Staufen1 controls mitochondrial metabolism via HIF2α in embryonal rhabdomyosarcoma and promotes tumorigenesis

Shekoufeh Almasi; Sahar SarmastiEmami; Stephen Baird; Zhibin Ning; Daniel Figeys; Jocelyn Côté; Kyle N Cowan; Bernard J Jasmin

doi:10.1007/s00018-023-04969-4

. 2023 Oct 17;80(11):328. doi: 10.1007/s00018-023-04969-4

Staufen1 controls mitochondrial metabolism via HIF2α in embryonal rhabdomyosarcoma and promotes tumorigenesis

Shekoufeh Almasi ¹, Sahar SarmastiEmami ¹, Stephen Baird ², Zhibin Ning ³, Daniel Figeys ³, Jocelyn Côté ^1,⁴, Kyle N Cowan ^1,^5,⁶, Bernard J Jasmin ^1,^4,^✉

PMCID: PMC11071833 PMID: 37847286

Abstract

Elevated mitochondrial metabolism promotes tumorigenesis of Embryonal Rhabdomyosarcomas (ERMS). Accordingly, targeting oxidative phosphorylation (OXPHOS) could represent a therapeutic strategy for ERMS. We previously demonstrated that genetic reduction of Staufen1 (STAU1) levels results in the inhibition of ERMS tumorigenicity. Here, we examined STAU1-mediated mechanisms in ERMS and focused on its potential involvement in regulating OXPHOS. We report the novel and differential role of STAU1 in mitochondrial metabolism in cancerous versus non-malignant skeletal muscle cells (NMSkMCs). Specifically, our data show that STAU1 depletion reduces OXPHOS and inhibits proliferation of ERMS cells. Our findings further reveal the binding of STAU1 to several OXPHOS mRNAs which affects their stability. Indeed, STAU1 depletion reduced the stability of OXPHOS mRNAs, causing inhibition of mitochondrial metabolism. In parallel, STAU1 depletion impacted negatively the HIF2α pathway which further modulates mitochondrial metabolism. Exogenous expression of HIF2α in STAU1-depleted cells reversed the mitochondrial inhibition and induced cell proliferation. However, opposite effects were observed in NMSkMCs. Altogether, these findings revealed the impact of STAU1 in the regulation of mitochondrial OXPHOS in cancer cells as well as its differential role in NMSkMCs. Overall, our results highlight the therapeutic potential of targeting STAU1 as a novel approach for inhibiting mitochondrial metabolism in ERMS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00018-023-04969-4.

Keywords: Embryonal rhabdomyosarcoma, STAU1, Mitochondrial metabolism, OXPHOS

Introduction

Rhabdomyosarcoma (RMS) is the most prevalent soft tissue cancer in children and young adults and, overall, it accounts for 4–8% of all childhood malignancies [1, 2]. High-grade and malignant RMS tumors usually arise in muscle beds and metastasize to various organs including the head and neck, the genitourinary tract, and extremities [1, 3, 4]. Embryonal (ERMS) and Alveolar (ARMS) are the two main forms of RMS which are driven by different mechanisms [2]. While ARMS tumors mostly result from chromosomal translocation between FOXO1 and PAX3/7 genes (called fusion-positive RMS), a single mutation for ERMS tumors has not yet been reported [5]. Therefore, ERMS is also called PAX fusion-negative or fusion-negative RMS. However, several allelic losses and mutations are associated with ERMS, such as copy number alterations and mutations in the RAS pathway [2]. Morphologically, ERMS tumors contain undifferentiated stellate-shaped, small ovoid, and spindle cells which show biologic features of primitive skeletal muscle [1]. Immunohistology staining for myogenin and MyoD1 is the most common diagnostic approach for detecting fusion-negative ERMS tumors [6]. Despite recent advances in cancer therapy, the relapse-free survival of metastatic ERMS is around 40%. This statistics further highlights the critical need for the identification of novel therapeutic targets leading to the development of new ERMS therapies [7].

Increased mitochondrial oxidative phosphorylation (OXPHOS) has been shown to play a crucial role in ERMS oncogenesis. Accordingly, targeting mitochondrial dysfunction has been proposed as a therapeutic approach for ERMS treatment [8]. In cells, approximately 90% of the intracellular oxygen is consumed by mitochondria to allow OXPHOS, a primary metabolic pathway for adenosine triphosphate (ATP) production [9]. The increased ATP demand due to a high proliferation rate of ERMS cells is fulfilled by elevated mitochondrial OXPHOS and oxygen consumption. Increased oxygen demand by mitochondria can cause hypoxia (reduced oxygen level), leading to hypoxic stress [10]. Therefore, upregulation of the Hypoxia-Inducible Factor (HIF), as an adaptive response, has been reported in ERMS tumors [11]. The elevated mitochondrial metabolism along with activation of the HIF pathway favors ERMS cells proliferation, oncogenesis, and metastasis under hypoxic condition [11].

In this context, we have previously reported the important role of the RNA-binding protein Staufen1 (STAU1) in ERMS oncogenesis. Specifically, we reported that STAU1 is upregulated in RMS and that this increased expression enhances RMS oncogenesis. Accordingly, STAU1 downregulation inhibits RMS growth and tumorigenesis. Interestingly, the underlying mechanism for this effect appears to be specific to distinct subtype of RMS [12]. Indeed, our results showed that in Alveolar RMS (ARMS), STAU1 knockdown led to autophagy inhibition via downregulation of the JNK pathway. These effects promoted apoptosis and reduced tumor growth of ARMS cells [13]. By contrast, STAU1 depletion limited in vitro proliferation and in vivo tumor growth of ERMS [12]. In the latter case however, the exact anti-growth mechanism by which STAU1 limits ERMS proliferation remains unknown.

STAU1 is a multifunctional, double-stranded RNA-binding protein involved in multiple aspects of RNA metabolism including splicing, stability, translation and transport [14]. The STAU1 gene expresses two splicing variants with a molecular mass of 55 and 63 KDa [15]. The STAU1⁵⁵ is the most expressed isoform in various tissues and differs from the STAU1⁶³ variant due to the lack of 81 N-terminal amino acid residues. STAU1⁵⁵ has been reported as the main isoform of STAU1 that controls the fate of target mRNAs [16, 17]. Hence, through its essential functions in mRNA metabolism and expression, STAU1 plays pivotal roles in cell fate [18]. Of particular relevance, several recent studies reported a link between STAU1 dysregulation and various human diseases including cancer [12, 14, 19, 20]. In addition to its oncogenic role in RMS, we also recently reported that STAU1 modulates prostate cancer progression [19]. Work from others have mainly focused on the impact of a mechanism referred to as STAU1-mediated mRNA decay, in cancer [21, 22], whereas several additional studies have highlighted a unique role of STAU1 in cancer growth [14, 23, 24]. For instance, a recent study emphasized the impact of STAU1 on cancer cell growth via maintenance of a balance between cell proliferation and apoptosis [23]. Altogether, these studies are coherent with the emerging notion that STAU1 is a novel and key factor in oncogenesis for many different cancers. A deeper understanding of its pro-cancer impact is thus warranted if the ultimate goal is to identify novel anti-cancer therapies based on STAU1 manipulation.

Given the available evidence on the upregulation of mitochondrial metabolism in ERMS [8] as well as our recent studies showing the oncogenic role of STAU1 in this malignancy [12], we wondered whether STAU1, directly or indirectly, regulates mitochondrial function in ERMS. To this end, we explored the potential mechanisms underlying STAU1 to the regulation of mitochondrial OXPHOS and the HIF pathway, two factors that promote cancer progression.

Materials and methods

Constructs and antibodies

The lentiviral constructs used were pTRIPZ-TurboRFP (GE Healthcare), pTRIPZ-TurboRFP-shSTAU1 (Clone IDs: V2TH_42695, V2TH_202941, V2TH_202833, V3TH_356890, V3TH_341446) (GE Healthcare dharmacon Inc.), pLKO.1-TRC (Addgene#10878), and pCDH-GFP-HA3, pCDH-hHif2α-HA3 (Wickham et al., 1999), pMD2.G (Addgene # 12259), and psPAX2 (Addgene #12260). LentiCRISPR v2-gsSTAU1 and LentiCRISPR v2-gsEPAS1 (Genome Editing and Molecular biology (GEM) facility of University of Ottawa).

Reagents

Cell culture media, FBS, l-Glutamine, and penicillin/streptomycin antibiotic were purchased from Invitrogen/Thermo Fisher Scientific. The chemical reagents purchased from Sigma-Aldrich were Oligomycin A (75351), FCCP (C2920), Rotenone (R8875), and Antimycin A (A8674). H2DCFDA (D399) and MitoSOX Green (M36005), Pierce™ Coomassie Plus (Bradford) Assay Reagent (23238), and CellTrace™ CFSE Cell Proliferation (C34554) kits were purchased from Thermo Fisher Scientific. Urea, dithiothreitol (DTT), ammonium bicarbonate (ABC), and iodoacetamide (IAA) were purchased from Sigma (St. Louis, MO), while HPLC grade water and acetonitrile (ACN), and formic acid (FA) were obtained from Merck (Darmstadt, Germany). Trypsin was purchased from Worthington Biochemical Corp (Lakewood, NJ). All the chemicals used were of analytical purity grade, except for ACN and FA, which were of HPLC grade.

Cell culture, transfection, and lentivirus production and infection

Mammalian immortalized cell lines were purchased from ATCC and cultured in the specific medium. The immortalized skeletal muscle cell line HSMM-C3 was generated in Dr. Pantic’s lab at University of Padua, Italy [25]. The RH36 cell line was from Dr. P. Houghton (St. Jude Children’s Hospital, Memphis, TN, USA), while the RD cell line was from ATCC. ERMS cells (RH36 and RD) were grown in RPMI-1640 medium. The culture media were supplemented with 10% heat-inactivated Fetal Bovine Serum (FBS; Gibco, Life Technologies, 16000036) and 20 μg/ml penicillin/streptomycin antibiotic (Gibco; Life Technologies, 15070063). HSMM-C3 cells were grown in SkBM™-2 Basal Medium (Lonza# CC-3245) supplemented with SkGM™-2 SingleQuots™ supplements (Lonza# CC-3244). All cells were grown at 37 °C and 5% CO₂ incubator. Since only a few ERMS cell lines are commercially available [26], we focused here on two ERMS cell lines, namely RD and RH36, to explore the impact of STAU1 and HIF2α. Also, given that our previous publication showed a similar impact of STAU1 expression in different clones of immortalized HSMM cell lines (HSMM-C2 and HSMM-C3), in this study, we only investigated one of these clones, HSMM-C3 [13].

To generate genetically modified cell lines, pTRIPZ-LV, LentiCRISPR v2, and pCDH plasmids were used according to the protocol of the 3rd generation lentiviral packaging system [27]. Briefly, lentiviral particles were generated in HEK-293T cells by co-transfection of psPAX2 (6 μg), pMD2.G (3 μg), and pTRIPZ-LV-gene-specific or pLKO-LV-gene-specific (6 μg) plasmids in the presence of PEI transfection reagent (Sigma). On the next day, the lentiviral particles were filtered (Millex-GS; 0.22 μm sterile filter) and stored at − 80 °C.

For transduction, cells were cultured in 6-well plates and grown for 24 h. The medium was replaced with 2 mL of cell-specific medium, containing 500 μL of lentivirus aliquot and 8 μg/mL of Sequebrene (Sigma) and incubated at 37 °C and 5% CO₂ for 72 h. Puromycin (concentration varied based on the cell type) was used to select transduced cells. Transduction efficiency was assessed with RT-qPCR and western blot.

To transiently overexpressed Hif2α in ERMS cell lines, transfection and transduction were performed as mentioned. Seventy-two hours post-transduction, cells were analyzed.

Western blotting

Cells were lysed in 1X RIPA buffer (20 mM Tris–HCl (pH 7.5), 150 mM NaCl, 1 mM Na²⁻ EDTA, 1 mM EGTA, 1% NP-40, 1% sodium deoxycholate, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na₃ VO₄, 1 µg/ml leupeptin) in the presence of Proteinase and Phosphatase inhibitor cocktails. Protein concentration was tested according to the BCA assay protocol (Thermo Fisher Scientific). For western blot analysis, 20 μg of protein samples was separated using SDS-PAGE gel electrophoresis and then transferred onto a 0.2 µm or 0.45 µm nitrocellulose membrane (BioRad). Membranes were blocked with 5% milk in 1X TBST (137 mM NaCl, 2.7 mM KCl, 19 mM Tris–base, 0.1% TWEEN 20) for 1 h followed by 3 washes. Membranes were incubated with specific primary antibodies overnight at 4 °C. Next, membranes were washed 3 times with 1X TBST and incubated with the appropriate HRP-conjugated secondary antibody for 1 h at RT. HRP substrate chemiluminescent reaction was performed at RT in light. The antibodies used were anti-Staufen1 (ab73478 and ProSciψ#5997), anti-EPAS1 (71565, Cell Signaling), and anti-β-actin (sc-47778).

Proteomics

Protein samples were prepared as above (using 1X RIPA lack of Proteinase inhibitor cocktail) and sent for proteomic analysis.

More specifically, for protein degradation step. The filter-aided sample preparation (FASP) method was modified for protein digestion (ref: PMID: 19876013). Briefly, 50 μg samples were loaded onto a 10 K filter (Amicon^® Ultra-0.5, Millipore), and 8 M urea was used to displace the original buffer. Protein reduction (with 10 mM DTT in 50 mM ABC) and alkylation (with 10 mM IAA in 50 mM ABC) were done sequentially in the filter. A mass ratio of 1:50 between trypsin and protein was used for digestion at 37 °C overnight with continuous shaking. The digested peptides were desalted using 200 μL filter-tip columns packed in-house with 10 μm ReproSil-Pur C18 beads (200 Å; Dr. Maisch GmbH, Germany) and dried down in a SpeedVac (ThermoFisher Scientific, San Jose, CA). The dried samples were then reconstituted in 100 μL of 0.5% (v/v) FA, and 2 μL was loaded for MS analysis.

The LCMS Analysis was done as follows: Dionex Ultimate RS3000 was connected to an Exploris 480 mass spectrometer (ThermoFisher Scientific, San Jose, CA) and operated with a nano-electrospray interface in positive ion mode. The solvent system consisted of buffer A (0.1% FA in water) and buffer B (0.1% FA in 80% acetonitrile). Reconstituted peptides were loaded on a 75 μm I.D. × 150 mm fused silica analytical column packed in-house with 3 μm ReproSil-Pur C18 beads (100 Å; Dr. Maisch GmbH, Ammerbuch, Germany). The flow rate was set to 300 nL/min, and the gradient was set as 5–35% buffer B in 105 min, followed by 5 min from 35 to 80%, 5 min of 80%, and 5 min of re-equilibration. The spray voltage was set to 2.2 kV, and the temperature of the heated capillary was 300 °C. One full MS scan from 350 to 1200 m/z was followed by a data-dependent MS/MS scan of the 15 most intense ions with a dynamic exclusion repeat count of 1 in 20 s. The mass resolution was 60,000 for MS1 and 15,000 for MS2. Real-time internal calibration by the lock mass of background ion 445.120025 was used. All data were recorded using Xcalibur software (ThermoFisher Scientific, San Jose, CA).

For data analysis, the peak lists of the raw files were processed and analyzed using MaxQuant software (Version 1.6.3.4.0) (ref: PMID: 19029910) against the human FASTA download from Uniprot, along with the built-in contaminant sequences, including commonly observed contaminants. Cysteine carbamidomethylation was selected as a fixed modification. Methionine oxidation and protein N-terminal acetylation ubiquitination were set for variable modification. Enzyme specificity was set to trypsin, not allowing for cleavage N-terminal to proline. Other parameters were used as default.

Further Pathway analysis was performed, on the obtained data from MaxQuant software, using IPA QIAGEN Inc. (https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis). First proteomics data was used to generate clustered heat maps indicating different protein expression patterns in three studied cell lines. Different levels of hierarchical clustering implicated similarities between or among 3 cell lines. More specifically, the first level of hierarchical cluster shows similarities in protein expression pattern across samples. The second and the third level of clustering focuses on the differences between protein profile of wildtype cells and STAU1-depleted cells as well as similarities among the replicates in each group. The generated heat map utilized color ranges from red to green depicting high to low protein expression levels.

Next, a causal analytics tool was used for downstream effect analysis and prediction of new targets which were not detected in the proteomics experiment. Another IPA causal analytics tool called “Upstream Regulator Analysis” was utilized for the identification of potential upstream regulators. The IPA Upstream Regulator Analysis identifies upstream regulators and predict whether they are activated or inhibited based on the gene expression changes in the experimental dataset. The analysis is performed based on expected causal effects between upstream regulators and targets which are derived from the literature compiled in the Ingenuity^® Knowledge Base. Upstream regulator can be transcription factors, cytokines, microRNAs, receptors, and kinases. More specifically, the analysis screens the known targets of each upstream regulator in the tested dataset. Based on a comparison between the targets’ actual direction of change in the test dataset and the related findings derived from the literature, a prediction is issued for each upstream regulator. IPA uses a z-score algorithm to make predictions which is designed to reduce the chance of random predictions. “The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [28] partner repository with the dataset identifier PXD042156”.

RNA extraction, reverse transcription, and quantitative real-time PCR

mRNA was isolated from cells using TRIzol and Invitrogen RNA Purification kit. 2 μg of purified RNA was used for the synthesis of complementary DNA (cDNA) according to the Super Script^® II First-Strand Synthesis kit instruction (Invitrogen). Gene expression was examined by real-time PCR using gene-specific primers (Supplementary Table 1). Data were analyzed based on the Livak and Schmittgen’s 2^−ΔΔCT method and normalized to β-actin reference genes.

RNA immunoprecipitation assay

Cultured cells were washed with 1X PBS and fixed in 1% formaldehyde at RT. The reaction was quenched with 0.25 M glycine and cells were scraped and re-suspended in 1X RIPA buffer. Samples were sonicated (15-s pulses) and centrifuged (16,000g for 10 min at 4 °C). 40 µl of A/G plus beads (Santa Cruz Biotechnology, Inc.) was washed and incubated in low stringency RIPA buffer (50 mM Tris–HCL pH:7.4, 150 mM NaCl, 1% NP-40, 0.25% Sodium Deoxycholate, 1 mM EDTA, 1X PIC, 200 µg/ml of competitor tRNA, and 40 µg/ml of salmon sperm DNA) at 4 °C for 1 h. Protein/RNA complexes were immuno-precipitated using 3 µg of anti-IgG or anti-STAU1 antibodies and protein A/G plus beads. On the next day, samples were washed with low stringency RIPA buffer and TE buffer (10 mM Tris–HCl, pH 8.0, and 1 mM EDTA) and eluted in 100 µl of elution buffer (50 mM Tris–HCl, pH 7.0, 5 mM EDTA, 10 mM DTT, and 1% SDS). The cross-linking was reversed in via formaldehyde incubating at 70 °C for 5 h. The immunoprecipitation was evaluated via western blotting using anti-STAU1 antibody. The STAU1-intecteting mRNAs were detected by RT-qPCR after RNA purification.

mRNA stability assay

Cultured cells were treated with 10 µg/ml Actinomycin D for 30, 60, 90, and 120 min. RNA isolation and cDNA synthesis were performed as above. Percentages of remaining mRNAs were determined via RT-qPCR assays; β-actin was used as a control gene.

CFSE proliferation assay

Cells were cultured in the presence of 2.5 µM CFSE (Carboxyfluorescein succinimidyl ester; C34554 Thermo Fisher Scientific) for 96 h. On the day of the experiment, cells were fixed with 300µL of 4% paraformaldehyde (PFA) for 15 min at RT. Fixed cells were washed and re-suspended in 500µL FACS buffer (1 × PBS, 1% FBS and 1% 0.5 M EDTA). The CFSE-positive cells were analyzed using BD FACSCelesta™ (Spectron Corporation) at a wavelength of 488 nm. The fluorescent intensity has negative relationship with proliferation rate where reduced fluorescent intensity correlates with high proliferation rate. Data were analyzed using the Flowing software 2.5.1.

MTT viability assay

Cells were grown in 96-well plates for 72 h. On the day of the experiment, cells were incubated with 200 μl of the 5 mg/ml MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium) solution at 37 °C for 3 h following by 1 h incubation with 100 μl dimethyl sulfoxide (DMSO) to dissolve the formazan product. Absorbance was measured at 570 nm using the Beckman Coulter AD340 plate reader.

Intracellular reactive oxygen species detection assay

Cells were cultured in 6-well plates for 96 h. On the day of the experiment, cells were stained with 100 µl H2DCFDA (2′,7′-dichlorofluorescein diacetate) solution (1:200 in 1 × PBS) to measure the total level of intracellular ROS. Next, cells were washed and re-suspended in 1 × PBS. Samples were read at 488 nm using BD FACSCelesta™ (Spectron Corporation) and data were analyzed by Flowing 2.5.1 software. (Note: positive control cells were incubated with 500 nM H₂O₂ for 30 min in 37 °C incubator prior the staining.)

Mitochondrial superoxide detection assay

Cells were cultured in 6-well plates for 96 h. On the day of the experiment, cells were stained with 100 μl of 5 μM MitoSOX green (M36005, Thermo Fisher Scientific) to measure the mitochondrial superoxide level. Next, cells were washed with 1 × HBSS twice and re-suspended in 1 ml of 1 × HBSS and read at 586 nm using BD FACSCelesta™. Data were analyzed by Flowing 2.5.1 software.

Extracellular flux analysis

Mitochondrial respiration rate was assessed using Seahorse XF-96 Extracellular Flux Analyzer (Seahorse Bioscience, Billerica, MA, USA). Cells were cultured in a 96-well plate purchased from Seahorse Bioscience. The oxygen consumption rate (OCR) was measured in XF media (unbuffered DMEM containing 10 mM glucose) under basal conditions and in the presence of mitochondrial inhibitors (1.0 µM Oligomycin A (Sigma; 75351), 1.5 µM FCCP (Sigma; C2920), 1.0 µM Rotenone (Sigma; R8875) and 1.0 µM Antimycin A (Sigma; A8674). OCR was normalized to the cell density measured by Bradford assay on 610 nm. Basal OCR was calculated by subtraction of the residual rate after Antimycin A treatment. The maximal rate was calculated by subtraction of the residual rate after Antimycin A treatment from FCCP-induced OCR. ATP production was calculated by subtraction of OCR after oligomycin A treatment from basal OCR.

Results

Expression of STAU1 is upregulated in ERMS cells and its downregulation reduces cell proliferation

We first assessed by Western blots and RT-qPCR, expression of STAU1 in the malignant ERMS cell lines RH36 and RD, in comparison to a normal human skeletal muscle cell line (HSMM-C3) (Fig. 1). Consistent with our previous findings [12], our experiments indicated that STAU1 expression is elevated in ERMS at both protein and mRNA levels. To assess the impact of STAU1 on biological functions in ERMS, we generated STAU1 knockdown cell lines using a mixture of three independent STAU1-targeting lentiviral-shRNA constructs. Western blots and RT-qPCR analyses revealed that more than 60% knockdown was achieved in the three cell lines (Fig. 1b). Next, we examined the effects of STAU1 knockdown on the viability of ERMS and HSMM-C3 cells by conducting CFSE cell proliferation assays. Our data revealed that STAU1 downregulation reduced proliferation of ERMS cells as shown by an increase in the intensity of the fluorescent signal. These effects were observed in ERMS cells but without a significant change in the proliferation of HSMM-C3 cells (Fig. 1c). These results are consistent with our previous findings in STAU1-knockdown and -overexpressing non-transformed skeletal muscle cells, in which STAU1 modulation did not impact proliferation and survival of these cells. On the contrary, STAU1 depletion had a negative impact on the survival of various types of cancer cells including ERMS, ARMS, and prostate cancer. These findings indicate that the anti-proliferative impact of STAU1 depletion is specific to cancer cells.

Fig. 1 — STAU1 is upregulated in ERMS cell lines and its genetic silencing reduced proliferation of ERMS cells without impacting HSMM-C3. a Western blot and RT-qPCR analyses of STAU1 in a normal human skeletal muscle cell line (HSMM-C3) and 2 ERMS cell lines (RH36 and RD). Quantification of STAU1, normalized to β-actin, is presented as a ratio of the control (n = 3). b Inducible scramble control (Crtl.) and STAU1-knockdown (KD) cells were treated with 20 µg/mL Doxycycline for 96 h and assessed for the protein and mRNA level of STAU1 by western blotting and RT-qPCR, data were normalized to GAPDH (n = 3). c HSMM-C3 and ERMS cells were stained with CFSE and grown for 96 h in the presence of 20 µg/mL Doxycycline. The obtained CFSE proliferation data are presented in histograms and corresponding bar charts. Quantification of the mean of fluorescent intensity is presented as a ratio of the control cells (control is set to 1.0) (n = 3). Data are mean ± sd, one-way ANOVA was performed versus control (pTRIPZ) ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

STAU1 downregulation reduces mitochondrial OXPHOS in ERMS cells while promoting OXPHOS in non-transformed skeletal muscle cells

To begin characterizing the underlying mechanisms potentially linking the anti-proliferative impact of STAU1 knockdown in ERMS cells and to OXPHOS, we first examined mitochondrial metabolic rate via extracellular flux analyses using a Seahorse XF-96 Analyzer. Results from these experiments showed that the basal and maximal respiration rates (OCR) as well as ATP production levels were reduced in STAU1-depleted ERMS cell lines in the presence of the mitochondrial inhibitors oligomycin A, FCCP, rotenone and antimycin A (Fig. 2a). However, opposite findings were obtained with HSMM-C3 cells in which STAU1 depletion led to an increase in the basal OCR, maximal OCR, and ATP levels. These data highlight that STAU1 downregulation caused a reduction in mitochondrial metabolism in ERMS cells while promoting mitochondrial metabolism in HSMM-C3 cells.

Fig. 2 — STAU1 knockdown differentially impacts mitochondrial function in ERMS and HSMM-C3 cells. a The analysis was performed after 96 h of Ctrl. and KD cells treatment with 20 µg/mL Doxycycline. Mitochondrial respiration rate was measured using the XF-96 extracellular flux analyser in the presence of 1 µM Oligomycin A (drug A), 2 µM FCCP (drug B), 1 µM Rotenone (drug C), and 1 µM Antimycin A (drug D). Basal Respiration Rate (OCR), Maximal Respiration Rate, and ATP production level were quantified by Seahorse Wave 2.6.1 software. Our data show a remark change in the mitochondrial metabolic rate in STAU1 knockdown cells. b Representative histogram of MitoSOX staining assay in HSMM-C3 and ERMS cells, showing an alteration in mitochondrial superoxide level in STAU1-knockdown cells. The statistical relevance is presented as a bar graph. c Representative histograms and bar charts of intracellular ROS levels in HSMM-C3 and ERMS cell lines suggest changes in the ROS production in STAU1-depleted cells (n = 3). Quantification of the mean of fluorescent intensity is presented as a ratio of the control cells (control is set to 1.0). Data are mean ± sd, t test and one-way ANOVA were performed versus control (pTRIPZ) ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

In agreement with these observations, STAU1-depleted ERMS cells showed significant reductions in the levels of mitochondrial superoxide (MitoROS) and intracellular ROS. However, both MitoROS and intracellular ROS levels were elevated in HSMM-C3 cells (Fig. 2b, c). Altogether these findings show for the first time that STAU1 regulates mitochondrial function in cells but that the impact of STAU1 depletion is distinct in malignant versus non-transformed skeletal muscle cells.

The OXPHOS canonical pathway is differentially regulated in STAU1-depleted ERMS cells versus non-transformed skeletal muscle cells

In attempts to gain further insight into the molecular events underlying STAU1-mediated control of mitochondrial function, we performed proteomics analyses. The proteomics data consist of clustered heat maps indicating different protein expression patterns in HSMM-C3 and ERMS cell lines with or without STAU1 depletion (Fig. 3a) [29]. More specifically, according to the top level of clustering, RH36 and RD cells fall in a similar hierarchical cluster showing similarities in protein expression pattern across samples. The second and the third level of clustering focus on the differences between protein profile of wildtype cells and STAU1-depleted cells as well as similarities among the replicates in each group. These data explain how STAU1 depletion impact the complete protein profile of each of the three cell lines (HSMM-C3, RH36, and RD) (Fig. 3a).

The heat map reveals color ranges from red toward green depicting high to low expression levels and reflecting levels for each protein within the samples. These data indicate how the basal expression level of different proteins varies in three parental cell lines without STAU1 knockdown (HSMM-C3, RH36, and RD). As shown in Fig. 3a, the protein expression patterns are highly different in wildtype HSMM-C3 compared to RD cells, where the highly expressed proteins in HSMM-C3 are mainly low expressing ones in RD cells. The similar and opposite patterns are observed at the bottom of the heat map for low expressing proteins in HSMM-C3 and high expressing ones for RD cells. These data also reveal that protein expression is significantly altered during transformation of healthy skeletal muscle cells to RD cells. Interestingly, this converse pattern was also detected after STAU1 depletion in these cells. For instance, for the highly expressed proteins in HSMM-C3, STAU1 depletion led to their downregulation. However, when the same proteins were low expressed in parental RD cells, STAU1 depletion upregulated their levels. This suggests that STAU1 inversely impacts non-transformed and cancer cells via alterations in the levels of differentially expressed proteins. In these experiments, RH36 cells showed an intermediate pattern of both basal protein expression and modulation after STAU1 depletion. Nonetheless, the first level of clustering shows that RH36 cell share more similarities with RD cells than with HSMM-C3 cells (Fig. 3a).

In addition to these observations, representative volcano plots in Fig. 3b demonstrate alterations in specific protein levels in each cell line with and without STAU1 depletion. While the x-axis shows fold changes for each protein, the y-axis represents the significance of this change where − log₁₀(p value) > 1.3 is considered significant (Fig. 3b). Therefore, the proteins which were upregulated in STAU1-knockdown cells compared to control cells fall to the right side of the graph while the downregulated proteins fall to the left side. In addition, proteins with significant alterations a − log₁₀(p value) > 1.5 fall above the horizontal line and are shown in red. As illustrated, the number of significantly altered proteins after STAU1 depletion (shown in red) was relatively higher in RD and HSMM-C3 cells compared to RH36 cells suggesting that STAU1 depletion had less impact on protein expression patterns in RH36 cells compared to the other two cell models.

Next, we determined how alterations in protein profiles due to STAU1 depletion impact canonical pathways and cell functions. Interestingly, OXPHOS was detected as the very first and highly altered pathway in STAU1-knockdown cells and showed an upregulation in HSMM-C3 but a downregulation in ERMS cells following STAU1 depletion (Fig. 3c). The pathway analysis was performed using the Ingenuity Pathway Analysis (IPA) tool (QIAGEN Inc.: https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). Specifically, IPA analyses highlight alterations in each canonical pathway based on changes in protein levels within the various pathways. The generated heat map shows noticeable differences in the activity of various canonical pathways in non-transformed HSMM-C3 versus ERMS (RH36 and RD) cells after STAU1-depletion. For each cell line, data are presented as a ratio of pathway activity in STAU1-knockdown/parental cells, where green and red colors show downregulation and upregulation of the pathway, respectively. For each protein, a Z-score was calculated based on the STAU1-knockdown/parental cells ratio among all cell lines. Therefore, upregulation and downregulation of each pathway is relative to the average changes in all three cell lines. For instance, STAU1 depletion led to the downregulation of the cell cycle, autophagy, and Rho GTPase pathways in HSMM-C3 cells compared to other cell lines while these pathways were all upregulated in ERMS cells (Fig. 3c).

Given the importance of OXPHOS in ERMS oncogenesis and considering our findings on the role of STAU1 in the regulation of mitochondrial metabolism (see above), we focused additional analyses on changes in OXPHOS pathways following STAU1 depletion [8]. To this end, IPA analyses of our proteomics data showed an opposite effect on OXPHOS in ERMS and HSMM-C3 cells (Fig. 3c). More specifically, our comparative analysis showed that the OXPHOS pathway was downregulated in STAU1-depleted ERMS cells while it was upregulated in HSMM-C3 STAU1-depleted cells. These effects were due to significant alterations in the expression levels of OXPHOS proteins in STAU1-depleted cells (Fig. 3d). As shown in the representative volcano plots (Fig. 3d), protein levels of several OXPHOS genes were increased in non-transformed skeletal muscle cells (HSMM-C3), consistent with the observed upregulation of the OXPHOS pathway. In contrast, a group of OXPHOS proteins were downregulated in ERMS cells after STAU1 depletion, confirming the downregulation of the OXPHOS pathway in these cells (Fig. 3d). Similar to Fig. 3b, upregulated proteins in STAU1-knockdown cells are on the right side of the plots while downregulated ones are shown on the left side. Significantly altered protein levels are located above the horizontal line and shown in red. In these analyses, we noted that alterations in the protein levels of OXPHOS genes were more pronounced in RD cells compared to RH36, which is entirely consistent with the greater impact of STAU1-depletion on the proliferation and mitochondrial metabolism of RD cells (see above and Figs. 1 and 2).

STAU1 downregulation impacts expression of nuclear OXPHOS mRNAs

Approximately, 120 protein-expressing genes are responsible for OXPHOS in cells [30]. Of those, only 13 proteins are encoded by the mitochondrial circular genome [31]. To further explore the mechanisms involved in OXPHOS regulation in STAU1-depleted cells, we separately analyzed alterations in mRNA and protein levels of nuclear and mitochondrial OXPHOS genes in control and STAU1-depleted cells.

To determine the impact of STAU1-depletion on nuclear OXPHOS mRNAs, we initially focused on mRNAs whose encoded proteins showed altered expression (Fig. 3d). More specifically, we only investigated proteins with a statistically significant and high fold change in each cell line. Therefore, 27 upregulated proteins were studied for HSMM-C3 cells while 6 and 27 downregulated proteins were tested for RH36 and RD cells, respectively (Table 1). We evaluated changes in mRNA levels of selected OXPHOS targets by RT-qPCR (Fig. 4).

Table 1.

List of altered OXPHOS proteins in STAU1-depleted cell lines compared to parental control cells

HSMM-C3			RH36			RD
Protein	Log2 (Fold change)	− Log10(p value)	Protein	Log2 (Fold change)	− Log10(p value)	Protein	Log2 (Fold change)	− Log10(p value)
COX17	0.221	4.162	ATPAF1	− 0.144	2.262	COX4I1	− 0.237	4.062
NDUFV1	0.250	3.591	COX7A2L	− 0.194	1.890	NDUFS8	− 0.258	3.971
NDUFA12	0.312	3.138	NDUFB3	− 0.336	1.838	NDUFA12	− 0.508	3.260
NDUFB4	0.146	2.707	NDUFS6	− 0.130	1.729	NDUFS6	− 0.355	3.242
MT-ATP6	0.204	2.669	ATPAF1	− 0.144	2.262	YWHAB	− 0.912	3.188
UQCRC2	0.167	2.618	COX7A2L	− 0.194	1.890	CYC1	− 0.143	3.078
COX7A2L	0.136	2.515				COX11	− 0.118	2.766
NDUFS4	0.195	2.401				NDUFS2	− 0.149	2.638
UQCRH	0.059	2.366				UQCRC1	− 0.118	2.562
UQCR10	0.167	2.346				NDUFS3	− 0.227	2.431
UQCRC1	0.166	2.209				NDUFS7	− 0.201	2.381
COX7A2	0.106	2.111				COX15	− 0.264	2.207
NDUFA2	0.153	2.062				COX5A	− 0.218	2.180
NDUFS3	0.175	2.044				NDUFA9	− 0.293	2.116
NDUFB8	0.270	2.002				UQCRH	− 0.225	2.109
ATP5C1	0.244	1.940				NDUFB10	− 0.196	2.092
CYC1	0.084	1.924				NDUFA8	− 0.169	2.024
UQCRQ	0.165	1.865				NDUFA3	− 0.189	1.871
NDUFS1	0.105	1.856				NDUFA5	− 0.155	1.811
NDUFB10	0.136	1.788				NDUFB9	− 0.139	1.811
UQCRB	0.158	1.770				ATP5C1	0.329	1.770
NDUFS5	0.234	1.666				NDUFV2	− 0.154	1.733
NDUFAB1	0.094	1.617				NDUFB8	− 0.177	1.718
NDUFB7	0.130	1.582				NDUFAB1	− 0.189	1.704
COX17	0.221	4.162				SDHA	− 0.084	1.620
NDUFV1	0.250	3.591				NDUFB5	− 0.175	1.608
NDUFA12		3.138				NDUFS5	− 0.096	1.560

Open in a new tab

RT-qPCR data showed significant downregulations in mRNA levels of 4 and 11 nuclear OXPHOS genes in RH36 and RD cells, respectively (Fig. 4b, c). By contrast, mRNA levels of 5 OXPHOS genes were significantly elevated in HSMM-C3 STAU1-depeleted cells (Fig. 4a). Given the known role of STAU1 in regulating mRNA stability [14], we wondered whether these effects were caused by the impact of STAU1-depletion on the stability of OXPHOS mRNAs. Since the control of mRNA stability requires interactions between STAU1 and target mRNAs [32, 33], we speculated that STAU1 indeed binds to nuclear OXPHOS mRNAs. In this context, previous hiCLIP analyses indicated that STAU1 directly interacts with several OXPHOS (both mitochondrial and nuclear genes) mRNAs in HEK-293 cells including NDUFA9, NDUFA11, NDUFC2, and mt-ATP6 [33]. Here, we thus conducted RNA immunoprecipitation (RNA-IP or RIP) assays using ERMS and HSMM-C3 cells and STAU1-specific antibodies to assess the potential interactions between STAU1 and OXPHOS mRNAs (Fig. 4d, e). Western blot analysis of STAU1 showed the efficient IP of this protein with GAPDH being used as a loading control (Fig. 4d). Based on our previous findings, we monitored the presence of c-myc mRNAs in the IP as a positive control in this assay [34]. As shown in Fig. 4D, E, the data are presented as a ratio of mRNA in STAU1 immuno-precipitated samples compared to input (Fig. 4d, e). Altogether, these RIP assays identified 5, 3 and 10 nuclear OXPHOS mRNAs targeted by STAU1 in HSMM-C3, RH36 and RD cells, respectively (Fig. 4e).

Since some of these target mRNAs showed alterations in both protein and mRNA levels, we then examined whether STAU1 controls the stability of these target mRNAs. For this, cells with or without STAU1 depletion were treated with 10 µg/ml of actinomycin D for various periods of time, and the percentage of remaining mRNAs was evaluated at each time point to determine the stability of each target mRNA. As shown in Fig. 5, we noticed marked alterations in the half-life (T_1/2) of target mRNAs, thereby suggesting that STAU1-depletion modulates the stability of several nuclear OXPHOS mRNAs in each cell lines (Fig. 5a–c). More specifically, the accumulation of 3 OXPHOS mRNAs (CYC1, NDUFB7, and UQCRH) in STAU1-depleted HSMM-C3 was increased indicating that STAU1 depletion increases the stability of these mRNAs (Fig. 5a) [35]. Conversely, the stability data suggest that STAU1 depletion negatively impacts the stability of OXPHOS mRNAs in ERMS cell lines (Fig. 5b, c). More specifically, in RH36 cells, STAU1 depletion was associated with a reduced half-life for ATPAF1, COX7A2L, and NDUFB3 (Fig. 5b). Similarly, in RD cells, STAU1 depletion led to a decrease in the half-life of COX5A, NDUFA3, NDUFA9, NDUFB8, NDUFS6, and UQCRH mRNAs (Fig. 5c). These findings further supported the former observations on the differential regulation of gene expression by STAU1 in non-transformed and ERMS cell lines.

Fig. 5 — STAU1 interaction with the nuclear OXPHOS mRNAs impacts their stability. a HSMM-C3 and ERMS cells were treated with 10 µg/ml Actinomycin D for 30, 60, and 90, and 120 min and RT-qPCR assay was performed to determine the percentage of remaining mRNAs. The mRNA half-lives (t_1/2) were calculated with 95% confidence intervals using by non-linear regression-one phase decay analysis in GraphPad Prism 9.4.0 software (n = 3). Line graphs showing the percentage of remaining mRNAs in HSMMC3 cells. b RH36 cells, c RD cells

STAU1 downregulation impacts expression of mitochondrial-expressing OXPHOS genes

Out of 13 mitochondrial-expressing OXPHOS genes, only 4 proteins (mt-ND1, mt-CO2, mt-ATP6, and mt-ATP8) were detected in our proteomics experiment (Fig. 6a). More specifically, our results indicate that except for mt-CO2, levels of the other 3 proteins were altered after STAU1 depletion. Furthermore, RT-qPCR analysis confirmed these alterations at the mRNA levels in STAU1-depleted cells (Fig. 6b).

Next, a causal network constructed from the individual relationships between detected proteins was created in Ingenuity Pathway Analyzer (IPA) and used to create mechanistic hypotheses that explain the expression changes observed in our proteomics data (http://www.ingenuity.com) [36]. Using one of the causal analytics tools for downstream effect analysis and prediction of new targets, we predicted the expression level of other mitochondrial mRNAs in the OXPHOS pathway. Based on this analysis, additional mitochondrial mRNAs (ND2-ND5) were predicted to also have altered expression patterns in STAU1-depleted cells. More specifically, mitochondrial mRNAs were predicted to be downregulated in STAU1-depleted ERMS cells while being upregulated in STAU1-depleted HSMM-C3 cells.

To confirm these predictions, we performed RT-qPCR experiments to compare mRNA levels of mt-ND genes (ND2-ND5) across all cell lines (Fig. 6c). In agreement with the pathway prediction data, the RT-qPCR results showed that in HSMM-C3 cells, all mt-ND mRNAs were upregulated while in RD cells, they were all downregulated after STAU1 depletion. In RH36 cells however, only 3 out 5 mt-ND mRNAs were dysregulated (Fig. 6c). Altogether, these findings illustrate that STAU1 regulates the expression levels of mitochondrial genes, specifically, complex I mitochondrial proteins [37]. Moreover, this effect was distinct in malignant and non-transformed skeletal muscle cells.

To determine how STAU1 depletion impacts mRNA levels of the observed (ND1 and ATP6) and predicted (ND2-ND5) mitochondrial OXPHOS genes, we determined the potential interaction between these mRNAs and STAU1 proteins using RIP assays. As above, c-myc was used as a positive control for STAU1/mRNA interactions and the data are reported as the ratio of STAU1-bound mRNAs to input samples [34]. The results from the RIP assays revealed that STAU1 interacts with several OXPHOS mt-mRNAs in each cell line (Fig. 6d). More specifically, STAU1 interacts with ND3 and ND5 mRNAs in HSMM-C3 cells, ND4L mRNA in RH36 and ND3, ND4L, ND5 in RD cells (Fig. 6d). These findings suggest that STAU1 interaction with mt-mRNAs controls their stability.

To determine whether indeed STAU1 interactions with mt-mRNAs impact their stability, we performed RNA stability assays in the presence of 10 µg/ml actinomycin D. Our mRNA stability data confirmed that in ERMS cells, STAU1 depletion caused a significant reduction in the half-life of OXPHOS mt-mRNAs (ND3, ND4L, and ND5) while it increased their half-life in HSMM-C3 cells (ND3 and ND5) (Fig. 6e). In other words, STAU1 interaction with mt-mRNAs in ERMS cells enhances their stability. Accordingly, STAU1 depletion negatively impacts the stability and levels of these mRNAs. However, in HSMM-C3 cells, STAU1 expression induces mRNA decay, whereas its depletion inhibited degradation of mt-mRNAs thereby increasing their half-life (Fig. 6e). Together these observations highlight the differential role of STAU1 in non-transformed versus malignant cells, where STAU1 binding to the mt-ND mRNAs of non-transformed cells promotes their degradation but increases mRNA stability in malignant cells. Despite the previous report on the presence of a STAU1-binding site in the 3’UTR of mt-ATP6 mRNA [33], we did not detect an interaction between STAU1 and mt-ATP6 mRNA in the tested cell lines. Noteworthy, and consistent with previous work from others, the observed half-lives for most mitochondrial mRNAs were ~ 100 min [38, 39].

STAU1 depletion alters HIF2α pathway

Another IPA causal analytics tool called “Upstream Regulator Analysis” allowed us to identify a potential upstream regulator of OXPHOS gene expression following STAU1 depletion [36]. In other words, the upstream regulator functions as an intermediate between STAU1 and OXPHOS (Fig. 11). The IPA upstream regulator analysis identified 26 common regulatory proteins in all 3 cell lines. Out of 26, only three of them showed a similar trend in both ERMS cell lines and opposite trend in HSMM-C3. However, among the three predicted upstream regulators only Hypoxia-Inducible Factor 2α (HIF2α or EPAS1) was known to be involved in mitochondrial metabolism and cell survival. Therefore, we focused on defining the impact of HIF2α on the STAU1-mediated control of mitochondrial metabolism. More specifically, our proteomics data revealed an upregulation of the HIF2α pathway in STAU1-depleted HSMM-C3 cells associated with an increase in the protein levels of several HIF2α downstream targets, namely, CEBP1, HSPA5, SOD2, and CAT (Fig. 7a, b). Similar to what we observed with OXPHOS proteins and mRNAs however, opposite results were obtained with ERMS cells in which protein levels of HIF2α and downstream targets were decreased in STAU1-depleted cells (Fig. 7a, b). To determine if the alterations in the protein levels of HIF2α and its downstream targets in STAU1-depleted cells are associated with changes in their mRNA levels, we conducted RT-qPCR assays (Fig. 7c). Data from these experiments confirmed that STAU1 depletion differentially altered mRNA levels of HIF2α and its downstream targets in HSMM-C3 and ERMS cells.

Fig. 11 — Schematic presentation of STAU1-mediated regulation of mitochondrial metabolism in non-transformed and malignant myoblasts. Both endogenous and exogenous expression of STAU1 in non-transformed myoblasts stability of OXPHOS and HIF2α mRNAs, leading to reduction in the protein level of these markers in the cell. Reduced expression of OXPHOS proteins, involved in mitochondrial structure, biogenesis, and function, abolishes mitochondrial metabolism. In parallel, reduction in HIF2α level inhibits expression of its downstream target genes including those involved in mitochondrial function which further leads to inhibition of mitochondrial metabolism. This inhibition results in the blockade of cell growth. By contrast, elevated STAU1 expression in ERMS cells increases the stability of OXPHOS and HIF2α mRNAs, leading to accumulation of these proteins. Increased levels of OXPHOS proteins along with HIF2α-induced expression of mitochondrial-related proteins enhances mitochondrial metabolism. As a result, increased mitochondrial ATP production promotes cancer cell proliferation and induces oncogenesis (Figure is created with BioRender.com)

Fig.7 — STAU1 knockdown causes downregulation of HIF2α pathway via direct interaction with HIF2α mRNA. a Proteomics analysis of the HIF2α levels in HSMM-C3 and ERMS cell lines. Bar chart represents the fold change of HIF2α protein in STAU1-depleted cells compared to control cells. b Box chart indicates the protein fold change of several HIF2α downstream targets in STAU1-depleted versus control cells. The data obtained from the proteomic assay and were analyzed in Perseus. c RT-qPCR analysis of the mRNA levels of HIF2α and its downstream targets in HSMM-C3 and ERMS cells. d The precipitated RNAs obtained from an RNA co-IP assay using G (IgG) or STAU1-specific antibody were analyzed by RT-qPCR and HIF2α-specific primers. Fold precipitation relative to β-actin mRNA was calculated and presented as a bar graph (n = 3). e Cells were treated with 10 µg/ml actinomycin D for 30, 60, and 90, and 120 min and RT-qPCR assay was performed to determine the percentage of remaining mRNAs. The mRNA half-lives (t_1/2) were calculated with 95% confidence intervals using by non-linear regression-one phase decay analysis in GraphPad Prism 9.4.0 software (n = 3). Line graphs represents the percentage of remaining mRNAs in control and STAU1-depleted cells. Data are mean ± sd, t test and one-way ANOVA were performed for comparison ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

Direct binding of HIF2α to the hypoxia response elements in the promotor region of target genes activate their transcription and ultimately leads to increases in mRNA and protein expression [40, 41]. Our data showed that changes in HIF2α protein was accompanied by alterations in mRNA levels, suggesting that STAU1 downregulation somehow affected HIF2α mRNA levels. Interestingly, the opposite changes in HIF2α mRNA levels were observed in HSMM-C3 cells compared to ERMS cell line. To determine if STAU1 directly regulates HIF2α mRNAs, we examined the potential interaction of STAU1 with HIF2α mRNAs by RIP assay. Our data revealed an interaction between STAU1 protein and HIF2α mRNAs in all three cell lines, suggesting a direct role of STAU1 in the regulation of the HIF2α pathway (Fig. 7d). In agreement with our findings, a direct interaction of STAU1 protein with mRNAs of another member of the HIF family (HIF1α) has been previously reported by HiCLIP analysis [33].

Based on these findings, we also examined the impact of STAU1 depletion on the stability of HIF2α mRNAs by mRNA stability assays in the presence of 10 µg/ml actinomycin D. Interestingly, the mRNA stability data indicated a negative impact of STAU1 depletion on the half-life of HIF2α mRNAs in ERMS cells. However, an opposite observation was made with HSMM-C3 cells since STAU1 depletion increased the half-life of HIF2α mRNAs (Fig. 7e). These data showed for the first time that STAU1 directly controls the HIF2α pathway and that this effect is opposite in non-transformed and ERMS cells.

STAU1-mediated control of the HIF2α pathway regulates mitochondrial metabolism in ERMS cell lines

Given the known and crucial role of the HIF2α pathway in regulating mitochondrial function [42], we further investigated the link between STAU1 and HIF2α in ERMS cells. For this, we generated STAU1⁻/HIF2α⁺ cell lines using a combination of the Lentiviral CRISPR STAU1 knockout system with the Transient Lentiviral HIF2α overexpression strategy. More specifically, STAU1 knockout in ERMS cells was generated using LV-CRISPR-sg STAU1 transduction followed by puromycin selection. The efficacy of the knockout was examined by Western blot analysis (Fig. 8a). Cell viability and extracellular flux analyses were performed to confirm that STAU1 knockout (STAU1-KO) cells react similarly to STAU1 knockdown cells. Also, RT-qPCR analysis of OXPHOS markers further confirmed the functional similarity of the two STAU1-depleted cell models (data included in Figs. 9a–d and 10e, f). Next, using LV-pCDH-hHif2α-HA3 lentiviral particles, HIF2α was overexpressed in parental and STAU1 knockout ERMS cells. ERMS cells without any manipulations in STAU1 and/or HIF2α were shown as STAU1^wt and/or HIF2α^wt while STAU1-knockout and HIF2α overexpressing cells were shown as STAU1⁻ and HIF2α⁺, respectively.

Fig.8 — Overexpression of HIF2α in STAU1-depleted ERMS cell lines rescued mitochondrial function and cell viability, comparable with wildtype cells. a STAU1 was knockout (KO) using Lentiviral CRISPR system in ERMS cells and exogenous HIFα was overexpressed using Lentiviral PCDH-HIF2α particles. Western blot analysis of STAU1 in control and knockout cells, the statistical relevance is presented as bar chart. b The protein level of exogenous HIF2α in control and STAU1-KO cells. c mRNA fold change of endogenous and exogenous HIF2α in ERMS cells. d Mitochondrial respiration rate ATP production level were measured using the XF-96 extracellular flux analyser in the presence of 1 µM Oligomycin A (drug A), 2 µM FCCP (drug B), 1 µM Rotenone (drug C), and 1 µM Antimycin A (drug D). Data were analyzed by Seahorse Wave 2.6.1 software and plotted in GraphPad Prism 9.4.0 software (n = 4). e MTT viability analysis of ERMS cells in the presence and/or absence of STAU1 and/or HIF2α (n = 3). Data are mean ± sd, t test and one-way ANOVA were performed for comparison ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

Fig. 9 — HIF2α overexpression in STAU1 knockdown ERMS cells altered the mRNA expression of nuclear- and mitochondrial OXPHOS genes. a RT-qPCR analysis of the mRNA levels of nuclear OXPHOS genes in RH36 cells. mRNA symbols from left to right: ATPAF1, COX7A2L, NDUFB3, NDUFB6. b RT-qPCR analysis of the mRNA levels of mitochondrial OXPHOS genes in RH36 cells. mRNA symbols from left to right: ND1, ND2, ND3, ND4, ND4L, ND5, ATP6, ATP8. c RT-qPCR analysis of the mRNA levels of nuclear OXPHOS genes in RD cells. mRNA symbols from left to right: COX4I1, COX5A, COX6C, COX15, CYC1, NDUFA3, NDUFA5, NDUFA8, NDUFA9, NDUFA12, NDUFAB1, NDUFB5, NDUFB8, NDUFB9, NDUFB10, NDUFS3, NDUFS5, NDUFS6, NDUFS7, NDUFS8, NDUFV2. d RT-qPCR analysis of the mRNA levels of mitochondrial OXPHOS genes in RD cells. mRNA symbols from left to right: ND1, ND2, ND3, ND4, ND4L, ND5, ATP6, ATP8. Data are mean ± sd (n = 3), one-way ANOVA was performed for comparison between STAU1⁻/HIF2α^wt and STAU1⁻/HIF2α⁺ ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

Fig. 10 — Knockout of HIF2α in STAU1-depleted HSMM-C3 cells altered mitochondrial function, comparable with wildtype cells. a STAU1 and HIF2α were knockout (KO) using Lentiviral CRISPR system in HSMM-C3 cells. Western blot analysis of STAU1 in control and knockout cells, the statistical relevance is presented as bar chart. b mRNA fold change of endogenous HIF2α in ERMS cells. c Mitochondrial respiration rate ATP production level were measured using the XF-96 extracellular flux analyser in the presence of 1 µM Oligomycin A (drug A), 2 µM FCCP (drug B), 1 µM Rotenone (drug C), and 1 µM Antimycin A (drug D). Data were analyzed by Seahorse Wave 2.6.1 software and plotted in GraphPad Prism 9.4.0 software (n = 3). d MTT viability analysis of HSMM-C3 cells in the presence and/or absence of STAU1 and/or HIF2α (n = 3). e RT-qPCR analysis of the mRNA levels of nuclear OXPHOS genes in HSMM-C3 cells. mRNA symbols from left to right: ATP5C1, COX7A2, COX7A2L, CYC1, NDUFA2, NDUFA10, NDUFA12, NDUFAB1, NDUFB1, NDUFB4, NDUFB7, NDUFB8, NDUFB10, NDUFBS1, NDUFBS3, NDUFBS4, NDUFBS5, NDUFBV1, UQCR10, UQCRB, UQCRC1, UQCRC2, UQCRH, UQCRQ. f RT-qPCR analysis of the mRNA levels of and mitochondrial OXPHOS genes in HSMM-C3 cells. mRNA symbols from left to right: ND1, ND2, ND3, ND4, ND4L, ND5, ATP6, ATP8. Data are mean ± sd (n = 3), t test and one-way ANOVA were performed for comparison between STAU1⁻/HIF2α⁻ and STAU1⁻/HIF2α^wt ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

STAU1 and HIF2α expression levels were examined using Western blots and RT-qPCR (Fig. 8a–c). First, Western blot analyses confirmed HIF2α overexpression in STAU1-depleted ERMS cells (Fig. 8b). It is noteworthy that overexpression of HIF2α did not impact STAU1 level in ERMS cell lines (Fig. 8b). Due to the low abundance of endogenous HIF2α in ERMS cells, the HIF2α antibody was not able to detect the alterations (showed in proteomics data and in RT-qPCR analysis) in protein levels of endogenous HIF2α. Therefore, we employed RT-qPCR to detect changes in HIF2α mRNAs in STAU1-knockout cells. As a result, a significant difference between endogenous and exogenous HIF2α levels in ERMS cells before and after HIF2α overexpression was confirmed (Fig. 8c).

To define the relationship between STAU1 and HIF2α on ERMS cell metabolism, we studied the impact of HIF2α overexpression on the mitochondrial metabolism of STAU1^wt and STAU1⁻ ERMS cells. For this, extracellular flux analyses were performed in the presence of the mitochondrial inhibitors oligomycin A, FCCP, rotenone, and antimycin A. Our results showed that overexpression of HIF2α in STAU1-knockout ERMS cells improved mitochondrial OCR and ATP production equivalent to their levels in parental ERMS cells (STAU1^wt/HIF2α^wt) (Fig. 8d). More specifically, the observed reduction in the OCR and ATP levels after STAU1 knockout in ERMS cells was reversed following HIF2α overexpression. These levels were consistent with the observed OCR and ATP levels in parental ERMS cells before genetic modulation of STAU1 expression. In other words, mitochondrial metabolism was restored in STAU1-knockout ERMS cells by HIF2α overexpression. Interestingly, in the presence of STAU1, HIF2α overexpression negatively impacted mitochondrial function and cell viability. However, when combined with STAU1 knockout, mitochondrial function and cell viability returned to the levels seen in parental cells (Fig. 8d, e). These data emphasize the crucial role of the HIF2α pathway in ERMS mitochondrial metabolism in the presence and absence of STAU1. Additionally, MTT viability data showed that this improved mitochondrial function was associated with an increase in the viability of STAU1⁻/ HIF2α⁺ cells (Fig. 8e). Altogether, these results revealed that in the absence of STAU1, HIF2α overexpression reinstates mitochondrial metabolism and cell proliferation in ERMS cell lines.

Overexpression of HIF2α in STAU1 knockout ERMS cells alters mRNA levels of nuclear- and mitochondrial OXPHOS genes

Next, we examined the impact of HIF2α overexpression on mRNA levels of nuclear- and mitochondrial OXPHOS genes using RT-qPCR. Our data showed that HIF2α overexpression in STAU1 knockout ERMS cells affected OXPHOS mRNA expression (Fig. 9a–d). More specifically, no significant change was observed in the mRNA levels of nuclear OXPHOS genes after HIF2α overexpression in RH36 cells (Fig. 9a) (Table 2). However, mRNA expression of several mitochondrial OXPHOS genes including ND2, ND3, ND4, and ATP8, was increased in RH36 STAU1⁻/HIF2α⁺ cells compared to between STAU1⁻/HIF2α^wt, but not in RH36 cells with only HIF2α overexpression or STAU1^wt/HIF2α⁺ (Fig. 9b) (Table 2). These data suggest that HIF2α overexpression alone does not impact mitochondrial OXPHOS expression but its combination with STAU1 depletion modulates the mRNA levels of several mitochondrial OXPHOS genes, thereby highlighting the sensitivity of HIF2α function to STAU1 levels.

Table 2.

Fold change of OXPHOS mRNAs in ERMS cell lines presented in Fig. 9

Cell line	Gene location	mRNA symbol	mRNA fold change
			STAU1^WT/HIF2α^WT		STAU1^WT/HIF2α⁺		STAU1/HIF2α^WT		STAU1/HIF2α⁺
			Mean	Std	Mean	Std	Mean	Std	Mean	Std
RH36	Nucleus	ATPAF1	1.00	0.06	0.94	0.09	0.58	0.04	0.62	0.07
		COX7A2L	1.00	0.06	0.88	0.15	0.64	0.08	0.65	0.14
		NDUFB3	1.00	0.05	0.72	0.06	0.76	0.13	0.62	0.03
		NDUFB6	1.00	0.04	0.79	0.10	0.75	0.04	0.78	0.12
	Mitochondria	ND1	1.01	0.16	0.61	0.06	0.78	0.02	0.88	0.11
		ND2	1.00	0.06	0.94	0.10	0.56	0.05	1.62	0.23
		ND3	1.01	0.18	1.41	0.11	0.67	0.15	2.77	0.23
		ND4	1.01	0.12	1.76	0.08	1.13	0.13	1.58	0.04
		ND4L	1.00	0.05	0.73	0.04	0.69	0.08	0.74	0.07
		ND5	1.02	0.21	0.52	0.04	0.41	0.06	0.41	0.07
		ATP6	1.00	0.11	0.12	0.01	0.25	0.03	0.13	0.01
		ATP8	1.00	0.11	1.32	0.10	0.75	0.05	1.61	0.04
RD	Nucleus	COX4I1	1.01	0.12	1.29	0.09	0.77	0.01	0.54	0.06
		COX5A	1.00	0.06	1.43	0.23	0.93	0.22	1.58	0.19
		COX6C	1.03	0.31	1.99	0.39	1.23	0.11	2.96	0.98
		COX15	1.01	0.17	0.99	0.19	0.52	0.03	0.28	0.15
		CYC1	1.01	0.22	1.68	0.19	0.41	0.06	0.59	0.09
		NDUFA3	1.02	0.26	1.24	0.46	0.74	0.04	2.06	0.14
		NDUFA5	1.00	0.07	1.82	0.14	0.79	0.16	1.04	0.08
		NDUFA8	1.00	0.10	1.04	0.05	0.86	0.11	1.24	0.19
		NDUFA9	1.00	0.03	0.96	0.06	0.78	0.19	1.31	0.07
		NDUFA12	1.01	0.13	1.82	0.22	0.75	0.03	1.11	0.06
		NDUFAB1	1.00	0.09	2.55	0.16	0.35	0.02	0.02	0.01
		NDUFB5	1.00	0.05	0.84	0.07	0.97	0.03	2.18	0.15
		NDUFB8	1.01	0.14	1.27	0.12	0.54	0.09	0.30	0.03
		NDUFB9	1.00	0.08	1.36	0.23	0.80	0.03	0.81	0.06
		NDUFB10	1.01	0.13	1.21	0.11	0.70	0.11	0.72	0.13
		NDUFS3	1.01	0.23	1.03	0.05	0.48	0.12	1.07	0.14
		NDUFS5	1.01	0.11	1.78	0.25	0.36	0.04	0.19	0.03
		NDUFS6	1.00	0.13	1.54	0.07	0.41	0.06	0.13	0.01
		NDUFS7	1.00	0.06	0.62	0.05	0.06	0.03	0.01	0.00
		NDUFS8	1.00	0.08	1.47	0.10	0.17	0.04	0.08	0.03
		NDUFV2	1.06	0.44	3.91	0.67	0.07	0.04	0.04	0.02
	Mitochondria	ND1	1.01	0.20	0.53	0.04	0.72	0.03	0.89	0.21
		ND2	1.00	0.12	0.70	0.09	0.63	0.03	1.11	0.15
		ND3	1.01	0.11	0.80	0.05	0.84	0.03	1.54	0.08
		ND4L	1.00 lePara>	0.04	0.73	0.10	0.70	0.02	1.40	0.08
		ND4L	1.00	0.08	0.60	0.05	0.68	0.03	0.78	0.03
		ND5	1.00	0.04	0.50	0.05	0.79	0.07	0.87	0.05
		ATP6	1.00	0.07	0.57	0.04	0.64	0.03	0.20	0.01

Open in a new tab

Similarly, in STAU1 knockout RD cells, HIF2α overexpression was associated with significant increases in the mRNA levels of both nuclear- and mitochondrial OXPHOS genes (Fig. 9c, d). More, specifically, mitochondrial OXPHOS mRNAs, such as ND2, ND3, ND4L, and ATP8, were significantly upregulated after HIF2α overexpression in STAU1 knockout RD cells (Fig. 9d, Table 2). Similarly, mRNA levels of several nuclear OXPHOS genes including COX5A, COX6C, NDUFA3, NDUFA9, NDUFB5, and NDUFS3, were all upregulated in STAU1⁻/HIF2α⁺ cells (Fig. 9c) (Table 2). Overall, these findings indicate that reduction of HIF2α levels in STAU1 knockout ERMS cells plays a role in the downregulation of OXPHOS mRNA and protein expression. Therefore, exogenous expression of HIF2α in STAU1-knockout cells increases mRNA levels of several OXPHOS genes and improved mitochondrial metabolism together with cell viability (Figs. 8, 9).

HIF2α differentially regulates mitochondrial metabolism and OXPHOS gene expression in non-malignant skeletal muscle cells

We also studied the impact of HIF2α levels on mitochondrial metabolism and expression of OXPHOS genes in HSMM-C3 cells. Since STAU1 depletion increased protein and mRNA expression of HIF2α in these cells, we investigated the impact of HIF2α downregulation in STAU1 knockout HSMM-C3 cells. To this end, we generated STAU1⁻/HIF2α⁻ cell lines using Lentiviral CRISPR Knockout system. STAU1 and HIF2α expression levels were examined using Western blot or RT-qPCR, respectively (Fig. 10a, b). The data confirmed greater than 60% reduction in protein and mRNA levels of STAU1 as well as of HIF2α mRNA levels.

The effect of HIF2α knockout on mitochondrial function in these cells was assessed by examining mitochondrial respiration and ATP production with and without HIF2α depletion. Extracellular flux analyses were conducted for the three genetically modified (STAU1⁻/HIF2α^wt, STAU1^wt/HIF2α⁻, STAU1⁻/HIF2α⁻) and the HSMM-C3 parental (STAU1^wt/HIF2α^wt) cell lines in the presence of mitochondrial inhibitors. Extracellular flux analyses showed that knockout of HIF2α in STAU1 knockout cells downregulated mitochondrial function, as shown by a significant reduction in OCR and ATP levels in STAU1⁻/HIF2α⁻ versus STAU1⁻/HIF2α^wt cells (Fig. 10c). Collectively, our data showed that STAU1 depletion increased mitochondrial function while HIF2α depletion reduced it. Therefore, the combination of STAU1 and HIF2α depletion returned mitochondrial function to a level similar to that seen in wildtype HSMM-C3 cells.

Despite STAU1 depletion, HIF2α depletion alone impacted the viability of HSMM-C3 cells as indicated by the reduction in the ratio of viable cells in a MTT assay (Fig. 10d). These data support the findings on the reduced mitochondrial function in HIF2α-depleted HSMM-C3 cells, (STAU1^wt/HIF2α⁻: Fig. 10d). However, in combination with STAU1 depletion, cell viability returned to the levels seen in HSMM-C3 parental cells (Fig. 10d), which is consistent with the changes observed in mitochondrial function in STAU1⁻/HIF2α⁻ cells (Fig. 10c). Collectively, the data show that combining STAU1 and HIF2α depletions did not affect mitochondrial function and viability of HSMM-C3 cells compared to HSMM-C3 parental cells.

We also determined the impact of HIF2α knockout on mRNA expression of nuclear- and mitochondrial OXPHOS genes using RT-qPCR. Our data indicate dysregulation of several nuclear OXPHOS mRNAs in STAU1⁻/HIF2α⁻ compared to STAU1⁻/HIF2α^wt cells. More specifically, mRNA levels of CYC1, NDUFB4, NDUFB10, UQCRB, and UQCRH, were reduced after HIF2α depletion in STAU1⁻ cells while COX7A2L, NDUFA2, NDUFA10 and NDUFS4, were upregulated in STAU1⁻/HIF2α⁻ cells (Fig. 10e, Table 3). Additionally, several mitochondrial OXPHOS mRNAs (ND1, ND2, ND4, ND4L, ND5, and ATP8) were downregulated in STAU1⁻/HIF2α⁻ cells (Fig. 10f, Table 3). These findings further emphasize the role of HIF2α in the regulation of mitochondrial genes in STAU1-KO cells. Nonetheless our data also indicate that in healthy HSMM-C3 cells, these changes do not specifically affect mitochondrial metabolism and cell survival. Indeed, STAU1 depletion protects HSMM-C3 cells from the negative impact of HIF2α depletion on mitochondrial function and cell survival. These findings are thus consistent with our previous studies suggesting that STAU1 modulation does not impact survival of non-transformed skeletal muscle cells, thereby limiting its impact to cancer cells which further highlights STAU1’s therapeutic potential [13].

Table 3.

Fold change of OXPHOS mRNAs in HSMM-C3 cell lines presented in Fig. 10

Cell line	Gene location	mRNA symbol	mRNA fold change
			STAU1^WT/HIF2α^WT		STAU1^WT/HIF2α⁻		STAU1⁻/HIF2α^WT		STAU1⁻/HIF2α⁻
			Mean	Std	Mean	Std	Mean	Std	Mean	Std
HSMM-C3	Nucleus	ATP5C1	1.00	0.04	1.35	0.07	1.12	0.17	1.36	0.24
		COX7A2	1.02	0.21	0.50	0.21	0.90	0.05	1.18	0.16
		COX7A2L	1.00	0.06	2.02	0.09	1.28	0.05	2.36	0.14
		CYC1	1.01	0.14	1.87	0.34	1.67	0.07	1.12	0.08
		NDUFA2	1.01	0.12	1.25	0.10	0.83	0.06	1.27	0.20
		NDUFA10	1.00	0.10	1.24	0.18	0.81	0.06	1.32	0.10
		NDUFA12	1.03	0.30	1.37	0.13	0.93	0.17	0.93	0.08
		NDUFAB1	1.03	0.30	1.37	0.13	1.00	0.07	0.93	0.08
		NDUFB1	1.00	0.03	1.26	0.10	0.88	0.06	1.09	0.02
		NDUFB4	0.89	0.37	0.59	0.46	0.88	0.05	0.48	0.16
		NDUFB7	1.00	0.10	1.58	0.11	1.72	0.10	1.42	0.04
		NDUFB8	1.01	0.19	1.38	0.28	0.88	0.03	1.00	0.09
		NDUFB10	1.00	0.09	1.42	0.24	1.63	0.04	0.76	0.05
		NDUFBS1	1.00	0.09	1.42	0.24	0.91	0.05	0.76	0.05
		NDUFBS3	1.00	0.13	1.31	0.12	0.72	0.01	0.87	0.08
		NDUFBS4	1.00	0.05	1.12	1.00	0.78	0.07	1.20	0.14
		NDUFBS5	1.00	0.05	1.20	0.16	0.82	0.09	0.62	0.17
		NDUFBV1	1.01	0.13	1.35	0.09	0.85	0.02	0.90	0.20
		UQCR10	1.03	0.29	1.65	0.05	0.93	0.07	0.95	0.34
		UQCRB	1.00	0.03	1.32	0.21	1.46	0.04	0.98	0.16
		UQCRC1	1.00	0.04	1.42	0.26	0.85	0.19	0.80	0.03
		UQCRC2	1.00	0.04	1.22	0.15	0.75	0.06	1.03	0.29
		UQCRH	1.00	0.08	1.23	0.12	1.67	0.03	0.65	0.06
		UQCRQ	1.00	0.08	1.06	0.08	0.81	0.11	0.94	0.18
	Mitochondria	ND1	1.00	0.03	0.91	0.18	1.59	0.03	0.76	0.19
		ND2	1.00	0.00	1.33	0.18	1.99	0.05	1.29	0.25
		ND3	1.00	0.10	1.54	0.15	1.60	0.14	1.50	0.21
		ND4	1.01	0.20	1.07	0.14	2.07	0.13	0.46	0.07
		ND4L	1.00	0.10	1.23	0.17	1.68	0.07	0.66	0.14
		ND5	1.01	0.16	1.15	0.20	1.49	0.08	0.96	0.11
		ATP6	1.01	0.10	1.39	0.29	1.35	0.05	1.07	0.05

Open in a new tab

Discussion

In the present study, we unravel the role of STAU1 in ERMS oncogenesis via regulation of mitochondrial function. More specifically, our findings show for the first time that the elevated levels of STAU1 in ERMS are associated with an upregulation of OXPHOS. Depletion of STAU1 in ERMS results in OXPHOS inhibition and, importantly, to suppression of cancerous cell proliferation. These effects were reflected by a marked reduction in mitochondrial respiration rate and in the levels of OXPHOS proteins. Moreover, STAU1 depletion also caused a downregulation of HIF2α, thereby abolishing ERMS cells’ response to mitochondrial dysfunction and further limiting their growth (Fig. 11). Exogenous expression of HIF2α revived the hypoxic response and ameliorated mitochondrial function and cell viability in ERMS cells. By contrast, STAU1 depletion enhanced OXPHOS and HIF2α signaling in non-transformed skeletal muscle cells without affecting cell viability. Mechanistically, our data further revealed that STAU1 differentially regulates the stability of OXPHOS and HIF2α mRNAs in ERMS versus non-transformed cells. Taken together, these results highlight the distinct and cancer-specific role of STAU1 in ERMS.

Our findings are consistent with previous work describing the importance of cellular metabolism in RMS progression [11, 43]. Cancer metabolism mainly relies on anaerobic metabolism of glucose (i.e., glycolysis) rather than mitochondrial OXPHOS [44]. However, skeletal muscle cancer cells tend to use both metabolic pathways for enhanced production of ATP [45]. In this context, a study by Fan et al. used ¹³C isotopomer examined various forms of metabolism in ARMS and non-transformed myoblasts. Their results showed a marked difference in the metabolic switch between ARMS and non-transformed myoblasts where active glycolysis generates the required precursors for the tricarboxylic acid (TCA) cycle [46] and further enhances mitochondrial metabolism. Coactivation of both metabolic pathways supports the elevated energy demands of highly proliferating ARMS cells, which led the authors to postulate that this phenomenon is an adaptive strategy for ARMS cells [47].

Similarly, elevated mitochondrial metabolism has been reported as a driving force for ERMS oncogenesis [8]. In this context, a recent study revealed that upregulation of Mitochondrial Calcium Uniporter (MCU) increased mitochondrial calcium uptake and enhanced mitochondrial metabolism and ROS production leading to elevated proliferation of ERMS cells. Therefore, knockdown of MCU reduced mitochondrial OXPHOS and inhibited ERMS growth [48]. These findings are consistent with previous studies showing the pivotal role of mitochondrial metabolism in ERMS progression while also highlighting the therapeutic potential of mitochondrial targeting for ERMS treatment [8, 49–51]. In this context, the observed opposite effects of STAU1 depletion in non-transformed and cancer cell lines, seen here, can be due to its link with elevated OXPHOS in ERMS cells compared with non-transformed cells [11]. The current findings are coherent with our previous results showing the differential role of STAU1 in the control of autophagy in ARMS and non-transformed cells [52].

The hypoxia induced by the elevated mitochondrial metabolism in ERMS cells is known to upregulates the HIF pathway [10, 11]. Working in concert, the upregulated mitochondrial metabolism and HIF pathway enhance ERMS progression under hypoxic conditions [11]. More specifically, upregulation of HIF transcription factors induces mRNA expression of over 100 target genes involved in the response to hypoxia, thereby allowing survival of ERMS cells under low oxygen tension [11]. The 3 isoforms of HIF (HIF1, HIF2, and HIF3) are dimeric proteins consisting of HIF-α and HIF-β subunits. While the HIF-β is constitutively expressed in the nucleus, HIF-α levels increase upon hypoxia. The direct binding of HIF dimers to the conserved sequences (hypoxia response elements or HREs) in the promotor region of HIF-regulated genes activates their transcription that leads to the activation of the hypoxic response and cell survival [40, 41]. More specifically, expression of several HIF targets including Superoxide desmolase SOD2 and CAT, plays essential roles in preventing oxidative stress through conversion of superoxide radicals (O²⁻) and hydrogen peroxide (H₂O₂) into water. As the main source of O²⁻ production in the cell, mitochondrial function heavily relies on the activity of the HIF pathway [53–55]. Therefore, the observed crosstalk between OXPHOS and HIF2α pathways in ERMS cell lines show that it can play an essential role in ERMS progression.

Our data showed a negative impact of HIF2α knockout on ERMS oncogenesis, regardless of the state of STAU1 expression. These findings are consistent with previous research in different types of cancers, including liver [56], renal [57], and breast cancers [58]. Moreover, a growing number of HIF targets are emerging as being directly or indirectly influence by mitochondrial biology given that a reciprocal relationship between mitochondria and the HIF pathway has been reported [53]. For instance, pharmacological inhibition of mitochondria blocks HIF2 induction during hypoxia, suggesting the negative impact of mitochondrial dysfunction on the HIF2 pathway [59]. Although the role of HIF1 in controlling mitochondrial function has been well established, controversial observations have been reported on the impact of HIF2α on mitochondrial function which may be partly due to a lack of sufficient studies on HIF2α compared to HIF1α [60]. Also, in some cases, opposite roles have been reported for HIF1α and HIF2α in specific cancers [61]. Given this, it seems that a better understanding of the independent and cancer-specific role of HIF2α in mitochondrial function and cell survival will require further studies. Here, we nonetheless report for the first time the negative impact of HIF2α depletion on mitochondrial OXPHOS and survival of ERMS cells, which can be a cancer-dependent effect and a novel role for HIF2α in ERMS. It is noteworthy that despite several differences in the expression of OXPHOS genes in RH36 and RD cell lines, the overall impact on the OXPHOS pathway was rather similar in both cell lines. The observed variations can be due to differences in the molecular characteristics of the two cell lines. Unfortunately, due to the limited information available on the molecular characteristics of RH36 cells, we are not able to further speculate on the observed variations [26].

Interestingly, our findings indicate also that the role of STAU1 as an intermediate controlling both events and creates a balance between HIF2α and OXPHOS in ERMS. Also, the distinct interaction between STAU1, HIF2α and OXPHOS likely explains the opposite observations in ERMS and non-transformed cells.

The opposite roles of STAU1 in ERMS and non-transformed cells can, at least partially, be attributed to the differential impacts of STAU1 on mRNA stability according to the cellular context (Fig. 11). More specifically, STAU1 interactions with target mRNAs regulate different aspects of mRNA metabolism including stability, decay, translation, and splicing [14]. In this context, we have previously reported the important role of STAU1 in autophagy regulation in ARMS through stabilization of the autophagy-related mRNAs Beclin-1 and ATG16L1 [13]. Although the precise molecular events causing stabilization or destabilization of targeted mRNAs in cancerous versus non-cancerous cells remain elusive, it has been proposed that STAU1 is central in a mechanism referred to as STAU1-mediated mRNA decay (SMD) [15]. During SMD, STAU1 proteins bind to the STAU1-binding site (SBS) within the 3′-UTR of target mRNAs and recruit the RNA degradation complex containing UPF1 RNA helicase [35]. STAU1 interactions with a target mRNA can determine its faith, not only based on the location of the SBS and recruited protein complexes but also, on the state of the cell and complement of proteins expressed in different cell types that affects STAU1 activity [14]. In the present study, STAU1 interactions with OXPHOS and HIF2α mRNAs differentially affected their fate in ERMS versus non-transformed cells indicating that the molecular events associated with STAU1 binding to target mRNAs are specific in these cell types, resulting in contrasting effects and likely reflecting differences in the protein complements expressed in the various cells as evidenced by our proteomics data.

Here, we revealed a novel and differential role of STAU1 in mitochondrial metabolism of cancer and non-malignant cells. This effect is partly due to the direct interaction between STAU1 protein and mitochondrial- and nuclear OXPHOS mRNAs. Briefly, STAU1 interaction enhances the stability of OXPHOS mRNA in ERMS cell lines while inducing mRNA decay in non-transformed cells. In addition, we also uncovered the role of STAU1 in controlling the HIF2α pathway, a main regulator of the hypoxic response [62]. This effect is caused by direct regulation of STAU1 on HIF2α mRNAs, thereby augmenting HIF2α protein levels and impacting expression of HIF2α downstream targets, such as superoxide desmolase (SOD2) and catalase (CAT) [63, 64]. More specifically, STAU1 promotes stabilization of HIF2α mRNAs in ERMS cells while inducing their degradation in non-transformed cells. Therefore, STAU1 downregulation exerts opposite effects on the activity of HIF2α pathway in ERMS versus non-transformed cells. In agreement with our findings on the STAU1 impact on OXPHOS pathway, the opposite effect of STAU1 on HIF2α mRNA led to a differential regulation of the HIF2α pathway in ERMS and non-transformed cells.

Based on a series of recently published studies, it is becoming evident that STAU1 is an emerging and key regulator of oncogenesis in various cancers including RMS, glioma, and prostate [12, 19, 65, 66]. Our findings on the novel role of STAU1 in cancer metabolism further validate its critical importance in cancer progression. Therefore, STAU1-targeting may be viewed as an attractive therapeutic approach for a variety of cancers and their treatment. Accordingly, greater understanding of the mechanistic role of STAU1 in cancer progression may pave the way for the development of novel STAU1-based anti-cancer therapies.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 316 KB)^{(315KB, pdf)}

Acknowledgements

We thank Dr. Pantic (University of Padua, Italy) for providing HSMM-C2 and HSMM-C3 cell lines. This work was supported by grants from the Cancer Research Society (CRS).

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SA and SSE. The first draft of the manuscript was written by SA and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Canadian Cancer Research Society [Grant number 24303].

Data availability

The data underlying this article are available in the article and in its online supplementary material. “The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [28] partner repository with the dataset identifier PXD042156”.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1.Rudzinski ER, Anderson JR, Chi Y-Y, et al. Histology, fusion status, and outcome in metastatic rhabdomyosarcoma: a report from the Children’s Oncology Group. Pediatr Blood Cancer. 2017 doi: 10.1002/pbc.26645. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Wang C. Childhood rhabdomyosarcoma: recent advances and prospective views. J Dent Res. 2012;91:341–350. doi: 10.1177/0022034511421490. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Hettmer S, Wagers AJ. Muscling in: uncovering the origins of rhabdomyosarcoma. Nat Med. 2010;16:171–173. doi: 10.1038/nm0210-171. [DOI] [PubMed] [Google Scholar]
4.Sarkar D, Ray S, Saha M, et al. Alveolar rhabdomyosarcoma with multiple distal metastases. A case report and review of literature. BMJ Case Rep. 2012 doi: 10.1136/bcr-2012-006523. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Galili N, Davis RJ, Fredericks WJ, et al. Fusion of a fork head domain gene to PAX3 in the solid tumour alveolar rhabdomyosarcoma. Nat Genet. 1993;5:230–235. doi: 10.1038/ng1193-230. [DOI] [PubMed] [Google Scholar]
6.Skapek SX, Ferrari A, Gupta AA, et al. Rhabdomyosarcoma. Nat Rev Dis Prim. 2019;5:1. doi: 10.1038/s41572-018-0051-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Oberlin O, Rey A, Lyden E, et al. Prognostic factors in metastatic rhabdomyosarcomas: results of a pooled analysis from United States and European cooperative groups. J Clin Oncol. 2008;26:2384–2389. doi: 10.1200/JCO.2007.14.7207. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Chen X, Stewart E, Shelat AA, et al. Targeting oxidative stress in embryonal rhabdomyosarcoma. Cancer Cell. 2013;24:710–724. doi: 10.1016/j.ccr.2013.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Solaini G, Baracca A, Lenaz G, Sgarbi G. Hypoxia and mitochondrial oxidative metabolism. Biochim Biophys Acta. 2010;1797:1171–1177. doi: 10.1016/j.bbabio.2010.02.011. [DOI] [PubMed] [Google Scholar]
10.Chandel NS, Maltepe E, Goldwasser E, et al. Mitochondrial reactive oxygen species trigger hypoxia-induced transcription. Proc Natl Acad Sci USA. 1998;95:11715–11720. doi: 10.1073/pnas.95.20.11715. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Monti E, Fanzani A. Uncovering metabolism in rhabdomyosarcoma. Cell Cycle. 2016;15:184–195. doi: 10.1080/15384101.2015.1071746. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Crawford Parks TE, Marcellus KA, Langill J, et al. Novel Roles for Staufen1 in Embryonal and Alveolar Rhabdomyosarcoma via c-myc-dependent and -independent events. Sci Rep. 2017;7:42342. doi: 10.1038/srep42342. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Almasi S, Parks TEC, Ravel-Chapuis A, et al. Differential regulation of autophagy by STAU1 in alveolar rhabdomyosarcoma and non-transformed skeletal muscle cells. Cell Oncol (Dordr) 2021;44:851–870. doi: 10.1007/s13402-021-00607-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Almasi S, Jasmin BJ. The multifunctional RNA-binding protein Staufen1: an emerging regulator of oncogenesis through its various roles in key cellular events. Cell Mol Life Sci. 2021;78:7145–7160. doi: 10.1007/s00018-021-03965-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Park E, Maquat LE. Staufen-mediated mRNA decay. Wiley Interdiscip Rev RNA. 2013;4:423–435. doi: 10.1002/wrna.1168. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Gonzalez Quesada Y, Bonnet-Magnaval F, DesGroseillers L. Phosphomimicry on STAU1 Serine 20 impairs STAU1 posttranscriptional functions and induces apoptosis in human transformed cells. Int J Mol Sci. 2022 doi: 10.3390/ijms23137344. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Gonzalez Quesada Y, DesGroseillers L. A degradation motif in STAU1 defines a novel family of proteins involved in inflammation. Int J Mol Sci. 2022;23:11588. doi: 10.3390/ijms231911588. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Haimovich G, Medina DA, Causse SZ, et al. Gene expression is circular: factors for mRNA degradation also Foster mRNA synthesis. Cell. 2013;153:1000–1011. doi: 10.1016/j.cell.2013.05.012. [DOI] [PubMed] [Google Scholar]
19.Marcellus KA, Crawford Parks TE, Almasi S, Jasmin BJ. Distinct roles for the RNA-binding protein Staufen1 in prostate cancer. BMC Cancer. 2021;21:120. doi: 10.1186/s12885-021-07844-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Paul S, Dansithong W, Figueroa KP, et al. Staufen1 in human neurodegeneration. Ann Neurol. 2021;89:1114–1128. doi: 10.1002/ana.26069. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Damas ND, Marcatti M, Côme C, et al. SNHG5 promotes colorectal cancer cell survival by counteracting STAU1-mediated mRNA destabilization. Nat Commun. 2016;7:13875. doi: 10.1038/ncomms13875. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Bonnet-Magnaval F, Diallo LH, Brunchault V, et al. High level of Staufen1 expression confers longer recurrence free survival to non-small cell lung cancer patients by promoting THBS1 mRNA degradation. Int J Mol Sci. 2021;23:215. doi: 10.3390/ijms23010215. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Bonnet-Magnaval F, DesGroseillers L. The Staufen1-dependent cell cycle regulon or how a misregulated RNA-binding protein leads to cancer. Biol Rev Camb Philos Soc. 2021;96:2192–2208. doi: 10.1111/brv.12749. [DOI] [PubMed] [Google Scholar]
24.Ghram M, Bonnet-Magnaval F, Hotea DI, et al. Staufen1 is essential for cell-cycle transitions and cell proliferation via the control of E2F1 expression. J Mol Biol. 2020;432:3881–3897. doi: 10.1016/j.jmb.2020.04.016. [DOI] [PubMed] [Google Scholar]
25.Pantic B, Borgia D, Giunco S, et al. Reliable and versatile immortal muscle cell models from healthy and myotonic dystrophy type 1 primary human myoblasts. Exp Cell Res. 2016;342:39–51. doi: 10.1016/j.yexcr.2016.02.013. [DOI] [PubMed] [Google Scholar]
26.Hinson ARP, Jones R, Crose LES, et al. Human rhabdomyosarcoma cell lines for rhabdomyosarcoma research: utility and pitfalls. Front Oncol. 2013;3:183. doi: 10.3389/fonc.2013.00183. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Klages N, Zufferey R, Trono D. A stable system for the high-titer production of multiply attenuated lentiviral vectors. Mol Ther. 2000;2:170–176. doi: 10.1006/mthe.2000.0103. [DOI] [PubMed] [Google Scholar]
28.Perez-Riverol Y, Bai J, Bandla C, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50:D543–D552. doi: 10.1093/nar/gkab1038. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Gehlenborg N, Wong B. Heat maps. Nat Methods. 2012;9:213–213. doi: 10.1038/nmeth.1902. [DOI] [PubMed] [Google Scholar]
30.Tang JX, Thompson K, Taylor RW, Oláhová M. Mitochondrial OXPHOS biogenesis: co-regulation of protein synthesis, import, and assembly pathways. Int J Mol Sci. 2020;21:3820. doi: 10.3390/ijms21113820. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Park D, Lee S, Min K-T. Techniques for investigating mitochondrial gene expression. BMB Rep. 2020;53:3–9. doi: 10.5483/BMBRep.2020.53.1.272. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Furic L, Maher-Laporte M, DesGroseillers L. A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1- and Staufen2-containing ribonucleoprotein complexes. RNA. 2008;14:324–335. doi: 10.1261/rna.720308. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Sugimoto Y, Vigilante A, Darbo E, et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature. 2015;519:491–494. doi: 10.1038/nature14280. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Crawford Parks TE, Marcellus KA, Péladeau C, et al. Overexpression of Staufen1 in DM1 mouse skeletal muscle exacerbates dystrophic and atrophic features. Hum Mol Genet. 2020;29:2185–2199. doi: 10.1093/hmg/ddaa111. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Gong C, Kim YK, Woeller CF, et al. SMD and NMD are competitive pathways that contribute to myogenesis: effects on PAX3 and myogenin mRNAs. Genes Dev. 2009;23:54–66. doi: 10.1101/gad.1717309. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Krämer A, Green J, Pollard J, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30:523–530. doi: 10.1093/bioinformatics/btt703. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Sharma LK, Lu J, Bai Y. Mitochondrial respiratory complex I: structure, function and implication in human diseases. Curr Med Chem. 2009;16:1266–1277. doi: 10.2174/092986709787846578. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Chujo T, Ohira T, Sakaguchi Y, et al. LRPPRC/SLIRP suppresses PNPase-mediated mRNA decay and promotes polyadenylation in human mitochondria. Nucleic Acids Res. 2012;40:8033–8047. doi: 10.1093/nar/gks506. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Nagao A, Hino-Shigi N, Suzuki T. Chapter 23 measuring mRNA decay in human mitochondria. Amsterdam: Elsevier; 2008. pp. 489–499. [DOI] [PubMed] [Google Scholar]
40.Burslem GM, Kyle HF, Nelson A, et al. Hypoxia inducible factor (HIF) as a model for studying inhibition of protein-protein interactions. Chem Sci. 2017;8:4188–4202. doi: 10.1039/c7sc00388a. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Orrenius S, Gogvadze V, Zhivotovsky B. Mitochondrial oxidative stress: implications for cell death. Annu Rev Pharmacol Toxicol. 2007;47:143–183. doi: 10.1146/annurev.pharmtox.47.120505.105122. [DOI] [PubMed] [Google Scholar]
42.Bao X, Zhang J, Huang G, et al. The crosstalk between HIFs and mitochondrial dysfunctions in cancer development. Cell Death Dis. 2021;12:215. doi: 10.1038/s41419-021-03505-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Kuhnt T, Pelz T, Qu X, et al. Mitochondrial OXPHOS Functions in R1H Rhabdomyosarcoma and Skeletal Muscles of the Rat. Neurochem Res. 2007;32:973–980. doi: 10.1007/s11064-006-9254-0. [DOI] [PubMed] [Google Scholar]
44.DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7:11–20. doi: 10.1016/j.cmet.2007.10.002. [DOI] [PubMed] [Google Scholar]
45.Fell D, Athel C-B. Understanding the control of metabolism. London: Portland Press; 1997. [Google Scholar]
46.Alabduladhem TO, Bordoni B (2022) Physiology, Krebs Cycle [PubMed]
47.Fan TW, Kucia M, Jankowski K, et al. Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes. Mol Cancer. 2008;7:79. doi: 10.1186/1476-4598-7-79. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Chiu HY, Loh AHP, Taneja R. Mitochondrial calcium uptake regulates tumour progression in embryonal rhabdomyosarcoma. Cell Death Dis. 2022;13:419. doi: 10.1038/s41419-022-04835-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Heinicke U, Kupka J, Fichter I, Fulda S. Critical role of mitochondria-mediated apoptosis for JNJ-26481585-induced antitumor activity in rhabdomyosarcoma. Oncogene. 2016;35:3729–3741. doi: 10.1038/onc.2015.440. [DOI] [PubMed] [Google Scholar]
50.Zhang M, Linardic CM, Kirsch DG. RAS and ROS in Rhabdomyosarcoma. Cancer Cell. 2013;24:689–691. doi: 10.1016/j.ccr.2013.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Yang L, Kong D, He M, et al. MiR-7 mediates mitochondrial impairment to trigger apoptosis and necroptosis in Rhabdomyosarcoma. Biochim Biophys Acta - Mol Cell Res. 2020;1867:118826. doi: 10.1016/j.bbamcr.2020.118826. [DOI] [PubMed] [Google Scholar]
52.Almasi S, Kennedy BE, El-Aghil M, et al. TRPM2 channel-mediated regulation of autophagy maintains mitochondrial function and promotes gastric cancer cell survival via the JNK-signaling pathway. J Biol Chem. 2018;293:3637–3650. doi: 10.1074/jbc.M117.817635. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Thomas LW, Ashcroft M. Exploring the molecular interface between hypoxia-inducible factor signalling and mitochondria. Cell Mol Life Sci. 2019;76:1759–1777. doi: 10.1007/s00018-019-03039-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Patel SA, Simon MC. Biology of hypoxia-inducible factor-2alpha in development and disease. Cell Death Differ. 2008;15:628–634. doi: 10.1038/cdd.2008.17. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Flynn JM, Melov S. SOD2 in mitochondrial dysfunction and neurodegeneration. Free Radic Biol Med. 2013;62:4–12. doi: 10.1016/j.freeradbiomed.2013.05.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Shneor D, Folberg R, Pe’er J,, et al. Stable knockdown of CREB, HIF-1 and HIF-2 by replication-competent retroviruses abrogates the responses to hypoxia in hepatocellular carcinoma. Cancer Gene Ther. 2017;24:64–74. doi: 10.1038/cgt.2016.68. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Bertout JA, Majmundar AJ, Gordan JD, et al. HIF2α inhibition promotes p53 pathway activity, tumor cell death, and radiation responses. Proc Natl Acad Sci. 2009;106:14391–14396. doi: 10.1073/pnas.0907357106. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Li NA, Wang H, Zhang J, Zhao E. Knockdown of hypoxia inducible factor-2α inhibits cell invasion via the downregulation of MMP-2 expression in breast cancer cells. Oncol Lett. 2016;11:3743–3748. doi: 10.3892/ol.2016.4471. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Brown ST, Nurse CA. Induction of HIF-2alpha is dependent on mitochondrial O2 consumption in an O2-sensitive adrenomedullary chromaffin cell line. Am J Physiol Cell Physiol. 2008;294:C1305–C1312. doi: 10.1152/ajpcell.00007.2008. [DOI] [PubMed] [Google Scholar]
60.Keith B, Johnson RS, Simon MC. HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression. Nat Rev Cancer. 2011;12:9–22. doi: 10.1038/nrc3183. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Imamura T, Kikuchi H, Herraiz M-T, et al. HIF-1alpha and HIF-2alpha have divergent roles in colon cancer. Int J cancer. 2009;124:763–771. doi: 10.1002/ijc.24032. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Zhu C, Yu J, Pan Q, et al. Hypoxia-inducible factor-2 alpha promotes the proliferation of human placenta-derived mesenchymal stem cells through the MAPK/ERK signaling pathway. Sci Rep. 2016;6:35489. doi: 10.1038/srep35489. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Thompson AAR, Elks PM, Marriott HM, et al. Hypoxia-inducible factor 2α regulates key neutrophil functions in humans, mice, and zebrafish. Blood. 2014;123:366–376. doi: 10.1182/blood-2013-05-500207. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Davis L, Recktenwald M, Hutt E, et al. Targeting HIF-2α in the tumor microenvironment: redefining the role of HIF-2α for solid cancer therapy. Cancers (Basel) 2022 doi: 10.3390/cancers14051259. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Su R, Ma J, Zheng J, et al. PABPC1-induced stabilization of BDNF-AS inhibits malignant progression of glioblastoma cells through STAU1-mediated decay. Cell Death Dis. 2020;11:81. doi: 10.1038/s41419-020-2267-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Jing F, Ruan X, Liu X, et al. The PABPC5/HCG15/ZNF331 feedback loop regulates vasculogenic mimicry of glioma via STAU1-mediated mRNA decay. Mol Ther oncolytics. 2020;17:216–231. doi: 10.1016/j.omto.2020.03.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary file1 (PDF 316 KB)^{(315KB, pdf)}

Data Availability Statement

[CR1] 1.Rudzinski ER, Anderson JR, Chi Y-Y, et al. Histology, fusion status, and outcome in metastatic rhabdomyosarcoma: a report from the Children’s Oncology Group. Pediatr Blood Cancer. 2017 doi: 10.1002/pbc.26645. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR2] 2.Wang C. Childhood rhabdomyosarcoma: recent advances and prospective views. J Dent Res. 2012;91:341–350. doi: 10.1177/0022034511421490. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Hettmer S, Wagers AJ. Muscling in: uncovering the origins of rhabdomyosarcoma. Nat Med. 2010;16:171–173. doi: 10.1038/nm0210-171. [DOI] [PubMed] [Google Scholar]

[CR4] 4.Sarkar D, Ray S, Saha M, et al. Alveolar rhabdomyosarcoma with multiple distal metastases. A case report and review of literature. BMJ Case Rep. 2012 doi: 10.1136/bcr-2012-006523. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR5] 5.Galili N, Davis RJ, Fredericks WJ, et al. Fusion of a fork head domain gene to PAX3 in the solid tumour alveolar rhabdomyosarcoma. Nat Genet. 1993;5:230–235. doi: 10.1038/ng1193-230. [DOI] [PubMed] [Google Scholar]

[CR6] 6.Skapek SX, Ferrari A, Gupta AA, et al. Rhabdomyosarcoma. Nat Rev Dis Prim. 2019;5:1. doi: 10.1038/s41572-018-0051-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Oberlin O, Rey A, Lyden E, et al. Prognostic factors in metastatic rhabdomyosarcomas: results of a pooled analysis from United States and European cooperative groups. J Clin Oncol. 2008;26:2384–2389. doi: 10.1200/JCO.2007.14.7207. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR8] 8.Chen X, Stewart E, Shelat AA, et al. Targeting oxidative stress in embryonal rhabdomyosarcoma. Cancer Cell. 2013;24:710–724. doi: 10.1016/j.ccr.2013.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR9] 9.Solaini G, Baracca A, Lenaz G, Sgarbi G. Hypoxia and mitochondrial oxidative metabolism. Biochim Biophys Acta. 2010;1797:1171–1177. doi: 10.1016/j.bbabio.2010.02.011. [DOI] [PubMed] [Google Scholar]

[CR10] 10.Chandel NS, Maltepe E, Goldwasser E, et al. Mitochondrial reactive oxygen species trigger hypoxia-induced transcription. Proc Natl Acad Sci USA. 1998;95:11715–11720. doi: 10.1073/pnas.95.20.11715. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR11] 11.Monti E, Fanzani A. Uncovering metabolism in rhabdomyosarcoma. Cell Cycle. 2016;15:184–195. doi: 10.1080/15384101.2015.1071746. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR12] 12.Crawford Parks TE, Marcellus KA, Langill J, et al. Novel Roles for Staufen1 in Embryonal and Alveolar Rhabdomyosarcoma via c-myc-dependent and -independent events. Sci Rep. 2017;7:42342. doi: 10.1038/srep42342. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR13] 13.Almasi S, Parks TEC, Ravel-Chapuis A, et al. Differential regulation of autophagy by STAU1 in alveolar rhabdomyosarcoma and non-transformed skeletal muscle cells. Cell Oncol (Dordr) 2021;44:851–870. doi: 10.1007/s13402-021-00607-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR14] 14.Almasi S, Jasmin BJ. The multifunctional RNA-binding protein Staufen1: an emerging regulator of oncogenesis through its various roles in key cellular events. Cell Mol Life Sci. 2021;78:7145–7160. doi: 10.1007/s00018-021-03965-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR15] 15.Park E, Maquat LE. Staufen-mediated mRNA decay. Wiley Interdiscip Rev RNA. 2013;4:423–435. doi: 10.1002/wrna.1168. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 16.Gonzalez Quesada Y, Bonnet-Magnaval F, DesGroseillers L. Phosphomimicry on STAU1 Serine 20 impairs STAU1 posttranscriptional functions and induces apoptosis in human transformed cells. Int J Mol Sci. 2022 doi: 10.3390/ijms23137344. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Gonzalez Quesada Y, DesGroseillers L. A degradation motif in STAU1 defines a novel family of proteins involved in inflammation. Int J Mol Sci. 2022;23:11588. doi: 10.3390/ijms231911588. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR18] 18.Haimovich G, Medina DA, Causse SZ, et al. Gene expression is circular: factors for mRNA degradation also Foster mRNA synthesis. Cell. 2013;153:1000–1011. doi: 10.1016/j.cell.2013.05.012. [DOI] [PubMed] [Google Scholar]

[CR19] 19.Marcellus KA, Crawford Parks TE, Almasi S, Jasmin BJ. Distinct roles for the RNA-binding protein Staufen1 in prostate cancer. BMC Cancer. 2021;21:120. doi: 10.1186/s12885-021-07844-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Paul S, Dansithong W, Figueroa KP, et al. Staufen1 in human neurodegeneration. Ann Neurol. 2021;89:1114–1128. doi: 10.1002/ana.26069. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR21] 21.Damas ND, Marcatti M, Côme C, et al. SNHG5 promotes colorectal cancer cell survival by counteracting STAU1-mediated mRNA destabilization. Nat Commun. 2016;7:13875. doi: 10.1038/ncomms13875. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR22] 22.Bonnet-Magnaval F, Diallo LH, Brunchault V, et al. High level of Staufen1 expression confers longer recurrence free survival to non-small cell lung cancer patients by promoting THBS1 mRNA degradation. Int J Mol Sci. 2021;23:215. doi: 10.3390/ijms23010215. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Bonnet-Magnaval F, DesGroseillers L. The Staufen1-dependent cell cycle regulon or how a misregulated RNA-binding protein leads to cancer. Biol Rev Camb Philos Soc. 2021;96:2192–2208. doi: 10.1111/brv.12749. [DOI] [PubMed] [Google Scholar]

[CR24] 24.Ghram M, Bonnet-Magnaval F, Hotea DI, et al. Staufen1 is essential for cell-cycle transitions and cell proliferation via the control of E2F1 expression. J Mol Biol. 2020;432:3881–3897. doi: 10.1016/j.jmb.2020.04.016. [DOI] [PubMed] [Google Scholar]

[CR25] 25.Pantic B, Borgia D, Giunco S, et al. Reliable and versatile immortal muscle cell models from healthy and myotonic dystrophy type 1 primary human myoblasts. Exp Cell Res. 2016;342:39–51. doi: 10.1016/j.yexcr.2016.02.013. [DOI] [PubMed] [Google Scholar]

[CR26] 26.Hinson ARP, Jones R, Crose LES, et al. Human rhabdomyosarcoma cell lines for rhabdomyosarcoma research: utility and pitfalls. Front Oncol. 2013;3:183. doi: 10.3389/fonc.2013.00183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR27] 27.Klages N, Zufferey R, Trono D. A stable system for the high-titer production of multiply attenuated lentiviral vectors. Mol Ther. 2000;2:170–176. doi: 10.1006/mthe.2000.0103. [DOI] [PubMed] [Google Scholar]

[CR28] 28.Perez-Riverol Y, Bai J, Bandla C, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50:D543–D552. doi: 10.1093/nar/gkab1038. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR29] 29.Gehlenborg N, Wong B. Heat maps. Nat Methods. 2012;9:213–213. doi: 10.1038/nmeth.1902. [DOI] [PubMed] [Google Scholar]

[CR30] 30.Tang JX, Thompson K, Taylor RW, Oláhová M. Mitochondrial OXPHOS biogenesis: co-regulation of protein synthesis, import, and assembly pathways. Int J Mol Sci. 2020;21:3820. doi: 10.3390/ijms21113820. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Park D, Lee S, Min K-T. Techniques for investigating mitochondrial gene expression. BMB Rep. 2020;53:3–9. doi: 10.5483/BMBRep.2020.53.1.272. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR32] 32.Furic L, Maher-Laporte M, DesGroseillers L. A genome-wide approach identifies distinct but overlapping subsets of cellular mRNAs associated with Staufen1- and Staufen2-containing ribonucleoprotein complexes. RNA. 2008;14:324–335. doi: 10.1261/rna.720308. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR33] 33.Sugimoto Y, Vigilante A, Darbo E, et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature. 2015;519:491–494. doi: 10.1038/nature14280. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Crawford Parks TE, Marcellus KA, Péladeau C, et al. Overexpression of Staufen1 in DM1 mouse skeletal muscle exacerbates dystrophic and atrophic features. Hum Mol Genet. 2020;29:2185–2199. doi: 10.1093/hmg/ddaa111. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Gong C, Kim YK, Woeller CF, et al. SMD and NMD are competitive pathways that contribute to myogenesis: effects on PAX3 and myogenin mRNAs. Genes Dev. 2009;23:54–66. doi: 10.1101/gad.1717309. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Krämer A, Green J, Pollard J, Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30:523–530. doi: 10.1093/bioinformatics/btt703. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Sharma LK, Lu J, Bai Y. Mitochondrial respiratory complex I: structure, function and implication in human diseases. Curr Med Chem. 2009;16:1266–1277. doi: 10.2174/092986709787846578. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR38] 38.Chujo T, Ohira T, Sakaguchi Y, et al. LRPPRC/SLIRP suppresses PNPase-mediated mRNA decay and promotes polyadenylation in human mitochondria. Nucleic Acids Res. 2012;40:8033–8047. doi: 10.1093/nar/gks506. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR39] 39.Nagao A, Hino-Shigi N, Suzuki T. Chapter 23 measuring mRNA decay in human mitochondria. Amsterdam: Elsevier; 2008. pp. 489–499. [DOI] [PubMed] [Google Scholar]

[CR40] 40.Burslem GM, Kyle HF, Nelson A, et al. Hypoxia inducible factor (HIF) as a model for studying inhibition of protein-protein interactions. Chem Sci. 2017;8:4188–4202. doi: 10.1039/c7sc00388a. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.Orrenius S, Gogvadze V, Zhivotovsky B. Mitochondrial oxidative stress: implications for cell death. Annu Rev Pharmacol Toxicol. 2007;47:143–183. doi: 10.1146/annurev.pharmtox.47.120505.105122. [DOI] [PubMed] [Google Scholar]

[CR42] 42.Bao X, Zhang J, Huang G, et al. The crosstalk between HIFs and mitochondrial dysfunctions in cancer development. Cell Death Dis. 2021;12:215. doi: 10.1038/s41419-021-03505-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Kuhnt T, Pelz T, Qu X, et al. Mitochondrial OXPHOS Functions in R1H Rhabdomyosarcoma and Skeletal Muscles of the Rat. Neurochem Res. 2007;32:973–980. doi: 10.1007/s11064-006-9254-0. [DOI] [PubMed] [Google Scholar]

[CR44] 44.DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7:11–20. doi: 10.1016/j.cmet.2007.10.002. [DOI] [PubMed] [Google Scholar]

[CR45] 45.Fell D, Athel C-B. Understanding the control of metabolism. London: Portland Press; 1997. [Google Scholar]

[CR46] 46.Alabduladhem TO, Bordoni B (2022) Physiology, Krebs Cycle [PubMed]

[CR47] 47.Fan TW, Kucia M, Jankowski K, et al. Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes. Mol Cancer. 2008;7:79. doi: 10.1186/1476-4598-7-79. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR48] 48.Chiu HY, Loh AHP, Taneja R. Mitochondrial calcium uptake regulates tumour progression in embryonal rhabdomyosarcoma. Cell Death Dis. 2022;13:419. doi: 10.1038/s41419-022-04835-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR49] 49.Heinicke U, Kupka J, Fichter I, Fulda S. Critical role of mitochondria-mediated apoptosis for JNJ-26481585-induced antitumor activity in rhabdomyosarcoma. Oncogene. 2016;35:3729–3741. doi: 10.1038/onc.2015.440. [DOI] [PubMed] [Google Scholar]

[CR50] 50.Zhang M, Linardic CM, Kirsch DG. RAS and ROS in Rhabdomyosarcoma. Cancer Cell. 2013;24:689–691. doi: 10.1016/j.ccr.2013.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR51] 51.Yang L, Kong D, He M, et al. MiR-7 mediates mitochondrial impairment to trigger apoptosis and necroptosis in Rhabdomyosarcoma. Biochim Biophys Acta - Mol Cell Res. 2020;1867:118826. doi: 10.1016/j.bbamcr.2020.118826. [DOI] [PubMed] [Google Scholar]

[CR52] 52.Almasi S, Kennedy BE, El-Aghil M, et al. TRPM2 channel-mediated regulation of autophagy maintains mitochondrial function and promotes gastric cancer cell survival via the JNK-signaling pathway. J Biol Chem. 2018;293:3637–3650. doi: 10.1074/jbc.M117.817635. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR53] 53.Thomas LW, Ashcroft M. Exploring the molecular interface between hypoxia-inducible factor signalling and mitochondria. Cell Mol Life Sci. 2019;76:1759–1777. doi: 10.1007/s00018-019-03039-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Patel SA, Simon MC. Biology of hypoxia-inducible factor-2alpha in development and disease. Cell Death Differ. 2008;15:628–634. doi: 10.1038/cdd.2008.17. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR55] 55.Flynn JM, Melov S. SOD2 in mitochondrial dysfunction and neurodegeneration. Free Radic Biol Med. 2013;62:4–12. doi: 10.1016/j.freeradbiomed.2013.05.027. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR56] 56.Shneor D, Folberg R, Pe’er J,, et al. Stable knockdown of CREB, HIF-1 and HIF-2 by replication-competent retroviruses abrogates the responses to hypoxia in hepatocellular carcinoma. Cancer Gene Ther. 2017;24:64–74. doi: 10.1038/cgt.2016.68. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR57] 57.Bertout JA, Majmundar AJ, Gordan JD, et al. HIF2α inhibition promotes p53 pathway activity, tumor cell death, and radiation responses. Proc Natl Acad Sci. 2009;106:14391–14396. doi: 10.1073/pnas.0907357106. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR58] 58.Li NA, Wang H, Zhang J, Zhao E. Knockdown of hypoxia inducible factor-2α inhibits cell invasion via the downregulation of MMP-2 expression in breast cancer cells. Oncol Lett. 2016;11:3743–3748. doi: 10.3892/ol.2016.4471. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR59] 59.Brown ST, Nurse CA. Induction of HIF-2alpha is dependent on mitochondrial O2 consumption in an O2-sensitive adrenomedullary chromaffin cell line. Am J Physiol Cell Physiol. 2008;294:C1305–C1312. doi: 10.1152/ajpcell.00007.2008. [DOI] [PubMed] [Google Scholar]

[CR60] 60.Keith B, Johnson RS, Simon MC. HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression. Nat Rev Cancer. 2011;12:9–22. doi: 10.1038/nrc3183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR61] 61.Imamura T, Kikuchi H, Herraiz M-T, et al. HIF-1alpha and HIF-2alpha have divergent roles in colon cancer. Int J cancer. 2009;124:763–771. doi: 10.1002/ijc.24032. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR62] 62.Zhu C, Yu J, Pan Q, et al. Hypoxia-inducible factor-2 alpha promotes the proliferation of human placenta-derived mesenchymal stem cells through the MAPK/ERK signaling pathway. Sci Rep. 2016;6:35489. doi: 10.1038/srep35489. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR63] 63.Thompson AAR, Elks PM, Marriott HM, et al. Hypoxia-inducible factor 2α regulates key neutrophil functions in humans, mice, and zebrafish. Blood. 2014;123:366–376. doi: 10.1182/blood-2013-05-500207. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR64] 64.Davis L, Recktenwald M, Hutt E, et al. Targeting HIF-2α in the tumor microenvironment: redefining the role of HIF-2α for solid cancer therapy. Cancers (Basel) 2022 doi: 10.3390/cancers14051259. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR65] 65.Su R, Ma J, Zheng J, et al. PABPC1-induced stabilization of BDNF-AS inhibits malignant progression of glioblastoma cells through STAU1-mediated decay. Cell Death Dis. 2020;11:81. doi: 10.1038/s41419-020-2267-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR66] 66.Jing F, Ruan X, Liu X, et al. The PABPC5/HCG15/ZNF331 feedback loop regulates vasculogenic mimicry of glioma via STAU1-mediated mRNA decay. Mol Ther oncolytics. 2020;17:216–231. doi: 10.1016/j.omto.2020.03.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Staufen1 controls mitochondrial metabolism via HIF2α in embryonal rhabdomyosarcoma and promotes tumorigenesis

Shekoufeh Almasi

Sahar SarmastiEmami

Stephen Baird

Zhibin Ning

Daniel Figeys

Jocelyn Côté

Kyle N Cowan

Bernard J Jasmin

Abstract

Supplementary Information

Introduction

Materials and methods

Constructs and antibodies

Reagents

Cell culture, transfection, and lentivirus production and infection

Western blotting

Proteomics

RNA extraction, reverse transcription, and quantitative real-time PCR

RNA immunoprecipitation assay

mRNA stability assay

CFSE proliferation assay

MTT viability assay

Intracellular reactive oxygen species detection assay

Mitochondrial superoxide detection assay

Extracellular flux analysis

Results

Expression of STAU1 is upregulated in ERMS cells and its downregulation reduces cell proliferation

Fig. 1.

STAU1 downregulation reduces mitochondrial OXPHOS in ERMS cells while promoting OXPHOS in non-transformed skeletal muscle cells

Fig. 2.

The OXPHOS canonical pathway is differentially regulated in STAU1-depleted ERMS cells versus non-transformed skeletal muscle cells

Fig. 3.

STAU1 downregulation impacts expression of nuclear OXPHOS mRNAs

Table 1.

Fig. 4.

Fig. 5.

STAU1 downregulation impacts expression of mitochondrial-expressing OXPHOS genes

Fig. 6.

STAU1 depletion alters HIF2α pathway

Fig. 11.

Fig.7.

STAU1-mediated control of the HIF2α pathway regulates mitochondrial metabolism in ERMS cell lines

Fig.8.

Fig. 9.

Fig. 10.

Overexpression of HIF2α in STAU1 knockout ERMS cells alters mRNA levels of nuclear- and mitochondrial OXPHOS genes

Table 2.

HIF2α differentially regulates mitochondrial metabolism and OXPHOS gene expression in non-malignant skeletal muscle cells

Table 3.

Discussion

Supplementary Information

Acknowledgements

Author contributions

Funding

Data availability

Declarations

Conflict of interest

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases