased transcript levels of FMOD and TGM2 and protein levels of FMOD and pSMAD2 in MGG8-DGCs (Figure 1G–I). Further, chromatin immunoprecipitation experiments revealed pSMAD2 occupancy on FMOD promoter in MGG8 DGCs that was significantly reduced by pretreating cells with SB431542 (Figure 1J). These results demonstrate a predominant expression and secretion of FMOD by DGCs that are promoted by TGF-β signaling. Tumor growth requires FMOD secreted by DGCs Toward exploring the possible role of DGC-secreted FMOD in glioma tumor growth, we first investigated the role of FMOD in GSC and DGC growth and interconversion between both cell populations in vitro using two human (MGG8 and U251) and two murine (AGR53 and DBT-Luc) glioma cell lines. We found that the absence of FMOD neither affected GSC growth and differentiation to DGC (Figure 2—figure supplement 1, Figure 2—figure supplement 2, Figure 2—figure supplement 3) nor DGC growth and reprogramming to form GSCs (Figure 2—figure supplement 4, Figure 2—figure supplement 5, Figure 2—figure supplement 6; more details in Appendix 1), consistent with our previous findings showing that FMOD does not affect glioma cell proliferation in vitro (Mondal et al., 2017). In line with previous findings that DGCs cooperate with GSCs to promote tumor growth (Wang et al., 2018), we then evaluated the ability of DGCs silenced for FMOD to support the growth of tumors initiated by GSCs in co-implantation experiments in a syngeneic mouse model using GSCs and DGCs derived from DBT-Luc glioma cells. Reminiscent of our observations in MGG8 cell line, DBT-Luc-DGCs express higher levels of FMOD than DBT-Luc-GSCs (Figure 2—figure supplement 5C D). To silence the expression of FMOD in DBT-Luc-DGCs, we used a doxycycline-inducible construct that contains an inducible mCherry-shRNA downstream of the Tet-responsive element (Angel et al., 2020; Figure 2A). The scheme of the co-implantation experiment is described on Figure 2B. DBT-Luc-GSC cells were coinjected with either DBT-Luc-DGC/miRNT (nontargeting shRNA) or DBT-Luc-DGC/miRFMOD (FMOD shRNA). In both groups, 50% of the mice received doxycycline on alternated days from day 9 post-injection until the end of the experiment. Tumors in mice coinjected with DBT-Luc-GSCs and DBT-Luc-DGCs/miRNT grew much faster and reached a significantly larger size (measured by bioluminescence) than tumors in mice injected with DBT-Luc-GSCs alone regardless of doxycycline treatment (Figure 2B–D, compare black and purple lines with blue line; Supplementary file 3). Notably, mice treated with doxycycline did show mCherry expression in tumors (Figure 2C). In contrast, injected DBT-Luc-DGC/miRFMOD cells failed to support the growth of tumors initiated by DBT-Luc-GSCs in doxycycline-treated mice compared to doxycycline-untreated mice (Figure 2B–D, compare red line with orange line; Supplementary file 3). While mice injected with DBT-Luc-GSCs+DBT-Luc-DGCs/miRFMOD (Dox+) showed an increase in tumor growth until the onset of doxycycline treatment (as seen in the rise in bioluminescence), subsequent tumor growth was drastically reduced. As expected, mice injected with DBT-Luc-DGCs alone developed substantially small tumors (Figure 2C and D). The small tumors formed in animals injected with either DBT-Luc-GSC+DBT-Luc-DGC/miRFMOD (Dox+) or DBT-Luc-GSC alone expressed significantly less FMOD protein than other tumors (Figure 2—figure supplement 7). These results indicate that FMOD secreted by DGCs is essential for the growth of tumors initiated by GSCs. Figure 2 with 7 supplements see all Download asset Open asset Differentiated glioma cell (DGC)-secreted fibromodulin (FMOD) is essential for tumor growth initiated by glioma stem-like cells (GSCs) in vivo in a co-implantation experiment. (A) Diagram of the inducible shFMOD lentiviral construct. (B) Schema depicts the GSC-DGC co-implantation experiment in C57BL/6 mice (n = 5 per group). Mice were injected subcutaneously with a combination of DBT-Luc-GSCs and DBT-Luc-DGCs transduced with either miRNT (nontargeting) or miRFMOD lentiviruses. To induce miRNT or miRFMOD (and mCherry), mice received doxycycline (100 µl of 1 mg/ml per animal) as intraperitoneal injections at indicated times. The control groups were only injected with DBT-Luc-GSCs or DBT-Luc-DGCs and did not receive doxycycline. (C) In vivo imaging of the injected mice shows tumor growth over time by bioluminescence and mCherry fluorescence, according to the timeline shown in (B). (D) Quantification of the total radiance. The different colors represent the different groups of animals. Significant differences between each of the groups were calculated using ANOVA. The p-values for days 28 and 32 are shown. A detailed comparison of the p-values between different groups is provided in Supplementary file 2. Figure 2—source data 1 Source data used to generate Figure 2C. https://cdn.elifesciences.org/articles/78972/elife-78972-fig2-data1-v3.zip Download elife-78972-fig2-data1-v3.zip Figure 2—source data 2 Source data used to generate Figure 2D. https://cdn.elifesciences.org/articles/78972/elife-78972-fig2-data2-v3.zip Download elife-78972-fig2-data2-v3.zip FMOD induces angiogenesis of host-derived and tumor-derived endothelial cells Tumor cell interactions with stromal cells are critical for glioma tumor growth (Pine et al., 2020). Small leucine-rich proteoglycans such as FMOD promote angiogenesis in the context of cutaneous wound healing (Pang et al., 2019; Zheng et al., 2014). In addition, we previously found a significant enrichment of the term ‘angiogenesis’ among differentially regulated genes in FMOD-silenced U251 glioma cells (Mondal et al., 2017). In light of these observations, we next examined the impact of FMOD on tumor angiogenesis. First, we tested the ability of glioma cell-derived FMOD to induce angiogenic network formation by immortalized human pulmonary microvascular endothelial cells (ST1). We used LN229 and U251 glioma cells, which express low and high levels of FMOD, respectively, for overexpression and silencing studies (Mondal et al., 2017). We found that the CM derived from LN229 cells stably expressing FMOD (LN229/FMOD) induced more angiogenesis than LN229/Vector stable cells (Figure 3A and B). Further, the CM of FMOD-silenced U251 cells was less efficient in promoting angiogenesis than the CM of cells expressing nontargeting siRNA (Figure 3C, Figure 3—figure supplement 1A) or shRNA (Figure 3—figure supplement 1B). The addition of recombinant human FMOD (rhFMOD) to the CM of U251/siFMOD cells rescued its ability to induce angiogenesis (Figure 3C, Figure 3—figure supplement 1A). More importantly, the addition of rhFMOD directly to endothelial cells induced angiogenesis in the presence of a control antibody (IgG) but not in the presence of an FMOD neutralizing antibody ( Figure 3—figure supplement 1C). Figure 3 with 3 supplements see all Download asset Open asset Differentiated glioma cell (DGC)-secreted fibromodulin (FMOD) promotes angiogenesis of host-derived and tumor-derived endothelial cells. (A) Representative images of in vitro network formation by ST1 cells treated with conditioned medium (CM) of LN229/Vector CM and LN229/FMOD. In the positive control condition (top right), cells are plated in complete endothelial cell media (M199) supplemented with endothelial cell growth factors (ECGS) and 20% fetal bovine serum (FBS), and in the negative control (top left), cells are plated in incomplete M199 (without serum and ECGS). Networks formed by ST1 cells treated with CM of LN229/Vector (left bottom) and LN229/FMOD (right bottom). Magnification = ×10, scale bar = 100 μm. (B) Quantification of the number of complete networks formed in (A). (C) Quantification of the number of networks formed by ST1 cells treated with CM of U251-DGC/siNT, U251-DGC/siFMOD, and U251-DGC/siFMOD + rhFMOD (400 nM) cells. (D) Quantification of the number of networks formed by ST1 cells treated with CM of MGG8-GSC, MGG8-DGC, MGG8-DGC/shNT, and MGG8-DGC/shFMOD supplemented with anti-FMOD or rhFMOD (400 nM) as indicated. (E) Representative images of in vitro network formation by primary human brain-derived microvascular endothelial cells (HBMECs). In the positive control condition (top right), HBMEC cells are plated in complete endothelial cell media (M199) supplemented with ECGS and 20% FBS, and in the negative control (top left), cells are plated in incomplete M199 without serum and ECGS. Networks formed by HBMEC cells treated with CM of MGG8-DGC/shNT, MGG8-DGC/shFMOD, and MGG8-DGC/shFMOD + rhFMOD (400 nM). Magnification = ×4, scale bar = 200 μm. (F) Quantification of the number of complete networks formed in (E). (G) RT-qPCR analysis showing transcript levels of CD31 (blue bars) and FMOD (orange bars) in ST1, MGG8-DGC/shNT, MGG8-DGC/shFMOD, MGG8-DGC-TDEC/shNT, and MGG8-DGC-TDEC/shFMOD cells. (H) Western blotting shows FMOD and CD31 protein levels in MGG8-DGC/shNT, MGG8-DGC/shFMOD, MGG8-DGC-TDEC/shNT, and MGG8-DGC-TDEC/shFMOD cells. (I) Representative images of in vitro network formation by ST1, MGG8-DGC-TDEC/shNT, and MGG8-DGC-TDEC/shFMOD upon bovine serum albumin (BSA) and rhFMOD (400 nM) treatments. Top panels: in the positive control conditions, ST1 or MGG8-DGC-TDEC cells are plated in complete endothelial cell media (M199) supplemented with ECGS and 20% FBS, and in the negative control conditions, ST1 or MGG8-DGC-TDEC cells are plated in incomplete M199 (without serum and ECGS). Bottom panels: networks formed by HBMEC cells treated with CM of MGG8-DGC-TDEC/shNT and MGG8-DGC-TDEC/shFMOD supplemented with either BSA or rhFMOD (400 nM). Magnification = ×4, scale bar = 200 μm. (J) Quantification of the number of complete networks formed in (I). For panels (B–D), (F), (G), and (J), n=3 and p-values were calculated by unpaired t-test with Welch’s correction. p-Value <0.05 was considered significant with *, **, and *** representing p-values <0.05, 0.01, and 0.001, respectively. ns, nonsignificance. 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