Post-transcriptional repression of GSNOR by microRNAs regulates S-nitrosylation and fuels breast cancer progression

Post-transcriptional repression of GSNOR by microRNAs regulates S-nitrosylation and fuels breast cancer progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Post-transcriptional repression of GSNOR by microRNAs regulates S-nitrosylation and fuels breast cancer progression Salvatore Rizza, Gianmarco Matrullo, Marta Ballester Martinez, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8532436/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The denitrosylase S-nitrosoglutathione reductase (GSNOR) is a central regulator of nitric oxide (NO) signaling, by controlling protein S-nitrosylation. GSNOR is downregulated in several human cancers, with breast cancer representing one of the major hits. However, the mechanisms driving its suppression and its precise role in tumor progression remain elusive. Here, we identify microRNAs as key post-transcriptional regulators of GSNOR in breast cancer, specifically miR-455-3p and miR-519a-5p. Restoring GSNOR levels, either directly or by inhibiting these onco-miRs, reduced global protein S-nitrosylation, including that of glycogen synthase kinase 3β (GSK3β). This event was associated with decreased GSK3β inhibitory phosphorylation and consequent subsequent suppression of oncogenic signaling pathways. Functionally, GSNOR restoration inhibited epithelial-mesenchymal transition by increasing E-cadherin and reducing nuclear SNAI1 levels, attenuated β-catenin signaling, and impaired cell invasion, motility, and mammospheres viability. Altogether, our findings unveil a novel regulatory axis in which specific microRNAs control protein S-nitrosylation by targeting GSNOR, thereby driving breast cancer progression, and establish GSNOR as a crucial tumor suppressor. Biological sciences/Cancer/Breast cancer Biological sciences/Cell biology/Post-translational modifications/Nitrosylation S-nitrosylation microRNA breast cancer nitric oxide EMT nitric oxide Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Breast cancer (BC) is the most frequent cancer in women. Despite the decreasing mortality rate due to early detection and more advanced therapies, BC remains a major cause of cancer mortality owing to heterogeneous molecular features and unpredictable responses to therapy. Among a series of well-described genetic and environmental factors, nitric oxide (NO) is known to play a role in BC onset, progression, and response to therapy 1 – 3 . NO activation of oncogenic pathways in BC has been associated with upregulation of the inducible isoform of nitric oxide synthase 1 , 4 . However, many aspects of NO signaling in BC are still unclear 5 , 6 . The chemical addition of NO to specific cysteines, a reaction named S-nitrosylation, constitutes a crucial NO-induced posttranslational modification of proteins governing cancer-related pathways 7 , 8 . Because of its extremely short half-life and high reactivity, NO binding to proteins and low-molecular-weight thiols ensures a longer spatial and temporal action 9 . S-nitrosylation of glutathione, the most abundant free thiol in cells, produces S-nitrosoglutathione (GSNO), which acts as a reservoir of NO due to slow NO-release kinetics and the capability of exchanging NO moiety with proteins 10 . GSNO catabolism is carried out by the evolutionary conserved denitrosylase S-nitrosoglutathione reductase (GSNOR) 11 , 12 . GSNOR deficiency has been associated with the development of liver cancer 13 , enhanced chemoresistance of human epidermal growth factor receptor 2-positive (HER2 + ) BC 14 , metabolic shift and immune evasion in colorectal cancer 15 . This evidence indicates that S-nitrosylation plays a primary role in neoplastic transformation and progression 12 , 14 , 16 , 17 . Recently, we have reported that GSNOR expression is reduced in multiple human cancers, with BC representing one of the major hits 18 . The lack of GSNOR sustains S-nitrosylation of focal adhesion kinase 1 (FAK1) at Cys-658 which, in turn, promotes FAK1 phosphorylation and resistance to anoikis , finally resulting in enhanced growth of tumor masses 18 – 20 . Despite these studies arguing for GSNOR acting as a tumor suppressor, the mechanisms that regulate GSNOR expression in cancer are still unknown. In this work, we demonstrate that microRNAs – that are notoriously involved in BC onset and progression - impact redox signaling through regulation of GSNOR, thereby rewiring S-nitrosylation and BC-associated signaling pathways. This unveils a novel regulatory axis where microRNA activity converges with redox signaling, profoundly influencing breast cancer progression. MATERIALS AND METHODS Cell cultures. Breast cancer cell lines MCF-7, MDA-MB-231, ZR-75-1, HCC38, T47D and MCF10A were purchased from ATCC (American Type Culture Collection). Cells were maintained in a humidified 5% CO2, 37°C incubator. MCF-7, MDA-MB-231, ZR-75-1 were grown in DMEM (Thermo-Fisher), whereas HCC38 and T47D were cultured in RPMI (Thermo-Fisher). MCF10A were cultured in MEGM (Lonza) supplemented with 100 ng/mL cholera toxin (Sigma-Aldrich). DMEM and RPMI were supplemented with a mixture of penicillin and streptomycin 10 U/ml 1% (v/v) and 10% (v/v) FBS (Thermo-Fisher). Mycoplasma contamination was routinely screened by a PCR-based assay (Eurofins Genomics). Mammospheres. Mammospheres were produced by seeding 750 (MCF7) or 1500 (MDA-MB-231) cells/well into ultra-low attachment U-bottom 96-multiwells (Corning, CLS7007-24EA) in standard growing conditions for 7-to-14 days, with 1/3 of the medium refreshed every 3 days. Mammospheres cell death was revealed by ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices) upon staining of spheroids with LIVE/DEAD® Cell Imaging Kit (488/570) (Thermo-Fisher Scientific, R37601) and Hoechst 33342 (Thermo-Fisher Scientific, 62249). Acquisition of images and 3D rendering of spheroids were performed using MetaXpress software (Molecular Devices). Mammosphere viability was assessed by CellTiter-Glo 3D Cell Viability Assay (Promega, G9681) following manufacturer's protocol. A Victor X4 plate reader (PerkinElmer) was used to record luminescence. Transfections and treatments. Overexpression of protein constructs was performed using Lipofectamine 3000 (Thermo Fisher Scientific, L3000001) according to manufacturer's instruction. The plasmids used in this work were generated in our laboratory. ADH5 cDNA coding for GSNOR was cloned into the vector pLPCX (Clontech). Anti-miRNAs and miRNA mimics were transfected into the cells by RNAiMAX (Thermo Fisher Scientific) according to manufacturer’s protocol. The list of miRs and antimiRs used is reported in Table S1 . Transient knock-down was performed by transfecting the cells with RNAiMAX (Thermo Fisher Scientific) endonuclease-prepared pools of siRNAs (esiRNA, Sigma-Aldrich) directed against ADH5 (siGSNOR, EHU104681), or with a scramble duplex (siSCR, SIC001). miRNAs analysis. The list of miRNAs targeting GSNOR overexpressed in GSNOR-downregulating BC samples has been drawn up with the following procedure. TCGA samples from 97 BC patients with paired tumour/adjacent normal tissue miRNA-seq and RNA-seq data were included in the analysis. RAID v2.0 ( www.rna-society.org/raid/ ) 21 was used to identify miRNAs targeting GSNOR mRNA. We selected only experimentally validated hits from 'CLASH', 'CLIP-seq' and 'PAR-CLIP' experiments. Finally, we investigated the expression of these miRNAs in the same 97 BC patients from TCGA utilizing DESeq2 to analyze differential expression between paired tumor and adjacent normal samples 22 . Our final list of candidates is reported along with their log2FoldChange (FC) and adjusted p-values (FDR-corrected for multiple testing). Western blotting. Whole cell protein extracts were obtained in lysis buffer (50 mM Tris HCl, pH 6.8, 2% SDS, 10% glycerol) followed by denaturation for 10 min at 98°C. Protein extracts from tumor biopsies were obtained upon homogenization in RIPA Buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM EDTA, 5 mM MgCl2, 1% Triton X-100, 0.25% Sodium Deoxycholate, 0.1% SDS, 5 mM β-glycerophosphate, 5 mM sodium fluoride, 2 mM sodium orthovanadate, protease inhibitor cocktail-P8340). Proteins were quantified by DC Protein Assay Kit (Bio-Rad, 5000116). Wester Blotting procedure was performed as reported 18 . Primary antibodies used and dilutions are listed in Table S2 . Biotin-switch assay. Protein S-nitrosylation was evaluated by biotin-switch assay as previously described 23 . Briefly, cells were homogenized in HEN buffer (HEPES-NaOH 250 mM, EDTA 1 mM, 0.1 mM Neocuproine, 1% Triton X-100, protease inhibitors, pH 7.8). Free cysteine residues were blocked in S-methyl methane-thiosulfonate 50 µM (MMTS, Thermo-Fisher Scientific) and 2.5% SDS and incubated for 30 min at 50°C. Proteins were then precipitated with cold acetone, collected by centrifugation, resuspended in HEN buffer with 1% (HEN/S) and incubated with EZ-Link HPDP-biotin 250 µM (Thermo-Fisher Scientific) in the presence or absence of sodium ascorbate 50 mM. Biotinylated proteins were then pulled down with agarose streptavidin beads and eluted in NuPAGE LDS Sample Buffer (Thermo-Fisher Scientific) and DTT. After incubation with the HRP-streptavidin (Cell Signaling), biotinylated proteins were revealed using the ECL Prime detection system (Amersham). RNA sequencing. MCF7 cells were grown in adhesion for 48 hours and total RNA was purified using RNeasy Plus kit (Qiagen, 74134) following producer's protocol. RNAseq of GSNOR-KO and parental WT MCF7 cells was performed by Next Generation Diagnostic srl c/o TIGEM while GSNOR-overexpressing and parental cells were analysed by BMKGENE (Biomarker Technologies). Raw gene-level count matrices were obtained from the sequencing provider and analyzed in R (v4.x) using RStudio Desktop (2024.12.1 + 563). Differential expression was performed using DESeq2 22 . Gene Set Enrichment Analysis (GSEA) was performed with the multilevel algorithm implemented in fgsea 24 . Gene sets for Hallmark and GO Biological Process collections were retrieved from MSigDB via msigdbr 25 , 26 . Significant pathways (p-adj < 0.05) were visualized using heatmaps generated in R (pheatmap, ggplot2) and further explored using EnrichmentMap in Cytoscape 27 , 28 with default similarity metrics (combined Jaccard/Overlap). Only significantly enriched (padj < 0,05) Hallmark and GO:BP pathways were included in both heatmap and network visualizations. RT-qPCR. Total RNA was extracted from cells by using the ReliaPrep RNA Cell Miniprep System (Promega) following the protocol of the manufacturer. Complementary DNA (cDNA) was synthetized by using the reverse transcriptase M-MLV (Promega) and followed manufacturer’s protocol. RT-qPCR was performed by using iTaq Universal SYBR Green Supermix (Bio-Rad) subjected to 35 cycles of amplification with a ViiA 7 RT-qPCR System (Thermo-Fisher). Data were normalized using the ribosomal protein L34 as housekeeping. Relative fold gene expression was calculated using the formula RQ = 2 −ΔΔCt , as reported 29 . Primers used for RT-qPCR analysis were the following: L34 - FW 5’-GGCCCTGCTGACATGTTTCTT-3’ L34 -RV 5’-GTCCCGAACCCCTGGTAATAGA-3’ GSNOR - FW 5’-CATTGCCACTGCGGTTTGCCAC-3’ GSNOR - RV 5’-AGTGTCACCCGCCTTCAGCTTAGT-3’ CDH1 – FW 5’-TGGACCGAGAGAGTTTCCCT-3’ CDH1 - RV 5’-CCCTTGTACGTGGTGGGATT-3’ S-nitrosoglutathione reductase activity. S-nitrosoglutathione reductase activity was carried out on protein extracts obtained by osmotic lysis of cells and tissue in distilled water, followed by sonication. Cell lysates were clarified by centrifugation and the activity determined using a spectrophotometer as reported 30 . Wound-Healing and Boyden-chamber assays. MDA-MB-231 cells were counted and 8x10 4 cells were resuspended in 100 µl of complete medium and seeded in wound healing assay chambers (Ibidi) placed in multiwell plates. Cells were incubated overnight at 37° C and 5% CO 2 . The following day full medium replaced with serum-free medium, the septum-forming insert was carefully removed, and images were acquired at different time points as indicated using a Celigo imaging cytometer (Revvity). Wound size was calculated by Fiji ImageJ 31 software. Cell invasion was assessed using a Matrigel-coated Boyden chamber (Corning, 8 µm pores). MCF-7 cells were seeded in serum-free medium into the upper chamber, with medium containing 10% FBS as a chemoattractant in the lower chamber. After 24 hours, non-invading cells were removed from the upper surface. The invaded cells on the lower membrane surface were fixed and stained for 30 minutes with a crystal violet solution (0.05% w/v crystal violet, 1% formaldehyde, 1% methanol; all chemicals from Sigma-Aldrich). Membranes were imaged using an Olympus brightfield microscope with a color camera at 10X magnification, and invaded cells were counted using Fiji ImageJ 31 software. Confocal fluorescence microscopy. Cells were grown on Screenstar microplates (Greiner) and fixed with 4% paraformaldehyde (Sigma-Aldrich), incubated with a permeabilization solution (PBS/Triton X-100 0,4% v/v) and blocked for 1 h with a blocking solution (PBS/FBS 10% v/v). Afterwards, cells were incubated over night at 4ºC with anti-SNAI1 and anti-CDH1 or with an anti-non-phopsho beta-catenin antibodies ( Table S2 ). After three washes in PBS, cells were incubated for 1 h with anti-mouse Alexa Fluor 568 and anti-rabbit Alexa Fluor 647 IgG Secondary Antibodies (Thermo-Fisher Scientific). Nuclei were stained with Hoechst 33342. Images of cells were acquired by a Cell discovery 7 microscope (Carl Zeiss) and ZEN microscope software (Carl Zeiss). Fluorescence images were adjusted for brightness, contrast, and color balance by using Fiji ImageJ analysis software. Images were deconvoluted using the software Huygens Professional (Scientific Volume Imaging). The analysis of SNAI1 nuclear localization, CDH1 and non-phospho beta-catenin intensity was performed using Arivis Pro 4.4.0 (Zeiss) software. BC samples and Immunohistochemistry. BC samples were collected by the Department of Pathology at the Copenhagen University Hospital. The project was approved (KF 01–069/03) by the Copenhagen and Frederiksberg regional division of the Danish National Committee on Biomedical Research Ethics. Written informed consent was obtained from each patient included in the study. Clinicopathological information was provided by the Department of Pathology, Copenhagen University Hospital. Fresh tissue samples were partitioned immediately following surgery, with a part being snap-frozen and a fraction fixed in neutral buffered formalin and paraffin embedded for IHC-based analyses. The complete procedure of sample collecting, processing and IHC analysis has been previously described 32 . Anti-ADH5 (GSNOR) antibody used was purchased by Sigma-Aldrich (HPA044578). GSNOR expression and promoter methylation analyses. The expression of ADH5 in BC was carried out by using UALCAN 33 . Welch’s T-test estimated the significance of differences in expression levels between normal and primary tumors or tumor subgroups based on clinicopathological features as reported 33 . The relative expression of GSNOR in BC cell lines and the analysis of ADH5 gene methylation in BC cells and human samples has been performed on Depmap portal ( https://depmap.org ). Quantification and statistical analysis. Statistical significance of all the data presented in this work was calculated using GraphPad Prism v.10 software. RESULTS GSNOR expression decreases in BC. Reduced GSNOR expression confers resistance to cell death 18 and lowers the anticancer efficacy of trastuzumab in HER2 + BC 14 . Based on this observation we wondered whether GSNOR expression changed in different subtypes and stages of BC. Comparative analysis of The Cancer Genome Atlas (TCGA) between tumor and paired healthy tissues confirmed our previous observations 18 , revealing a marked downregulation of GSNOR (ADH5) in BC samples (log2FoldChange ≤ − 0.80, p = 1.24 × 10⁻²⁹) (Fig. 1 A ) . GSNOR levels were significantly lower in all the stages of the disease and cancer subtypes (i.e., Luminal, HER2 + , triple negative) if compared to paired normal breast biopsies ( Figs. S1A-B ). In addition, we found that low GSNOR levels correlated with poor prognosis of BC patients (Fig. 1 B), with Luminal B and HER2 + subtypes driving the overall statistics ( Fig. S1 C ). In line with previous results 34 , high levels of GSNOR represented a positive marker of endocrine therapy efficacy, although it was not a strong predictive factor of patient survival upon generic chemotherapy ( Fig S1 D ). Immunohistochemistry (IHC) analyses of human paraffin-embedded biopsies from BC patients confirmed that GSNOR was almost undetectable in tumor areas if compared with paired normal tissue (Fig. 1 C). We also observed that GSNOR protein levels and activity, as well as mRNA levels, were significantly lower than paired adjacent healthy specimens (Fig. 1 D-F). We then analyzed GSNOR expression in human breast-derived cell lines confirming that the great majority of cancer cells—sorted according to tumor subtype or classified according to the expression status of estrogen receptor (ER) and HER2—showed lower GSNOR expression than a non-cancerous breast cell line (Fig. 1 G). Among these cell lines, we selected five representative models of the different BC subtypes and one non-cancerous epithelial breast cell line (MCF10A) for validation (Fig. 1 H). Evaluation of protein and mRNA levels of GSNOR, as well as its enzymatic activity, was significantly lower in all BC cells compared to their non-tumor counterparts (Fig. 1 H and Figs. S1E, F ). These results indicate that GSNOR loss typifies all BC subtypes, regardless of differences they might have in genetic backgrounds and tumorigenic phenotypes. GSNOR expression in BC is regulated by microRNAs. We next investigated the mechanisms contributing to GSNOR decrease in BC. We previously reported that GSNOR expression declines with aging due to the methylation of CpG islands at the promoter of the ADH5 gene, which encodes GSNOR 12 , 35 , 36 . Based on this evidence, we analyzed the methylation status of the ADH5 promoter in biopsies from BC patients and a panel of several BC cell lines. Results obtained did not reveal a considerable rate in promoter methylation of both tumor and non-tumor breast samples and BC cell lines ( Figs. S2A- B ). High-throughput studies predicted that GSNOR might be regulated by microRNAs (miRs) 12 , 37 , 38 . The presence in the genome of GSNOR pseudogenes, which can act as competing endogenous RNAs (ceRNAs) through a mechanism reported in invasive BC 39 , indirectly suggests a post-transcriptional regulation of GSNOR expression, such as that operated by miRs 40 . Based on this, we identified miRs differentially expressed in BC patients with low GSNOR levels from the TCGA database. We then integrated this list with computationally and experimentally validated GSNOR-targeting miRs from the RAID v2.0 database 21 . This analysis provided a list of GSNOR-targeting miRs that are significantly modulated in BC (Fig. 2 A). We then focused on miRs that were upregulated, and investigated their capability of modulating GSNOR expression by neutralizing them with antisense oligonucleotides (anti-miRs) 41 . We initially confirmed that both cell lines express the above-mentioned miRs 42 – 44 ( Fig. S2 C ) and validated anti-miRs transfection efficiency by fluorescent reporters ( Figs. S2D-E ). We evaluated GSNOR expression upon transfecting the correspondent anti-miRs of the five GSNOR-targeting miRs reported to be the most significant ( miR-455-3p, miR-519a-5p, miR-182-5p, miR-9-5p, miR-449a ) in the luminal MCF7 and triple negative MDA-MB-231 cell lines. To note, -3p and − 5p designate the mature miRNA sequences derived from the 3' and 5' arms of the precursor miR hairpin structure, respectively. This nomenclature is omitted when pre-miRs produce a unique mature miR. Figure 2 B shows that anti- miR-455-3p and anti-miR-519a-5p significantly rescue GSNOR expression in MCF7 while only anti-miR-519a-5p was effective in MDA-MB-231. These data were supported by results obtained upon overexpression of miR-455-3p and miR-519a-5p mimics into both cell lines, which reduced GSNOR levels with a similar pattern (Fig. 2 C). The structures and sequences of miR-455-3p and miR-519a-5p are well characterized ( Fig. S2 F ). The binding region of both miR-455-3p and miR-519a-5p on GSNOR mRNA maps at the 3’UTR. This was already validated in cellular systems by CLIP sequencing (GSE42701 45 , GSE44404 46 ) and reported in miRTarBase, the database of experimentally validated microRNA-target interactions (Fig. 2 D) 47 . Both miRs have been linked to cancer biology due to their role in cell proliferation, chemoresistance and cell migration 48 – 52 . In line with this, we found out that BC patients expressing high levels of miR-519a-5p have a poor prognosis, with a similar trend shown by miR-455-3p (Fig. 2 E ) . However, to date, no functional link between these two miRs and GSNOR has not yet been reported. GSNOR modulation alters BC cells gene expression. To understand which processes were affected by modulation of GSNOR expression in BC, we performed RNA-seq on MCF7 cells with stable GSNOR knockdown (via CRISPR/Cas9) and on MCF7 cells ectopically overexpressing GSNOR. Gene Set Enrichment Analysis (GSEA) was performed to identify biological processes affected by GSNOR modulation. Enriched gene sets were clustered by similarity and visualized as a network map to highlight the most relevant altered processes (Figs. 3 A-B and Table S1 ). This analysis revealed that GSNOR deficiency mainly upregulated pathways related to cell adhesion, cytoskeleton reorganization, tissue morphogenesis, and proliferation (Fig. 3 A, in red ). These changes were potentially driven by the enrichment of signaling pathways such as WNT and growth factor-related signaling. Conversely, GSNOR deficiency was associated with downregulation of gene sets involved in mitochondrial metabolism, transport, and membrane organization (Fig. 3 A, in blue ). In contrast, GSNOR overexpression led to a marked reduction of in immune-related processes, including cytokine and interferon signaling (Fig. 3 B, in blue ), while promoting enrichment of processes involved in cell cycle regulation, chromatin remodeling, and DNA repair (Fig. 3 B, in red ). GSNOR overexpression—similar to its ablation—impaired mitochondrial metabolism and respiratory chain activity, consistent with previous reports showing that alteration in S-nitrosylation homeostasis can exert dual effects on mitochondrial physiology 23 , 53 , 54 . To identify pathways unidirectionally depending on GSNOR expression, we focused on the processes that were inversely regulated upon GSNOR depletion or overexpression by analyzing hallmark gene sets in parallel. This comparison revealed that hallmarks enriched in GSNOR-depleted cells—including the WNT/β-catenin pathway, apical junction, p53 signaling, and estrogen response—were consistently suppressed upon GSNOR overexpression (Fig. 3 C). While the relationship between GSNOR and p53 has been previously established 30 , the link to estrogen response is novel and may provide a mechanistic explanation for the reduced efficacy of endocrine therapy observed in GSNOR-low breast cancer patients. This observation warrants a dedicated clinical investigation beyond the scope of this work. Based on these results, we focused our subsequent analysis on apical junctions and WNT/β-catenin pathway, as these gene sets exhibited the most prominent opposing enrichment profiles between GSNOR-depleted and overexpressing cells (Fig. 3 D). Overall, these data indicate that GSNOR expression levels reshape the cellular transcriptome, likely through S-nitrosylation-dependent regulation of signaling pathways controlling cell motility, adhesion, proliferation, and mitochondrial homeostasis. Restoration of GSNOR levels reduces BC spheroids viability, cell invasion and motility. We then investigated the functional role of GSNOR in BC and its associations with malignant phenotype. To assess whether restoring GSNOR levels affected BC cell survival, we overexpressed GSNOR in mammospheres derived from MCF7 and MDA-MB-231. Results obtained indicated that GSNOR overexpression resulted in increased cell death (Fig. 4 A and Fig. S3 A ) and reduced mammospheres viability (Fig. 4 B) in both cell lines. Similar effects were observed in MCF7 mammospheres following transfection with anti-miR-455 and anti-miR-519a (Fig. 4 C). However, the reduction in cell viability induced by these anti-miRs was abolished when GSNOR was concomitantly silenced by siRNA (Figs. 4 C-D and Fig. S3 B ), indicating that these miRs regulate mammospheres viability primarily through modulation of GSNOR expression. In contrast to mammospheres, GSNOR overexpression did not induce cell death in adherent cells, consistent with our previous observations 18 . We than investigated the role of GSNOR deficiency in migratory and invasive capacity of BC cells. Reintroducing GSNOR in MCF7 cells reduced their ability to migrate through a Matrigel-coated membrane (Fig. 4 E), while wound-healing assays in MDA-MD-231 cells showed that GSNOR overexpression significantly delayed wound closure (Fig. 4 F). Overall, these results demonstrate that restoring GSNOR levels in breast cancer impairs cells survival in spheroid cultures, and suppresses invasive, and migratory behaviors. GSNOR re-expression reduces protein S-nitrosylation and suppress GSK-3β inhibition. Reduced GSNO catabolism resulting from miR-mediated repression of GSNOR is expected to lead to GSNO accumulation and, consequently, to increased protein S-nitrosylation (Fig. 5 A). Consistent with this model, we detected significant high levels of S-nitrosylated proteins in MCF7 cells which were markedly reduced upon ectopic re-expression of GSNOR (Fig. 5 B and Figs. S3A ) or upon neutralization of miR-455 and miR-519a with anti-miRs, which restore GSNOR levels (Fig. 5 C and Fig. S3 C ). To our knowledge, this represents the first evidence that specific miRNAs directly regulate global protein S-nitrosylated levels. We have recently shown that protein S-nitrosylation promotes the activation of focal adhesion kinase 1 (FAK1), thereby enhancing resistance to anoikis (detachment-induced cell death) and supporting tumor masses formation in vivo 18 . FAK1 is a key regulator of cell migration and EMT, two complex processes controlled by tightly coordinated kinase signaling and post-translational modifications. In this context, a recent study revealed that glycogen synthase kinase 3β (GSK3β)—a key inhibitor of canonical WNT signaling through phosphorylation-mediated β-catenin degradation—can itself be inhibited through S-nitrosylation. Inhibition of GSK3β promotes cell growth, differentiation and motility. These phenotypes closely mirror our experimental observations and are consistent with the transcriptional profile of MCF7 cells in which S-nitrosylation is modulated by miRs and GSNOR expression. On this basis, we analyzed the S-nitrosylated forms of both FAK1 and GSK3b in MCF7 cells and found that both proteins were abundantly S-nitrosylated under basal conditions (Figs. 5 D-E). Of note, the S-nitrosylation signal was almost lost upon GSNOR re-expression or following overexpression of anti-miR-455 and anti-miR-519a (Figs. 5 D-E). Since the effect of S-nitrosylation on FAK1 was extensively characterized in our previous work 18 , the present study focused primarily on GSK3β. A major mechanism of GSK3β inhibition is phosphorylation of its N-terminal Serine 9 (S9) residue by protein kinase B (PKB/Akt), which is also a FAK1 downstream target 55 . Western blot analysis revealed that GSK3β is phosphorylated at S9 under basal conditions in MCF7 cells, and that both GSNOR overexpression and anti-miR treatment markedly reduced this inhibitory phosphorylation (Figs. 5 F-G). Notably, S-nitrosylation has been reported to inhibit GSK3β activity independently of its phosphorylation status 56 . Consistent with this, our data indicate that oncogenic inhibitory modifications of GSK3β (both phosphorylation and S-nitrosylation) are reversed by restoring GSNOR levels in BC cells. Overall, these results suggest that GSNOR repression by miRs promotes a molecular profile potentially favoring BC cell survival and EMT through regulation of FAK1 and GSK3β S-nitrosylation (Fig. 5 A). GSNOR repression by microRNA regulates EMT via SNAI1/E-cadherin pathway. Cell migration and invasion frequently arise from EMT, a developmental program that is re-activated in cancer, transforming stationary epithelial cells into migratory and invasive cells. EMT is commonly associated with increased expression of SNAI1 (Snail family transcriptional repressor 1), a zinc-finger transcription factor that drives this process by repressing Cadherin-1 (CDH1, also known as E-Cadherin), a key component of cell–cell adhesion. GSK3b contributes to this pathway by regulating SNAI1 stability. Inhibition of GSK3β, indeed, stabilizes active SNAI1, which in turn represses CDH1 expression and activates EMT. In light of our results showing inhibitory phosphorylation and S-nitrosylation of GSK3β in BC cells (Figs. 5 D-G), we analyzed SNA1 and CDH1 expressions under basal conditions or following restoration of GSNOR levels. We observed that CDH1 protein levels increased upon GSNOR overexpression, while SNAI1 levels were reduced in both MCF7 and MDA-MB-231 cells (Fig. 6 A and S4 A). GSNOR overexpression also enhanced CDH1 expression at the transcriptional level, as confirmed by RT-qPCR in MCF7 (Fig. 6 B) and across a panel of BC cell lines ( Fig. S4 B ). SNAI1 and CDH1 expression and localization were further evaluated by confocal microscopy, which revealed that GSNOR overexpression reduced nuclear accumulation of SNAI1 while promoting CDH1 localization at cell–cell junctions (Figs. 6 C-D). Consistently, transfection of anti-miR-455 and anti-miR-519a in MCF7 cells, which restores GSNOR levels, resulted in a similar increase in CDH1 protein levels and decrease in SNAI1 (Figs. 6 E). Interestingly, RT-qPCR analyses showed that anti-miRs did not affect GSNOR mRNA levels but instead enhanced its translation into protein (Figs. 6 E–F). This increase was sufficient to mildly elevate CDH1 expression, with only anti-miR-519a inducing a statistically significant effect (Fig. 6 F). Nevertheless, both anti-miR-455 and anti-miR-519a reduced nuclear accumulation of SNAI1 and promoted CDH1 localization at cell–cell junctions—effects that mirrored those observed with GSNOR overexpression. Notably, these effects were abolished when anti-miRs were co-transfected with GSNOR siRNA (Figs. 6 G–H). Overall, these findings indicate that GSNOR upregulation suppresses EMT in BC cells, both directly and via inhibition of miR-455 and miR-519a. Mechanistically, this effect promotes CDH1 expression and membrane localization, along with reduced nuclear accumulation of SNAI1, likely through modulation GSK3β activity. GSNOR repression by miRs activates β-Catenin pathway GSK3β plays a central role in regulating the WNT/β-catenin signaling pathway, a key driver of tumor progression, proliferation, and stemness in several cancer types including BC 57 . Under physiological conditions, GSK3β phosphorylates β-catenin, targeting it for proteasomal degradation 58 . Inhibition of GSK3β prevents β-catenin phosphorylation, leading to its accumulation in the cytoplasm and subsequent nuclear translocation, where it activates transcription of oncogenic target genes. Given our findings that GSNOR expression modulates GSK3β through S-nitrosylation and inhibitory phosphorylation, we next investigated whether this regulation affects β-catenin activation in BC cells. To address this point, we measured levels of active (non-phosphorylated) β-catenin in MCF7 and MDA-MB-231 cells. GSNOR overexpression resulted in a marked reduction of active β-catenin protein levels in MCF7 while only minor effects were observed in MDA-MB-231 cells (Fig. 7 A). Similarly, transfection of anti-miR-455 and anti-miR-519a, which enhance GSNOR protein expression, led to decreased levels of active β-catenin, although statistical significance was reached only with anti-miR-519a in both cell lines (Fig. 7 B). To further evaluate the expression and subcellular distribution of active β-catenin, we performed confocal microscopy analyses. Both anti-miR-455 and anti-miR-519a significantly reduced active β-catenin signal intensity (Figs. 7 C-D), consistent with their ability to increase GSNOR protein levels (Fig. 7 B). Importantly, the reduction caused by anti-miRs on active β-catenin levels (both total and nuclear) was abolished upon GSNOR silencing with siRNA, confirming that the effects on β-catenin are GSNOR-dependent (Figs. 7 C–D). These results suggest that GSNOR re-expression in BC cells negatively regulates β-catenin signaling. This effect is linked to GSK3β activity and further supports GSNOR role as a tumor suppressor in breast cancer. DISCUSSION Despite decades of extensive research on NO signaling in cancer biology, a comprehensive understanding of the roles of this reactive and pleiotropic molecule in malignancy is still lacking. The complexity of NO biology in tumor development arises from several factors, including i) the dynamic regulation and subcellular localization of NO-producing enzymes, ii) the context-dependent nature of their activity 6 , 59 , and iii) the contribution of immune infiltrates, which generate massive NO fluxes that profoundly influence tumor biology 60 . Together, these elements make it difficult to define clear cause-effect relationships between NO signaling and cancer progression. Within this complex regulatory landscape, several reports have shown that expression of the major denitrosylating enzyme, GSNOR, is reduced in multiple human cancers and inversely correlates with patient survival, with BC representing one of the most significant associations 16 , 61 . This emerging body of evidence points to excessive protein S-nitrosylation, rather than NO levels per se , as a potential mechanism contributing to the tumorigenic effects of NO. Although this concept is progressively gaining recognition in the field, the mechanisms regulating GSNOR expression in BC, as well as the specific tumor phenotypes resulting from its downregulation remain largely unclear. Here, we addressed these issues. In particular, our experiments in human samples and BC cell lines provide evidence that reduced GSNOR expression is a common feature across all BC subtypes. We further demonstrate that specific oncogenic microRNAs, namely miR-455 and miR-519a, promote pro-tumorigenic processes via repression of GSNOR and, consequently, increase of protein S-nitrosylation. To our knowledge, this is the first study demonstrating that microRNAs can modulate global protein S-nitrosylation and thereby influence a broad set of proteins that are not their direct targets. We show that S-nitrosylation of oncogenes and tumor suppressors not only directly affects enzymatic activity and subcellular localization but also alters the susceptibility of these proteins to additional PTM, particularly phosphorylation. This multilevel regulation adds complexity to cancer signaling pathways influenced by NO via S-nitrosylation. From a mechanistic point of view, we show that GSNOR downregulation in BC cells promotes SNAI1 nuclear translocation and CDH1 repression through S-nitrosylation-mediated inhibition of GSK3β, a mechanism originally described in cardiomyocytes 62 . In parallel, inhibition of GSK3β resulting from GSNOR loss contributes to activation of the WNT/β-catenin signaling pathway. Of note, β-catenin itself is a direct target of S-nitrosylation 63 , suggesting that further layer of regulation may amplify WNT/β-catenin signaling under conditions of impaired denitrosylation. Crosstalk between different PTMs has been widely described in tumor biology. Well-known examples include the interplay between phosphorylation and ubiquitination in the regulation of protein stability 64 , as observed for β-catenin, whose phosphorylation drives ubiquitin-dependent degradation 65 , 66 , or p53, whose activity and turnover are controlled by coordinated phosphorylation, acetylation, and ubiquitination 67 , 68 . Similar interactions have also been reported between acetylation and phosphorylation 69 , 70 , as well as between SUMOylation and ubiquitination 71 , often influencing not only the modified protein but also interacting or functionally related components within the same signaling pathway. A similar regulatory mechanism has also been described for microRNAs, which can indirectly modulate the extent of specific PTMs by targeting enzymes responsible for their removal. In this regard, microRNAs have been shown to downregulate phosphatases or deacetylases, leading to sustained phosphorylation or acetylation of multiple downstream substrates and persistent activation of oncogenic signaling 72 , 73 . Well-established examples include miR-34a–mediated repression of the deacetylase SIRT1, resulting in increased p53 acetylation 74 , as well as miR-155 and miR-222 that target, respectively, the phosphatase SHIP1 and the PP2A regulatory subunit PPP2R2A, leading to sustained AKT phosphorylation 75 , 76 . Similarly, our data suggest that S-nitrosylation is an oncogenically relevant PTM regulated by microRNAs through modulation of reversing enzymes, specifically the denitrosylase GSNOR. While a comparable regulatory mechanism has already been reported for other redox-based PTMs (e.g., cysteine oxidation, S-glutathionylation)—where microRNAs have been shown to influence cellular redox homeostasis by repressing antioxidant enzymes and redox systems 77 , 78 —to our knowledge, this is the first study describing such a mechanism for S-nitrosylation. Overall, our findings indicate that microRNA-mediated regulation of protein S-nitrosylation, via the suppression of GSNOR expression, represents a mechanism through which they modulate global oncogenic signaling network, thereby sustaining the progression of BC and, potentially, of other tumors characterized by GSNOR downregulation. Declarations DECLARATION OF INTERESTS The authors declare no competing interests. ACKNOWLEDGEMENTS The authors are grateful to Laila Fisher for her secretarial assistance. This work was supported by: KBVU (R231-A13855, R352-A20537 to G.F.); the Novo Nordisk Foundation (NNF22OC0079352 to G.F.; NNF24OC0091871 to S.R.); the Italian Association for Cancer Research (IG2023-29221 to G.F.). L.I. was granted by the Horizon Europe MSCA Doctoral Network grant n. 101119873 (NO-CANCER-NET). AUTHOR CONTRIBUTIONS G.F. supervised the project. G.F. and S.R. conceived and designed the study and wrote the manuscript. SR prepared all figures. S.R., V.F. and G.M. conducted cell line experiments. G.M., S.R., and L.I. performed Western blot analyses. S.R. and C.C. analyzed human BC specimens. I.G. performed immunohistochemistry on human BC samples. L.V. and S.R. carried out RT-qPCR analyses. S.R. conducted microscopy experiments, and G.M. performed biotin-switch assays. C.P. and M.B.M. performed and analyzed the RNAseq data. V.F., G.P., and M.H.C. conducted the in-silico miRNA analysis. B.B. provided conceptual advice, critical and experimental support. All authors contributed to the description of the methods and results, edited, reviewed and approved the final manuscript. DATA AVAILABILITY The authors declare that all data supporting the findings of this study are available within the paper and its supplementary information files. All numeric data used to build histograms and graphs as well as the statistical test used for each experiment are reported in Table S2 . The RNAseq datasets generated during the current study are publicly available in GEO repository (GSE311614, GSE311283). This study did not generate new unique reagents. References Thomas DD, Wink DA. NOS2 as an Emergent Player in Progression of Cancer. Antioxid Redox Signal 2017; 26: 963–965. Granados-Principal S, Liu Y, Guevara ML, Blanco E, Choi DS, Qian W et al. Inhibition of iNOS as a novel effective targeted therapy against triple-negative breast cancer. Breast Cancer Research 2015; 17: 1–16. Ehrenfeld P, Cordova F, Duran WN, Sanchez FA. 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Boxplot showing GSNOR expression (ADH5 gene product) in a set of normal (n=114) and BC (n=1135) human samples from TCGA database. \u003cstrong\u003eB\u003c/strong\u003e. Kaplan–Meier survival analysis of patients bearing GSNOR-high (red) or GSNOR-low (black) breast tumors, generated using TCGA clinical data\u003csup\u003e79\u003c/sup\u003e. Tumors were stratified according to GSNOR protein levels. \u003cstrong\u003eC\u003c/strong\u003e.\u0026nbsp; Representative histological sections showing normal breast ducts (left) and paired tumor areas (right) within the same tissue section. Sections were stained with hematoxylin and eosin and immune-stained with an anti-GSNOR antibody. Original magnifications are indicated. \u003cstrong\u003eD\u003c/strong\u003e. Immunoblot analysis of GSNOR expression in breast cancer tissues (T) and matched adjacent normal tissues (N). β-actin was used as loading control. Quantification by densitometric analysis is shown on the right. \u003cstrong\u003eE\u003c/strong\u003e. RT–qPCR analysis of GSNOR mRNA levels in the same patient cohort shown in (D), expressed as log fold change in tumor versus matched non-tumor tissues. Expression levels were normalized to GAPDH. \u003cstrong\u003eF.\u003c/strong\u003e GSNOR enzymatic activity expressed as nmol of NADH consumed per mg of total protein per minute in normal and BC tissues. \u003cstrong\u003eG. \u003c/strong\u003eViolin plots\u003cstrong\u003e \u003c/strong\u003eshowing GSNOR expression (log2 fold change of TPM+1 values) across a panel of BC cell lines grouped according to histological subtype (left) or estrogen receptor and HER2 status (right). Each dot represents one cell line; the red dashed line indicates GSNOR expression in non-cancerous breast cells. The central line represents the median. \u003cstrong\u003eH\u003c/strong\u003e. Classification and immunoprofile of the non-cancerous MCF10A cell line and five BC cell lines, together with representative immunoblot analysis of GSNOR expression. GAPDH and β-actin were used as loading controls. boxplot, center line represents the median; box limits are the 25th and 75th percentiles; whiskers show 5-95 percentile in A and min to max range in D-F. Data were analyzed using two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e ≤ 0.01; ***, \u003cem\u003ep\u003c/em\u003e ≤ 0.001; ****, \u003cem\u003ep\u003c/em\u003e ≤ 0.0001.\u003c/p\u003e","description":"","filename":"Figure01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/2ea10384d5ba0515dc39ca2b.jpg"},{"id":100756733,"identity":"9f388052-d6cf-44c3-a401-5a4d06e3e562","added_by":"auto","created_at":"2026-01-21 06:40:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1745129,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSNOR expression in BC is regulated by microRNAs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eList of miRNAs predicted to target GSNOR and significantly modulated in GSNOR-low BC samples. Relative expression is reported as log2 fold change. \u003cstrong\u003eB-C.\u003c/strong\u003e Representative immunoblot analysis of GSNOR in MCF7 and MDA-MB-231 cells following ectopic expression of antimiRs (B) or miRNA mimics (C). Vinculin was used as a loading control. Bar graphs show densitometric quantification ± SD from at least three independent experiments. \u003cstrong\u003eD.\u003c/strong\u003e Sequence and secondary structure of miR-455 and miR-519a and their predicted binding sites within the ADH5 3’ UTR region (highlighted in light blue). \u003cstrong\u003eE.\u003c/strong\u003e Kaplan-Meier survival analysis of BC patients stratified according to miR-455 or miR-519a expression levels. High expression is shown in red and low expression in black. Data in (B) and (C) were analyzed using one-way ANOVA. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e ≤ 0.01; ***, \u003cem\u003ep\u003c/em\u003e ≤0 .001; \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/367786c2a2670d4e42218fbc.jpg"},{"id":100756835,"identity":"52826eaf-d946-4f76-b025-1fa38f77636d","added_by":"auto","created_at":"2026-01-21 06:41:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3815489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModulation of GSNOR alters gene expression programs in BC cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA, B.\u003c/strong\u003e Gene set enrichment analysis (GSEA) for Gene Ontology Biological Process (GO:BP) collections significantly enriched (adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in GSNOR-knockout (A) or GSNOR-overexpressing MCF7 cells compared with their respective controls. Network maps were generated in Cytoscape by grouping GO:BP terms based on default similarity metrics (combined Jaccard/Overlap coefficient). Clusters were manually annotated. Upregulated gene sets are in light red, whereas downregulated gene sets are in blue. Node size reflects the number of genes within each gene set, and edge thickness indicates the similarity coefficient. Detailed information for each node and gene set is provided in \u003cstrong\u003eSupplementary Table 1. C. \u003c/strong\u003eGSEA of the Hallmarks gene set in GSNOR-knock out (left) and GSNOR-overexpressing (right) MCF7 cells relative to their corresponding controls. The heat map shows normalized enrichment score (NES) for pathways with adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in at least one condition. Statistical significance is reported on the left for each pathway. Hallmark pathways differentially enriched between the two conditions are highlighted in grey. \u003cstrong\u003eD.\u003c/strong\u003e ERepresentative enrichment plots for the APICAL JUNCTION (top) and WNT_BETA_CATENIN_SIGNALING (bottom) Hallmark gene sets in GSNOR GSNOR-knockout (left) and GSNOR-overexpressing (right) MCF7 cells compared to their respective controls. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u0026nbsp;\u003cem\u003ep\u003c/em\u003e ≤ 0.01; ***, \u003cem\u003ep\u003c/em\u003e ≤ 0.001; ****, \u003cem\u003ep\u003c/em\u003e ≤ 0.0001; \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/e5370fe8dd634a4941f54b71.jpg"},{"id":100756717,"identity":"862bed83-dd98-4203-a5c5-5977f8ba9a27","added_by":"auto","created_at":"2026-01-21 06:39:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2067022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestoration of GSNOR levels reduces BC spheroids viability, cell invasion and motility.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA, C.\u003c/strong\u003e Representative confocal microscopy images (max projections of ≥10 z-stacks, 10 µm step size) of mammospheres derived from MCF7 and MDA-MD-231 mammospheres upon ectopic expression of GSNOR (plPCX-GSNOR) or a control vector (pLPCX) (A), and MCF7 cells upon transfection of anti-miRs (C). Nuclei (blue) were stained by Hoechst 33342, while live (green) and dead cells (red) were labeled with CalceinAM and BOBO-3 Iodide, respectively. \u003cstrong\u003eB, D.\u003c/strong\u003e Viability of the mammospheres shown in (A) and (C), assessed using the CellTiter 3D assay and expressed as relative luminescence units. Bar graphs represent mean ± SD of at least three independent experiments. \u003cstrong\u003eE.\u003c/strong\u003e Representative images of crystal violet-stained MCF7 cells in Matrigel invasion assays. Upper panels show cells seeded on the Matrigel-coated membrane following GSNOR overexpression (+GSNOR) or control transfection (mock). Lower panels show cells that migrated to the lower surface of the membrane. Quantification is shown as the mean ± SD of invading cells per fields from at least three independent experiments. \u003cstrong\u003eF.\u003c/strong\u003e Representative images from wound-healing assays performed in MDA-MB-231 cells following ectopic expression of GSNOR (+GSNOR) or control vector (Mock). Digital overlays highlight cells (green) and wound edges (yellow dashed lines). The graph shows the percentage of wound area closure from at least three independent experiments. Data in (B) and (E) were analyzed using unpaired \u003cem\u003et\u003c/em\u003e test. Data in (D) were analyzed using one-way ANOVA. Wound-healing data in (F) were analyzed using a paired \u003cem\u003et\u003c/em\u003e test. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e ≤ 0.01; ***, \u003cem\u003ep\u003c/em\u003e ≤ 0.001; \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/ac9f6ade29d50fe8338730cd.jpg"},{"id":100756730,"identity":"83747b65-71cc-428b-8bfc-a24fa5d6fb3f","added_by":"auto","created_at":"2026-01-21 06:40:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1196563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSNOR limits protein S-nitrosylation and suppresses inhibitory phosphorylation of GSK-3B.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eSchematic model showing the proposed mechanism. miR-519a-5p and miR-455-3p bind GSNOR mRNA and reduce its expression, leading to accumulation of S-nitrosoglutathione (GSNO). Through transnitrosylation reactions, GSNO promotes S-nitrosylation of target proteins. Among these targets, S-nitrosylation is proposed to activate focal adhesion kinase 1 (FAK1) and to inhibit glycogen synthase kinase 3β (GSK3β), resulting in enhanced cell survival and activation of pathways involved in cell migration and EMT. \u003cstrong\u003eB-C.\u003c/strong\u003e Representative biotin-switch assays showing total protein S-nitrosylation (PSNOs) in MCF7 cells following GSNOR overexpression (+GSNOR) (B) or ectopic expression of antimiR-455 and antimiR-519a (C) compared with the respective control conditions. Samples processed in the absence of ascorbate (−Asc) indicate background signal. \u003cstrong\u003eD, E.\u003c/strong\u003e Representative immunoblot of FAK1 and GSK3β, together with their nitrosylated (SNO) forms, in MCF7 cells upon GSNOR overexpression (+GSNOR) (D) or anti-miR-455 and anti-miR-519a expression (E), compared with corresponding controls. \u003cem\u003e-Asc\u003c/em\u003e show the background signal for the SNO forms of FAK1 and GSK3β. \u003cstrong\u003eF, G. \u003c/strong\u003eRepresentative immunoblot analysis of GSK3β and its phosphorylated form on serine 9 (pGSK3β\u003csup\u003eS9\u003c/sup\u003e) under the same experimental conditions shown in (D) and (E), respectively. Bar graphs in (D-G) show densitometric quantification ± SD from at least three independent experiments. Data in (D) and (F) were analyzed using a two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. Data in (E) and (G) were analyzed using one-way ANOVA. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e ≤ 0.01.\u003c/p\u003e","description":"","filename":"Figure05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/1a75654a0300a1c97effc29d.jpg"},{"id":100756723,"identity":"0b86e2fa-5708-4b1e-99a7-6e5db040e91e","added_by":"auto","created_at":"2026-01-21 06:39:59","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1874299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSNOR repression by microRNA regulates EMT via SNAI1/CDH1 pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA, E. \u003c/strong\u003eRepresentative immunoblot analysis of CDH1, SNAI1 and GSNOR in MCF7 cells following GSNOR overexpression (+GSNOR) (A) or ectopic expression of anti-miR-455 and anti-miR-519a (E) compared with the respective control conditions. Stain-free or vinculin were used as loading control, as indicated. Bar graphs show densitometric quantification ± SD from at least three independent experiments. \u003cstrong\u003eB, F.\u003c/strong\u003e RT-qPCR analysis of GSNOR and CDH1 mRNA levels in MCF7 cells following GSNOR overexpression (+GSNOR) (B) or ectopic expression of anti-miR-455 and anti-miR-519a (F), compared with the respective controls. Data are shown as mean ± SEM from at least three independent experiments performed in technical triplicates.\u003cstrong\u003e C, G.\u003c/strong\u003eRepresentative confocal microscopy images showing SNAI1 and CDH1 in MCF7 cells upon GSNOR overexpression (+GSNOR) (C) or following anti-miR-455 and anti-miR-519a expression in cells transfected with control siRNA (siSCR) or GSNOR-targeting siRNA (siGSNOR) (G). Nuclei (blue) were stained by Hoechst 33342. SNAI1 is shown in yellow and CDH1 in magenta. \u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eH.\u003c/strong\u003e Quantification of the percentage of nuclear SNAI1-positive signal per cell and of the normalized CDH1 fluorescence intensity per field corresponding to the images shown in (C) and (G), respectively. Violin plots report data distribution, with red lines indicating the median. Data in (A) and (B) were analyzed using a paired \u003cem\u003et\u003c/em\u003e test. Data in (D) were analyzed using a two-tailed unpaired \u003cem\u003et\u003c/em\u003e test. Data in (E), (F) and (H) were analyzed using one-way ANOVA. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e≤ 0.01; ****, \u003cem\u003ep\u003c/em\u003e ≤ 0.0001; \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure06.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/430c78c2480d369beb61522d.jpg"},{"id":100756897,"identity":"5df0b5d1-8271-4c8b-beff-51a95062c61d","added_by":"auto","created_at":"2026-01-21 06:41:32","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1487388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroRNAs-mediated repression of GSNOR activates β-Catenin pathway.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA, B. \u003c/strong\u003eRepresentative immunoblot analysis of the non-phosphorylated (active) form of β-Catenin and GSNOR in MCF7 and MDA-MB-231 cells following GSNOR overexpression (+GSNOR) (A) or ectopic expression of anti-miR-455 and anti-miR-519a (B), compared with the respective control conditions. Stain-free or vinculin was used as loading control, as indicated. Bar graphs show densitometric quantification ± SD from at least three independent experiments. \u003cstrong\u003eC.\u003c/strong\u003eRepresentative confocal microscopy images showing active β-Catenin localization in MCF7 cells following anti-miR-455 and anti-miR-519a expression in cells transfected with control siRNA (siSCR) or GSNOR-targeting siRNA (siGSNOR). Nuclei (blue) were stained with Hoechst 33342; active β-Catenin is shown in red. \u003cstrong\u003eD.\u003c/strong\u003e Quantification of nuclear active β-Catenin intensity by based on the images shown in (C). In the box plot, the center line represents the median, box limits indicate the 25th and 75th percentiles, and whiskers show 5th-95th percentile. Data in (A) were analyzed using a two-tailed paired \u003cem\u003et\u003c/em\u003e test. Data in (B) and (D) were analyzed using one-way ANOVA. *, \u003cem\u003ep\u003c/em\u003e ≤ 0.05; **, \u003cem\u003ep\u003c/em\u003e ≤ 0.01; ****, \u003cem\u003ep\u003c/em\u003e≤ 0.0001; \u003cem\u003ens\u003c/em\u003e, not significant.\u003c/p\u003e","description":"","filename":"Figure07.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/f8bf2d5eb72594d6d06187b8.jpg"},{"id":100798191,"identity":"72c664b5-41e8-4fbb-9408-add055a49476","added_by":"auto","created_at":"2026-01-21 13:53:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16353275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/a07a2877-b2d5-48c3-9eda-a14ce747d0ed.pdf"},{"id":100756788,"identity":"cedcc2b6-8e3d-4141-a842-9d0392d4e115","added_by":"auto","created_at":"2026-01-21 06:40:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":125758,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"BCTable1miRsandantimiRs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/77061ef053d0979876bb6482.pdf"},{"id":100756931,"identity":"14e36022-fccc-48e8-9cf2-0c9dc61f4e18","added_by":"auto","created_at":"2026-01-21 06:41:45","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":115413,"visible":true,"origin":"","legend":"Table 2","description":"","filename":"BCTable2Antibodies.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/de42ab10de19dcc754f74226.pdf"},{"id":100756759,"identity":"37bb8016-ff28-46a1-a060-7a0afc81c193","added_by":"auto","created_at":"2026-01-21 06:40:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SUPPLEMENTARYFIGURESlegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/06567c65f5ceedb25398f9c6.docx"},{"id":100756719,"identity":"5a8831be-db88-4bd3-87c4-cd3d3bdc988a","added_by":"auto","created_at":"2026-01-21 06:39:37","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2212308,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/6d481026d54c6e5d744c3fb4.jpg"},{"id":100756693,"identity":"56c543c8-8f15-45b9-9544-ffb0d8766e00","added_by":"auto","created_at":"2026-01-21 06:38:45","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1825227,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/3a70324905d707839c1f456e.jpg"},{"id":100796559,"identity":"b5997810-8c7f-4e88-82f3-d5814af8102a","added_by":"auto","created_at":"2026-01-21 13:44:11","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":872592,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3\u003c/p\u003e","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/4f5df441d9b1219026b3e0fb.jpg"},{"id":100756845,"identity":"5573806b-1403-440d-a4d1-1173fc93fe34","added_by":"auto","created_at":"2026-01-21 06:41:14","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1191356,"visible":true,"origin":"","legend":"Figure S4","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/c51304afc74b655a3358cc2a.jpg"},{"id":100756772,"identity":"36067fad-bf6d-40ac-97b5-c4571bc4504d","added_by":"auto","created_at":"2026-01-21 06:40:44","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1524876,"visible":true,"origin":"","legend":"Related Manuscript File","description":"","filename":"BCTableS1GSEAclustering.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/e6cc66a99ef10938b70b4cc6.xlsx"},{"id":100756904,"identity":"ce8578f7-aaab-42d7-b580-21b3becb86f6","added_by":"auto","created_at":"2026-01-21 06:41:33","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":96019,"visible":true,"origin":"","legend":"Related Manuscript File","description":"","filename":"BCTableS2Rawstatistics.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8532436/v1/9849f4036c490b37956745d0.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"\u003cp\u003ePost-transcriptional repression of GSNOR by microRNAs regulates S-nitrosylation and fuels breast cancer progression\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC) is the most frequent cancer in women. Despite the decreasing mortality rate due to early detection and more advanced therapies, BC remains a major cause of cancer mortality owing to heterogeneous molecular features and unpredictable responses to therapy. Among a series of well-described genetic and environmental factors, nitric oxide (NO) is known to play a role in BC onset, progression, and response to therapy\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. NO activation of oncogenic pathways in BC has been associated with upregulation of the inducible isoform of nitric oxide synthase\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, many aspects of NO signaling in BC are still unclear\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The chemical addition of NO to specific cysteines, a reaction named S-nitrosylation, constitutes a crucial NO-induced posttranslational modification of proteins governing cancer-related pathways\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Because of its extremely short half-life and high reactivity, NO binding to proteins and low-molecular-weight thiols ensures a longer spatial and temporal action\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. S-nitrosylation of glutathione, the most abundant free thiol in cells, produces S-nitrosoglutathione (GSNO), which acts as a reservoir of NO due to slow NO-release kinetics and the capability of exchanging NO moiety with proteins\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. GSNO catabolism is carried out by the evolutionary conserved denitrosylase S-nitrosoglutathione reductase (GSNOR)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. GSNOR deficiency has been associated with the development of liver cancer\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, enhanced chemoresistance of human epidermal growth factor receptor 2-positive (HER2\u003csup\u003e+\u003c/sup\u003e) BC\u003csup\u003e14\u003c/sup\u003e, metabolic shift and immune evasion in colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This evidence indicates that S-nitrosylation plays a primary role in neoplastic transformation and progression\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recently, we have reported that GSNOR expression is reduced in multiple human cancers, with BC representing one of the major hits\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The lack of GSNOR sustains S-nitrosylation of focal adhesion kinase 1 (FAK1) at Cys-658 which, in turn, promotes FAK1 phosphorylation and resistance to \u003cem\u003eanoikis\u003c/em\u003e, finally resulting in enhanced growth of tumor masses\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Despite these studies arguing for GSNOR acting as a tumor suppressor, the mechanisms that regulate GSNOR expression in cancer are still unknown.\u003c/p\u003e \u003cp\u003eIn this work, we demonstrate that microRNAs \u0026ndash; that are notoriously involved in BC onset and progression - impact redox signaling through regulation of GSNOR, thereby rewiring S-nitrosylation and BC-associated signaling pathways. This unveils a novel regulatory axis where microRNA activity converges with redox signaling, profoundly influencing breast cancer progression.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e \u003cb\u003eCell cultures.\u003c/b\u003e Breast cancer cell lines MCF-7, MDA-MB-231, ZR-75-1, HCC38, T47D and MCF10A were purchased from ATCC (American Type Culture Collection). Cells were maintained in a humidified 5% CO2, 37\u0026deg;C incubator. MCF-7, MDA-MB-231, ZR-75-1 were grown in DMEM (Thermo-Fisher), whereas HCC38 and T47D were cultured in RPMI (Thermo-Fisher). MCF10A were cultured in MEGM (Lonza) supplemented with 100 ng/mL cholera toxin (Sigma-Aldrich). DMEM and RPMI were supplemented with a mixture of penicillin and streptomycin 10 U/ml 1% (v/v) and 10% (v/v) FBS (Thermo-Fisher). Mycoplasma contamination was routinely screened by a PCR-based assay (Eurofins Genomics).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMammospheres.\u003c/b\u003e Mammospheres were produced by seeding 750 (MCF7) or 1500 (MDA-MB-231) cells/well into ultra-low attachment U-bottom 96-multiwells (Corning, CLS7007-24EA) in standard growing conditions for 7-to-14 days, with 1/3 of the medium refreshed every 3 days. Mammospheres cell death was revealed by ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices) upon staining of spheroids with LIVE/DEAD\u0026reg; Cell Imaging Kit (488/570) (Thermo-Fisher Scientific, R37601) and Hoechst 33342 (Thermo-Fisher Scientific, 62249). Acquisition of images and 3D rendering of spheroids were performed using MetaXpress software (Molecular Devices). Mammosphere viability was assessed by CellTiter-Glo 3D Cell Viability Assay (Promega, G9681) following manufacturer's protocol. A Victor X4 plate reader (PerkinElmer) was used to record luminescence.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransfections and treatments.\u003c/b\u003e Overexpression of protein constructs was performed using Lipofectamine 3000 (Thermo Fisher Scientific, L3000001) according to manufacturer's instruction. The plasmids used in this work were generated in our laboratory. ADH5 cDNA coding for GSNOR was cloned into the vector pLPCX (Clontech). Anti-miRNAs and miRNA mimics were transfected into the cells by RNAiMAX (Thermo Fisher Scientific) according to manufacturer\u0026rsquo;s protocol. The list of miRs and antimiRs used is reported in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTransient knock-down was performed by transfecting the cells with RNAiMAX (Thermo Fisher Scientific) endonuclease-prepared pools of siRNAs (esiRNA, Sigma-Aldrich) directed against ADH5 (siGSNOR, EHU104681), or with a scramble duplex (siSCR, SIC001).\u003c/p\u003e \u003cp\u003e \u003cb\u003emiRNAs analysis.\u003c/b\u003e The list of miRNAs targeting GSNOR overexpressed in GSNOR-downregulating BC samples has been drawn up with the following procedure. TCGA samples from 97 BC patients with paired tumour/adjacent normal tissue miRNA-seq and RNA-seq data were included in the analysis. RAID v2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rna-society.org/raid/\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e21\u003c/sup\u003e was used to identify miRNAs targeting GSNOR mRNA. We selected only experimentally validated hits from 'CLASH', 'CLIP-seq' and 'PAR-CLIP' experiments. Finally, we investigated the expression of these miRNAs in the same 97 BC patients from TCGA utilizing DESeq2 to analyze differential expression between paired tumor and adjacent normal samples\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our final list of candidates is reported along with their log2FoldChange (FC) and adjusted p-values (FDR-corrected for multiple testing).\u003c/p\u003e \u003cp\u003e \u003cb\u003eWestern blotting.\u003c/b\u003e Whole cell protein extracts were obtained in lysis buffer (50 mM Tris HCl, pH 6.8, 2% SDS, 10% glycerol) followed by denaturation for 10 min at 98\u0026deg;C. Protein extracts from tumor biopsies were obtained upon homogenization in RIPA Buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM EDTA, 5 mM MgCl2, 1% Triton X-100, 0.25% Sodium Deoxycholate, 0.1% SDS, 5 mM β-glycerophosphate, 5 mM sodium fluoride, 2 mM sodium orthovanadate, protease inhibitor cocktail-P8340). Proteins were quantified by DC Protein Assay Kit (Bio-Rad, 5000116). Wester Blotting procedure was performed as reported\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Primary antibodies used and dilutions are listed in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiotin-switch assay.\u003c/b\u003e Protein S-nitrosylation was evaluated by biotin-switch assay as previously described\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Briefly, cells were homogenized in HEN buffer (HEPES-NaOH 250 mM, EDTA 1 mM, 0.1 mM Neocuproine, 1% Triton X-100, protease inhibitors, pH 7.8). Free cysteine residues were blocked in S-methyl methane-thiosulfonate 50 \u0026micro;M (MMTS, Thermo-Fisher Scientific) and 2.5% SDS and incubated for 30 min at 50\u0026deg;C. Proteins were then precipitated with cold acetone, collected by centrifugation, resuspended in HEN buffer with 1% (HEN/S) and incubated with EZ-Link HPDP-biotin 250 \u0026micro;M (Thermo-Fisher Scientific) in the presence or absence of sodium ascorbate 50 mM. Biotinylated proteins were then pulled down with agarose streptavidin beads and eluted in NuPAGE LDS Sample Buffer (Thermo-Fisher Scientific) and DTT. After incubation with the HRP-streptavidin (Cell Signaling), biotinylated proteins were revealed using the ECL Prime detection system (Amersham).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRNA sequencing.\u003c/b\u003e MCF7 cells were grown in adhesion for 48 hours and total RNA was purified using RNeasy Plus kit (Qiagen, 74134) following producer's protocol. RNAseq of GSNOR-KO and parental WT MCF7 cells was performed by Next Generation Diagnostic srl c/o TIGEM while GSNOR-overexpressing and parental cells were analysed by BMKGENE (Biomarker Technologies). Raw gene-level count matrices were obtained from the sequencing provider and analyzed in R (v4.x) using RStudio Desktop (2024.12.1\u0026thinsp;+\u0026thinsp;563). Differential expression was performed using DESeq2\u003csup\u003e22\u003c/sup\u003e. Gene Set Enrichment Analysis (GSEA) was performed with the multilevel algorithm implemented in fgsea\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Gene sets for Hallmark and GO Biological Process collections were retrieved from MSigDB via msigdbr\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Significant pathways (p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized using heatmaps generated in R (pheatmap, ggplot2) and further explored using EnrichmentMap in Cytoscape\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e with default similarity metrics (combined Jaccard/Overlap). Only significantly enriched (padj\u0026thinsp;\u0026lt;\u0026thinsp;0,05) Hallmark and GO:BP pathways were included in both heatmap and network visualizations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRT-qPCR.\u003c/b\u003e Total RNA was extracted from cells by using the ReliaPrep RNA Cell Miniprep System (Promega) following the protocol of the manufacturer. Complementary DNA (cDNA) was synthetized by using the reverse transcriptase M-MLV (Promega) and followed manufacturer\u0026rsquo;s protocol. RT-qPCR was performed by using iTaq Universal SYBR Green Supermix (Bio-Rad) subjected to 35 cycles of amplification with a ViiA 7 RT-qPCR System (Thermo-Fisher). Data were normalized using the ribosomal protein L34 as housekeeping. Relative fold gene expression was calculated using the formula RQ\u0026thinsp;=\u0026thinsp;2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e, as reported \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Primers used for RT-qPCR analysis were the following:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL34 - FW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GGCCCTGCTGACATGTTTCTT-3\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL34 -RV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-GTCCCGAACCCCTGGTAATAGA-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSNOR - FW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CATTGCCACTGCGGTTTGCCAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSNOR - RV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-AGTGTCACCCGCCTTCAGCTTAGT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH1 \u0026ndash; FW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-TGGACCGAGAGAGTTTCCCT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDH1 - RV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;-CCCTTGTACGTGGTGGGATT-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eS-nitrosoglutathione reductase activity.\u003c/b\u003e S-nitrosoglutathione reductase activity was carried out on protein extracts obtained by osmotic lysis of cells and tissue in distilled water, followed by sonication. Cell lysates were clarified by centrifugation and the activity determined using a spectrophotometer as reported\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWound-Healing and Boyden-chamber assays.\u003c/b\u003e MDA-MB-231 cells were counted and 8x10\u003csup\u003e4\u003c/sup\u003e cells were resuspended in 100 \u0026micro;l of complete medium and seeded in wound healing assay chambers (Ibidi) placed in multiwell plates. Cells were incubated overnight at 37\u0026deg; C and 5% CO\u003csub\u003e2\u003c/sub\u003e. The following day full medium replaced with serum-free medium, the septum-forming insert was carefully removed, and images were acquired at different time points as indicated using a Celigo imaging cytometer (Revvity). Wound size was calculated by Fiji ImageJ\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e software.\u003c/p\u003e \u003cp\u003eCell invasion was assessed using a Matrigel-coated Boyden chamber (Corning, 8 \u0026micro;m pores). MCF-7 cells were seeded in serum-free medium into the upper chamber, with medium containing 10% FBS as a chemoattractant in the lower chamber. After 24 hours, non-invading cells were removed from the upper surface. The invaded cells on the lower membrane surface were fixed and stained for 30 minutes with a crystal violet solution (0.05% w/v crystal violet, 1% formaldehyde, 1% methanol; all chemicals from Sigma-Aldrich). Membranes were imaged using an Olympus brightfield microscope with a color camera at 10X magnification, and invaded cells were counted using Fiji ImageJ\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e software.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConfocal fluorescence microscopy.\u003c/b\u003e Cells were grown on Screenstar microplates (Greiner) and fixed with 4% paraformaldehyde (Sigma-Aldrich), incubated with a permeabilization solution (PBS/Triton X-100 0,4% v/v) and blocked for 1 h with a blocking solution (PBS/FBS 10% v/v). Afterwards, cells were incubated over night at 4\u0026ordm;C with anti-SNAI1 and anti-CDH1 or with an anti-non-phopsho beta-catenin antibodies (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). After three washes in PBS, cells were incubated for 1 h with anti-mouse Alexa Fluor 568 and anti-rabbit Alexa Fluor 647 IgG Secondary Antibodies (Thermo-Fisher Scientific). Nuclei were stained with Hoechst 33342. Images of cells were acquired by a Cell discovery 7 microscope (Carl Zeiss) and ZEN microscope software (Carl Zeiss). Fluorescence images were adjusted for brightness, contrast, and color balance by using Fiji ImageJ analysis software. Images were deconvoluted using the software Huygens Professional (Scientific Volume Imaging). The analysis of SNAI1 nuclear localization, CDH1 and non-phospho beta-catenin intensity was performed using Arivis Pro 4.4.0 (Zeiss) software.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBC samples and Immunohistochemistry.\u003c/b\u003e BC samples were collected by the Department of Pathology at the Copenhagen University Hospital. The project was approved (KF 01\u0026ndash;069/03) by the \u003cem\u003eCopenhagen and Frederiksberg regional division of the Danish National Committee on Biomedical Research Ethics.\u003c/em\u003e Written informed consent was obtained from each patient included in the study. Clinicopathological information was provided by the Department of Pathology, Copenhagen University Hospital. Fresh tissue samples were partitioned immediately following surgery, with a part being snap-frozen and a fraction fixed in neutral buffered formalin and paraffin embedded for IHC-based analyses. The complete procedure of sample collecting, processing and IHC analysis has been previously described\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Anti-ADH5 (GSNOR) antibody used was purchased by Sigma-Aldrich (HPA044578).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGSNOR expression and promoter methylation analyses.\u003c/b\u003e The expression of ADH5 in BC was carried out by using UALCAN\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Welch\u0026rsquo;s T-test estimated the significance of differences in expression levels between normal and primary tumors or tumor subgroups based on clinicopathological features as reported\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The relative expression of GSNOR in BC cell lines and the analysis of ADH5 gene methylation in BC cells and human samples has been performed on Depmap portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://depmap.org\u003c/span\u003e\u003cspan address=\"https://depmap.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantification and statistical analysis.\u003c/b\u003e Statistical significance of all the data presented in this work was calculated using GraphPad Prism v.10 software.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eGSNOR expression decreases in BC.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eReduced GSNOR expression confers resistance to cell death\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and lowers the anticancer efficacy of trastuzumab in HER2\u003csup\u003e+\u003c/sup\u003e BC\u003csup\u003e14\u003c/sup\u003e. Based on this observation we wondered whether GSNOR expression changed in different subtypes and stages of BC. Comparative analysis of \u003cem\u003eThe Cancer Genome Atlas\u003c/em\u003e (TCGA) between tumor and paired healthy tissues confirmed our previous observations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, revealing a marked downregulation of GSNOR (ADH5) in BC samples (log2FoldChange\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.80, p\u0026thinsp;=\u0026thinsp;1.24 \u0026times; 10⁻\u0026sup2;⁹) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. GSNOR levels were significantly lower in all the stages of the disease and cancer subtypes (i.e., Luminal, HER2\u003csup\u003e+\u003c/sup\u003e, triple negative) if compared to paired normal breast biopsies (\u003cb\u003eFigs. S1A-B\u003c/b\u003e). In addition, we found that low GSNOR levels correlated with poor prognosis of BC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), with Luminal B and HER2\u003csup\u003e+\u003c/sup\u003e subtypes driving the overall statistics (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). In line with previous results\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, high levels of GSNOR represented a positive marker of endocrine therapy efficacy, although it was not a strong predictive factor of patient survival upon generic chemotherapy (\u003cb\u003eFig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD\u003c/b\u003e). Immunohistochemistry (IHC) analyses of human paraffin-embedded biopsies from BC patients confirmed that GSNOR was almost undetectable in tumor areas if compared with paired normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We also observed that GSNOR protein levels and activity, as well as mRNA levels, were significantly lower than paired adjacent healthy specimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then analyzed GSNOR expression in human breast-derived cell lines confirming that the great majority of cancer cells\u0026mdash;sorted according to tumor subtype or classified according to the expression status of estrogen receptor (ER) and HER2\u0026mdash;showed lower GSNOR expression than a non-cancerous breast cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Among these cell lines, we selected five representative models of the different BC subtypes and one non-cancerous epithelial breast cell line (MCF10A) for validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Evaluation of protein and mRNA levels of GSNOR, as well as its enzymatic activity, was significantly lower in all BC cells compared to their non-tumor counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH \u003cb\u003eand Figs. S1E, F\u003c/b\u003e). These results indicate that GSNOR loss typifies all BC subtypes, regardless of differences they might have in genetic backgrounds and tumorigenic phenotypes.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eGSNOR expression in BC is regulated by microRNAs.\u003c/b\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe next investigated the mechanisms contributing to GSNOR decrease in BC. We previously reported that GSNOR expression declines with aging due to the methylation of CpG islands at the promoter of the \u003cem\u003eADH5\u003c/em\u003e gene, which encodes GSNOR\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Based on this evidence, we analyzed the methylation status of the \u003cem\u003eADH5\u003c/em\u003e promoter in biopsies from BC patients and a panel of several BC cell lines. Results obtained did not reveal a considerable rate in promoter methylation of both tumor and non-tumor breast samples and BC cell lines (\u003cb\u003eFigs. S2A- B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigh-throughput studies predicted that GSNOR might be regulated by microRNAs (miRs)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The presence in the genome of GSNOR pseudogenes, which can act as competing endogenous RNAs (ceRNAs) through a mechanism reported in invasive BC\u003csup\u003e39\u003c/sup\u003e, indirectly suggests a post-transcriptional regulation of GSNOR expression, such as that operated by miRs\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Based on this, we identified miRs differentially expressed in BC patients with low GSNOR levels from the TCGA database. We then integrated this list with computationally and experimentally validated GSNOR-targeting miRs from the RAID v2.0 database\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This analysis provided a list of GSNOR-targeting miRs that are significantly modulated in BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We then focused on miRs that were upregulated, and investigated their capability of modulating GSNOR expression by neutralizing them with antisense oligonucleotides (anti-miRs)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. We initially confirmed that both cell lines express the above-mentioned miRs\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC\u003c/b\u003e) and validated anti-miRs transfection efficiency by fluorescent reporters (\u003cb\u003eFigs. S2D-E\u003c/b\u003e). We evaluated GSNOR expression upon transfecting the correspondent anti-miRs of the five GSNOR-targeting miRs reported to be the most significant (\u003cem\u003emiR-455-3p, miR-519a-5p, miR-182-5p, miR-9-5p, miR-449a\u003c/em\u003e) in the luminal MCF7 and triple negative MDA-MB-231 cell lines. To note, -3p and \u0026minus;\u0026thinsp;5p designate the mature miRNA sequences derived from the 3' and 5' arms of the precursor miR hairpin structure, respectively. This nomenclature is omitted when pre-miRs produce a unique mature miR. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB shows that anti- miR-455-3p and anti-miR-519a-5p significantly rescue GSNOR expression in MCF7 while only anti-miR-519a-5p was effective in MDA-MB-231. These data were supported by results obtained upon overexpression of miR-455-3p and miR-519a-5p mimics into both cell lines, which reduced GSNOR levels with a similar pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe structures and sequences of miR-455-3p and miR-519a-5p are well characterized (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eF\u003c/b\u003e). The binding region of both miR-455-3p and miR-519a-5p on GSNOR mRNA maps at the 3\u0026rsquo;UTR. This was already validated in cellular systems by CLIP sequencing (GSE42701\u003csup\u003e45\u003c/sup\u003e, GSE44404\u003csup\u003e46\u003c/sup\u003e) and reported in miRTarBase, the database of experimentally validated microRNA-target interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Both miRs have been linked to cancer biology due to their role in cell proliferation, chemoresistance and cell migration\u003csup\u003e\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In line with this, we found out that BC patients expressing high levels of miR-519a-5p have a poor prognosis, with a similar trend shown by miR-455-3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. However, to date, no functional link between these two miRs and GSNOR has not yet been reported.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eGSNOR modulation alters BC cells gene expression.\u003c/b\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo understand which processes were affected by modulation of GSNOR expression in BC, we performed RNA-seq on MCF7 cells with stable GSNOR knockdown (via CRISPR/Cas9) and on MCF7 cells ectopically overexpressing GSNOR. Gene Set Enrichment Analysis (GSEA) was performed to identify biological processes affected by GSNOR modulation. Enriched gene sets were clustered by similarity and visualized as a network map to highlight the most relevant altered processes (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B \u003cb\u003eand Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). This analysis revealed that GSNOR deficiency mainly upregulated pathways related to cell adhesion, cytoskeleton reorganization, tissue morphogenesis, and proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003ein red\u003c/b\u003e). These changes were potentially driven by the enrichment of signaling pathways such as WNT and growth factor-related signaling. Conversely, GSNOR deficiency was associated with downregulation of gene sets involved in mitochondrial metabolism, transport, and membrane organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003ein blue\u003c/b\u003e). In contrast, GSNOR overexpression led to a marked reduction of in immune-related processes, including cytokine and interferon signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003ein blue\u003c/b\u003e), while promoting enrichment of processes involved in cell cycle regulation, chromatin remodeling, and DNA repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cb\u003ein red\u003c/b\u003e). GSNOR overexpression\u0026mdash;similar to its ablation\u0026mdash;impaired mitochondrial metabolism and respiratory chain activity, consistent with previous reports showing that alteration in S-nitrosylation homeostasis can exert dual effects on mitochondrial physiology\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify pathways unidirectionally depending on GSNOR expression, we focused on the processes that were inversely regulated upon GSNOR depletion or overexpression by analyzing hallmark gene sets in parallel. This comparison revealed that hallmarks enriched in GSNOR-depleted cells\u0026mdash;including the WNT/β-catenin pathway, apical junction, p53 signaling, and estrogen response\u0026mdash;were consistently suppressed upon GSNOR overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). While the relationship between GSNOR and p53 has been previously established\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, the link to estrogen response is novel and may provide a mechanistic explanation for the reduced efficacy of endocrine therapy observed in GSNOR-low breast cancer patients. This observation warrants a dedicated clinical investigation beyond the scope of this work.\u003c/p\u003e \u003cp\u003eBased on these results, we focused our subsequent analysis on apical junctions and WNT/β-catenin pathway, as these gene sets exhibited the most prominent opposing enrichment profiles between GSNOR-depleted and overexpressing cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOverall, these data indicate that GSNOR expression levels reshape the cellular transcriptome, likely through S-nitrosylation-dependent regulation of signaling pathways controlling cell motility, adhesion, proliferation, and mitochondrial homeostasis.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRestoration of GSNOR levels reduces BC spheroids viability, cell invasion and motility.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eWe then investigated the functional role of GSNOR in BC and its associations with malignant phenotype. To assess whether restoring GSNOR levels affected BC cell survival, we overexpressed GSNOR in mammospheres derived from MCF7 and MDA-MB-231. Results obtained indicated that GSNOR overexpression resulted in increased cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA \u003cb\u003eand Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e) and reduced mammospheres viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) in both cell lines. Similar effects were observed in MCF7 mammospheres following transfection with anti-miR-455 and anti-miR-519a (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). However, the reduction in cell viability induced by these anti-miRs was abolished when GSNOR was concomitantly silenced by siRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D \u003cb\u003eand Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u003c/b\u003e), indicating that these miRs regulate mammospheres viability primarily through modulation of GSNOR expression. In contrast to mammospheres, GSNOR overexpression did not induce cell death in adherent cells, consistent with our previous observations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. We than investigated the role of GSNOR deficiency in migratory and invasive capacity of BC cells. Reintroducing GSNOR in MCF7 cells reduced their ability to migrate through a Matrigel-coated membrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), while wound-healing assays in MDA-MD-231 cells showed that GSNOR overexpression significantly delayed wound closure (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Overall, these results demonstrate that restoring GSNOR levels in breast cancer impairs cells survival in spheroid cultures, and suppresses invasive, and migratory behaviors.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGSNOR re-expression reduces protein S-nitrosylation and suppress GSK-3β inhibition.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eReduced GSNO catabolism resulting from miR-mediated repression of GSNOR is expected to lead to GSNO accumulation and, consequently, to increased protein S-nitrosylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Consistent with this model, we detected significant high levels of S-nitrosylated proteins in MCF7 cells which were markedly reduced upon ectopic re-expression of GSNOR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cb\u003eFigs. S3A\u003c/b\u003e) or upon neutralization of miR-455 and miR-519a with anti-miRs, which restore GSNOR levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eC \u003cb\u003eand Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC\u003c/b\u003e). To our knowledge, this represents the first evidence that specific miRNAs directly regulate global protein S-nitrosylated levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe have recently shown that protein S-nitrosylation promotes the activation of focal adhesion kinase 1 (FAK1), thereby enhancing resistance to anoikis (detachment-induced cell death) and supporting tumor masses formation in vivo\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. FAK1 is a key regulator of cell migration and EMT, two complex processes controlled by tightly coordinated kinase signaling and post-translational modifications.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this context, a recent study revealed that glycogen synthase kinase 3β (GSK3β)\u0026mdash;a key inhibitor of canonical WNT signaling through phosphorylation-mediated β-catenin degradation\u0026mdash;can itself be inhibited through S-nitrosylation. Inhibition of GSK3β promotes cell growth, differentiation and motility. These phenotypes closely mirror our experimental observations and are consistent with the transcriptional profile of MCF7 cells in which S-nitrosylation is modulated by miRs and GSNOR expression.\u003c/p\u003e\u003cp\u003eOn this basis, we analyzed the S-nitrosylated forms of both FAK1 and GSK3b in MCF7 cells and found that both proteins were abundantly S-nitrosylated under basal conditions (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). Of note, the S-nitrosylation signal was almost lost upon GSNOR re-expression or following overexpression of anti-miR-455 and anti-miR-519a (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E). Since the effect of S-nitrosylation on FAK1 was extensively characterized in our previous work\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, the present study focused primarily on GSK3β. A major mechanism of GSK3β inhibition is phosphorylation of its N-terminal Serine 9 (S9) residue by protein kinase B (PKB/Akt), which is also a FAK1 downstream target\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Western blot analysis revealed that GSK3β is phosphorylated at S9 under basal conditions in MCF7 cells, and that both GSNOR overexpression and anti-miR treatment markedly reduced this inhibitory phosphorylation (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). Notably, S-nitrosylation has been reported to inhibit GSK3β activity independently of its phosphorylation status\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Consistent with this, our data indicate that oncogenic inhibitory modifications of GSK3β (both phosphorylation and S-nitrosylation) are reversed by restoring GSNOR levels in BC cells.\u003c/p\u003e\u003cp\u003eOverall, these results suggest that GSNOR repression by miRs promotes a molecular profile potentially favoring BC cell survival and EMT through regulation of FAK1 and GSK3β S-nitrosylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e \u003cb\u003eGSNOR repression by microRNA regulates EMT via SNAI1/E-cadherin pathway.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eCell migration and invasion frequently arise from EMT, a developmental program that is re-activated in cancer, transforming stationary epithelial cells into migratory and invasive cells. EMT is commonly associated with increased expression of SNAI1 (Snail family transcriptional repressor 1), a zinc-finger transcription factor that drives this process by repressing Cadherin-1 (CDH1, also known as E-Cadherin), a key component of cell\u0026ndash;cell adhesion. GSK3b contributes to this pathway by regulating SNAI1 stability. Inhibition of GSK3β, indeed, stabilizes active SNAI1, which in turn represses CDH1 expression and activates EMT. In light of our results showing inhibitory phosphorylation and S-nitrosylation of GSK3β in BC cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-G), we analyzed SNA1 and CDH1 expressions under basal conditions or following restoration of GSNOR levels. We observed that CDH1 protein levels increased upon GSNOR overexpression, while SNAI1 levels were reduced in both MCF7 and MDA-MB-231 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). GSNOR overexpression also enhanced CDH1 expression at the transcriptional level, as confirmed by RT-qPCR in MCF7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) and across a panel of BC cell lines (\u003cb\u003eFig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB\u003c/b\u003e). SNAI1 and CDH1 expression and localization were further evaluated by confocal microscopy, which revealed that GSNOR overexpression reduced nuclear accumulation of SNAI1 while promoting CDH1 localization at cell\u0026ndash;cell junctions (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). Consistently, transfection of anti-miR-455 and anti-miR-519a in MCF7 cells, which restores GSNOR levels, resulted in a similar increase in CDH1 protein levels and decrease in SNAI1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Interestingly, RT-qPCR analyses showed that anti-miRs did not affect GSNOR mRNA levels but instead enhanced its translation into protein (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u0026ndash;F). This increase was sufficient to mildly elevate CDH1 expression, with only anti-miR-519a inducing a statistically significant effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Nevertheless, both anti-miR-455 and anti-miR-519a reduced nuclear accumulation of SNAI1 and promoted CDH1 localization at cell\u0026ndash;cell junctions\u0026mdash;effects that mirrored those observed with GSNOR overexpression. Notably, these effects were abolished when anti-miRs were co-transfected with GSNOR siRNA (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eG\u0026ndash;H).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, these findings indicate that GSNOR upregulation suppresses EMT in BC cells, both directly and via inhibition of miR-455 and miR-519a. Mechanistically, this effect promotes CDH1 expression and membrane localization, along with reduced nuclear accumulation of SNAI1, likely through modulation GSK3β activity.\u003c/p\u003e\n\u003ch3\u003eGSNOR repression by miRs activates β-Catenin pathway\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGSK3β plays a central role in regulating the WNT/β-catenin signaling pathway, a key driver of tumor progression, proliferation, and stemness in several cancer types including BC\u003csup\u003e57\u003c/sup\u003e. Under physiological conditions, GSK3β phosphorylates β-catenin, targeting it for proteasomal degradation\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Inhibition of GSK3β prevents β-catenin phosphorylation, leading to its accumulation in the cytoplasm and subsequent nuclear translocation, where it activates transcription of oncogenic target genes. Given our findings that GSNOR expression modulates GSK3β through S-nitrosylation and inhibitory phosphorylation, we next investigated whether this regulation affects β-catenin activation in BC cells.\u003c/p\u003e \u003cp\u003eTo address this point, we measured levels of active (non-phosphorylated) β-catenin in MCF7 and MDA-MB-231 cells. GSNOR overexpression resulted in a marked reduction of active β-catenin protein levels in MCF7 while only minor effects were observed in MDA-MB-231 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Similarly, transfection of anti-miR-455 and anti-miR-519a, which enhance GSNOR protein expression, led to decreased levels of active β-catenin, although statistical significance was reached only with anti-miR-519a in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the expression and subcellular distribution of active β-catenin, we performed confocal microscopy analyses. Both anti-miR-455 and anti-miR-519a significantly reduced active β-catenin signal intensity (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D), consistent with their ability to increase GSNOR protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Importantly, the reduction caused by anti-miRs on active β-catenin levels (both total and nuclear) was abolished upon GSNOR silencing with siRNA, confirming that the effects on β-catenin are GSNOR-dependent (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e \u003cp\u003eThese results suggest that GSNOR re-expression in BC cells negatively regulates β-catenin signaling. This effect is linked to GSK3β activity and further supports GSNOR role as a tumor suppressor in breast cancer.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eDespite decades of extensive research on NO signaling in cancer biology, a comprehensive understanding of the roles of this reactive and pleiotropic molecule in malignancy is still lacking. The complexity of NO biology in tumor development arises from several factors, including i) the dynamic regulation and subcellular localization of NO-producing enzymes, ii) the context-dependent nature of their activity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and iii) the contribution of immune infiltrates, which generate massive NO fluxes that profoundly influence tumor biology\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Together, these elements make it difficult to define clear cause-effect relationships between NO signaling and cancer progression.\u003c/p\u003e \u003cp\u003eWithin this complex regulatory landscape, several reports have shown that expression of the major denitrosylating enzyme, GSNOR, is reduced in multiple human cancers and inversely correlates with patient survival, with BC representing one of the most significant associations\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. This emerging body of evidence points to excessive protein S-nitrosylation, rather than NO levels \u003cem\u003eper se\u003c/em\u003e, as a potential mechanism contributing to the tumorigenic effects of NO. Although this concept is progressively gaining recognition in the field, the mechanisms regulating GSNOR expression in BC, as well as the specific tumor phenotypes resulting from its downregulation remain largely unclear.\u003c/p\u003e \u003cp\u003eHere, we addressed these issues. In particular, our experiments in human samples and BC cell lines provide evidence that reduced GSNOR expression is a common feature across all BC subtypes. We further demonstrate that specific oncogenic microRNAs, namely miR-455 and miR-519a, promote pro-tumorigenic processes via repression of GSNOR and, consequently, increase of protein S-nitrosylation.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study demonstrating that microRNAs can modulate global protein S-nitrosylation and thereby influence a broad set of proteins that are not their direct targets. We show that S-nitrosylation of oncogenes and tumor suppressors not only directly affects enzymatic activity and subcellular localization but also alters the susceptibility of these proteins to additional PTM, particularly phosphorylation. This multilevel regulation adds complexity to cancer signaling pathways influenced by NO via S-nitrosylation. From a mechanistic point of view, we show that GSNOR downregulation in BC cells promotes SNAI1 nuclear translocation and CDH1 repression through S-nitrosylation-mediated inhibition of GSK3β, a mechanism originally described in cardiomyocytes\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In parallel, inhibition of GSK3β resulting from GSNOR loss contributes to activation of the WNT/β-catenin signaling pathway. Of note, β-catenin itself is a direct target of S-nitrosylation\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, suggesting that further layer of regulation may amplify WNT/β-catenin signaling under conditions of impaired denitrosylation.\u003c/p\u003e \u003cp\u003eCrosstalk between different PTMs has been widely described in tumor biology. Well-known examples include the interplay between phosphorylation and ubiquitination in the regulation of protein stability\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, as observed for β-catenin, whose phosphorylation drives ubiquitin-dependent degradation\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, or p53, whose activity and turnover are controlled by coordinated phosphorylation, acetylation, and ubiquitination\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Similar interactions have also been reported between acetylation and phosphorylation\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, as well as between SUMOylation and ubiquitination\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, often influencing not only the modified protein but also interacting or functionally related components within the same signaling pathway.\u003c/p\u003e \u003cp\u003eA similar regulatory mechanism has also been described for microRNAs, which can indirectly modulate the extent of specific PTMs by targeting enzymes responsible for their removal. In this regard, microRNAs have been shown to downregulate phosphatases or deacetylases, leading to sustained phosphorylation or acetylation of multiple downstream substrates and persistent activation of oncogenic signaling\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Well-established examples include miR-34a\u0026ndash;mediated repression of the deacetylase SIRT1, resulting in increased p53 acetylation\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, as well as miR-155 and miR-222 that target, respectively, the phosphatase SHIP1 and the PP2A regulatory subunit PPP2R2A, leading to sustained AKT phosphorylation\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, our data suggest that S-nitrosylation is an oncogenically relevant PTM regulated by microRNAs through modulation of reversing enzymes, specifically the denitrosylase GSNOR. While a comparable regulatory mechanism has already been reported for other redox-based PTMs (e.g., cysteine oxidation, S-glutathionylation)\u0026mdash;where microRNAs have been shown to influence cellular redox homeostasis by repressing antioxidant enzymes and redox systems\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e\u0026mdash;to our knowledge, this is the first study describing such a mechanism for S-nitrosylation.\u003c/p\u003e \u003cp\u003eOverall, our findings indicate that microRNA-mediated regulation of protein S-nitrosylation, via the suppression of GSNOR expression, represents a mechanism through which they modulate global oncogenic signaling network, thereby sustaining the progression of BC and, potentially, of other tumors characterized by GSNOR downregulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to Laila Fisher for her secretarial assistance. This work was supported by: KBVU (R231-A13855, R352-A20537 to G.F.); the Novo Nordisk Foundation (NNF22OC0079352 to G.F.; NNF24OC0091871\u0026nbsp;to S.R.); the Italian Association for Cancer Research (IG2023-29221 to G.F.). L.I. was granted by the Horizon Europe MSCA Doctoral Network grant n. 101119873 (NO-CANCER-NET).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.F. supervised the project. G.F. and S.R. conceived and designed the study and wrote the manuscript. SR prepared all figures. S.R., V.F. and G.M. conducted cell line experiments. G.M., S.R., and L.I. performed Western blot analyses. S.R. and C.C. analyzed human BC specimens. I.G. performed immunohistochemistry on human BC samples. L.V. and S.R. carried out RT-qPCR analyses. S.R. conducted microscopy experiments, and G.M. performed biotin-switch assays. C.P. and M.B.M. performed and analyzed the RNAseq data. V.F., G.P., and M.H.C. conducted the \u003cem\u003ein-silico\u003c/em\u003e miRNA analysis. B.B. provided conceptual advice, critical and experimental support. All authors contributed to the description of the methods and results, edited, reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that all data supporting the findings of this study are available within the paper and its supplementary information files. All numeric data used to build histograms and graphs as well as the statistical test used for each experiment are reported in \u003cstrong\u003eTable S2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe RNAseq datasets generated during the current study are publicly available in GEO repository (GSE311614, GSE311283).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis study did not generate new unique reagents.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThomas DD, Wink DA. 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Pathway-level mutational signatures predict breast cancer outcomes and reveal therapeutic targets. \u003cem\u003eBr J Pharmacol\u003c/em\u003e 2025; 182: 5734\u0026ndash;5747.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"S-nitrosylation, microRNA, breast cancer, nitric oxide, EMT, nitric oxide","lastPublishedDoi":"10.21203/rs.3.rs-8532436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8532436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe denitrosylase S-nitrosoglutathione reductase (GSNOR) is a central regulator of nitric oxide (NO) signaling, by controlling protein S-nitrosylation. GSNOR is downregulated in several human cancers, with breast cancer representing one of the major hits. However, the mechanisms driving its suppression and its precise role in tumor progression remain elusive. Here, we identify microRNAs as key post-transcriptional regulators of GSNOR in breast cancer, specifically miR-455-3p and miR-519a-5p. Restoring GSNOR levels, either directly or by inhibiting these onco-miRs, reduced global protein S-nitrosylation, including that of glycogen synthase kinase 3β (GSK3β). This event was associated with decreased GSK3β inhibitory phosphorylation and consequent subsequent suppression of oncogenic signaling pathways. Functionally, GSNOR restoration inhibited epithelial-mesenchymal transition by increasing E-cadherin and reducing nuclear SNAI1 levels, attenuated β-catenin signaling, and impaired cell invasion, motility, and mammospheres viability. Altogether, our findings unveil a novel regulatory axis in which specific microRNAs control protein S-nitrosylation by targeting GSNOR, thereby driving breast cancer progression, and establish GSNOR as a crucial tumor suppressor.\u003c/p\u003e","manuscriptTitle":"Post-transcriptional repression of GSNOR by microRNAs regulates S-nitrosylation and fuels breast cancer progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 06:36:23","doi":"10.21203/rs.3.rs-8532436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-03-27T16:09:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-03-14T00:46:02+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-26T20:06:42+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-16T19:30:42+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-02T20:07:56+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-02-02T11:27:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T13:48:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oncogene","date":"2026-01-13T12:42:10+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-01-09T13:47:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T14:31:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f6fb61bf-c117-40e4-a2b5-2c106df691cf","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":61482583,"name":"Biological sciences/Cancer/Breast cancer"},{"id":61482584,"name":"Biological sciences/Cell biology/Post-translational modifications/Nitrosylation"}],"tags":[],"updatedAt":"2026-03-27T16:13:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 06:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8532436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8532436","identity":"rs-8532436","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}