Cellular and Molecular Life Sciences (2023) 80:328

Cellular and Molecular Life Sciences

https://doi.org/10.1007/s00018-023-04969-4
ORIGINAL ARTICLE
Staufen1 controls mitochondrial metabolism via HIF2α in embryonal rhabdomyosarcoma and promotes tumorigenesis
Shekoufeh Almasi · Sahar SarmastiEmami1 · Stephen Baird · Zhibin Ning · Daniel Figeys3 · Jocelyn Côté1,4 · Kyle N. Cowan1,5,6 · Bernard J. Jasmin1,4
Received: 20 May 2023 / Revised: 31 August 2023 / Accepted: 18 September 2023 / Published online: 17 October 2023
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023
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.
Keywords Embryonal rhabdomyosarcoma · STAU1 · Mitochondrial metabolism · OXPHOS

* Bernard J. Jasmin jasmin@uottawa.ca
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 fusionpositive 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 fusionnegative 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-bind- ing 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. S TAU155 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 dharma- con 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). Len- tiCRISPR 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, dithiothrei- tol (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/strepto- mycin antibiotic (Gibco; Life Technologies, 15070063). HSMM-C3 cells were grown in SkBM™-2 Basal Medium (Lonza# CC-3245) supplemented with SkGM™-2 Sin- gleQuots™ 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 cellspecific 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 ReproSilPur 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 reequilibration. 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. qiagenbioin forma tics.c om/produc ts/ingenuityp athway- analy sis). 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 (Inv- itrogen). 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 Mito- SOX 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 Oli- gomycin 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. 1a). 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-knock- down 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.
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 ◂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
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.
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. qiage nbioi nform atics. com/produ cts/ingen uity- pathw

◂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
ay- analy sis). 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).
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

◂Fig. 3 STAU1 downregulation differentially impacts OXPHOS pathway in ERMS and HSMM-C3 cells. a Proteomics analysis was performed after 96 h of Ctrl. and KD cells treatment with 20 µg/mL Doxycycline. The obtained MaxQuent data were further analyzed using Perseus to identify differential protein levels among samples. Next, IPA analysis was performed on the obtained protein expression data to determine the alterations in various canonical pathways including OXPHOS pathway. Heat map showing the altered protein levels in Wildtype and STAU1-depleted cells (n = 3). b Heat map indication differential dysregulation of various canonical pathways in HSMM-C3 and ERMS cell lines after STAU1 depletion. Data are presented as a ratio of pathway activity in STAU1-KD versus Control cells. c Volcano plots showing the fold change and significance of upregulated and downregulated proteins in STAU1-KD cells compared to control cells. The protein fold changes with - log10 (p values) ≥ 1.5 are considered significant and shows in Red. d Volcano plots showing the fold change of OXPHOS proteins in STAU1-KD cells compared to control cells. The protein fold changes with - log10 (p values) ≥ 1.5 are considered significant and shows in Red. Details are included in Table 1
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 (T1/2) 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.
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. ingen uity. 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.

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	

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

Fig. 4 STAU1 interacts with nuclear OXPHOS mRNAs in ERMS and HSMM-C3 cells. a RT-qPCR analysis of nuclear OXPHOS genes in STAU1-depleted HSMM-C3 cells compared to control cells. b RT- qPCR analysis of nuclear OXPHOS genes in RH36 cells. c RT-qPCR analysis of nuclear OXPHOS genes in RD cells. d Representative Western blot of precipitated STAU1 protein. RNA co-IP assays were performed with control immunoglobulin G (IgG) or STAU1-specific
antibody. The precipitated protein was analyzed by Western blotting with STAU1-specific antibody. e RT-qPCR analysis of the precipitated RNA using primers for endogenous mRNAs. Fold precipitation relative to β-actin mRNA was calculated and presented as a bar graph (n = 3). 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

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 (t1/2) 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

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.
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 STAU1wt and/or HIF2αwt while STAU1-knockout and HIF2α overexpressing cells were shown as STAU1- and HIF2α+, respectively.
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 STAU1wt 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 (STAU1wt/HIF2αwt) (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.

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◂Fig. 6 STAU1 downregulation impacts the expression levels of mitochondrial OXPHOS genes. a Box and Whisker plots representing the proteomics analysis of the mitochondrial OXPHOS proteins. The OXPHOS protein fold changes in STAU1-KD cells are calculated as a ratio of control cells. b RT-qPCR analysis of the mitochondrial OXPHOS genes in HSMM-C3 and ERMS cells. c The IPA predicted dysregulation of several mitochondrial OXPHOS proteins which were not detected in proteomics assay. The bar charts represent the mRNA fold change of the predicted proteins obtained from RT-qPCR assay. d The precipitated RNAs obtained from an RNA co-IP assay using G (IgG) or STAU1-specific antibody were analyzed by RT-qPCR, using primers for endogenous mitochondrial OXPHOS mRNAs. 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 (t1/2) 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 versus control (pTRIPZ) ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05
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 S TAU1-/HIF2αwt, but not in RH36 cells with only HIF2α overexpression or STAU1wt/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.
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-/HIF2αwt, STAU1wt/HIF2α-, STAU1-/HIF2α-) and the HSMM-C3 parental (STAU1wt/ HIF2αwt) 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-/HIF2αwt 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, (STAU1wt/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

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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 (t1/2) 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

OXPHOS genes using RT-qPCR. Our data indicate dysregulation of several nuclear OXPHOS mRNAs in STAU1-/HIF2α- compared to STAU1-/HIF2αwt cells.
More specifically, mRNA levels of CYC1, NDUFB4, NDUFB10, UQCRB, and UQCRH, were reduced after HIF2α depletion in S TAU1- cells while COX7A2L,

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 Antimy- cin 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

202788227222

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-/HIF2αwt and STAU1-/HIF2α+ ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

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 nontransformed skeletal muscle cells, thereby limiting its impact to cancer cells which further highlights STAU1’s therapeutic potential [13].
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

Table 2 Fold change of OXPHOS mRNAs in ERMS cell lines presented in Fig. 9	Cell line	Gene location	mRNA symbol	mRNA fold change	
				STAU1WT/
HIF2αWT	STAU1WT/
HIF2α+	STAU1/
HIF2αWT	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	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	

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 nontransformed skeletal muscle cells without affecting cell

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-/HIF2αwt ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05

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	
				STAU1WT/
HIF2αWT	STAU1WT/
HIF2α-	STAU1-/
HIF2αWT	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	

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].

◂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 nontransformed 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)
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-trans- formed 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 The online version contains supplementary material available at https://doi. org/ 10. 1007/s00018- 023- 04969-4.
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-fi- nancial interests to disclose.
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4 The Eric J. Poulin Centre for Neuromuscular Diseases, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
5 Department of Surgery, Division of Paediatric Surgery, University of Ottawa, Children’s Hospital of Eastern Ontario, Ottawa, ON K1Y 4E9, Canada
6 Molecular Biomedicine Program, Children’s Hospital
of Eastern Ontario, Ottawa, ON K1H 8L1, Canada