BDE-47 Alters Surface Glycans and microRNA Cargo of Macrophage-Derived Vesicle Subpopulations Modulating EV-Mediated Senescence Signaling

BDE-47 Alters Surface Glycans and microRNA Cargo of Macrophage-Derived Vesicle Subpopulations Modulating EV-Mediated Senescence Signaling | 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 Research Article BDE-47 Alters Surface Glycans and microRNA Cargo of Macrophage-Derived Vesicle Subpopulations Modulating EV-Mediated Senescence Signaling Noemi Aloi, Alessia Maria Sampino, Alessia Li Vigni, Ilaria Cosentini, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7480117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Extracellular vesicles (EVs) are key mediators of intercellular communication, and their molecular cargo and surface properties can be profoundly influenced by external stimuli. In the context of inflammation, immune cells increase EV release to regulate immunity and metabolism. We previously demonstrated that the widespread environmental pollutant BDE-47 modulates macrophage innate immune responses through inflammasome inhibition and EVs biogenesis. In the present study, we investigated how BDE-47 exposure influences the heterogeneity of EVs released by THP-1 M(LPS) macrophages by characterizing their physical properties and surface markers. We also assessed the impact of BDE-47 on the microRNA cargo of different EV subtypes and investigated EVs functional role in modulating senescence-associated processes in heterologous LNCaP target cells Methods EV populations were isolated from BDE-47-treated THP-1 M(LPS) macrophages by differential centrifugation, allowing the separation of small (sEVs) and large EVs (lEVs). Their size, concentration, and surface characteristics were assessed through NTA, DLS, Western blotting, and lectin-binding assays, while EV-associated miRNAs were profiled by microarray. Pathway enrichment analysis was conducted to identify key biological pathways altered due to EVs treatment. The downstream effects of different subtypes of EVs (sEVs BDE−47 and lEVs BDE−47 ) were evaluated on LNCaP cells by BrdU incorporation and β-galactosidase senescence assays. In addition, transcriptional and Western Blot analyses were performed to investigate the expression of genes involved in cell cycle regulation. Results Our results show that BDE-47 does not alter EV size but profoundly reshapes their molecular identity. Specifically, we observed changes in glycan surface expression and a selective modulation of miRNA sorting in both sEVs and lEVs. Bioinformatic analysis revealed a distinct BDE-47–associated EV-miRNA signature linked to the regulation of cell cycle checkpoint pathways. Functional assays further demonstrated that sEVs BDE−47 and lEVs BDE−47 differentially influence proliferation, induction of senescence, and the expression of p16 and p21 genes in LNCaP cells. Conclusions Our findings highlight EVs as central targets and mediators of pollutant-induced cellular effects, unveiling a novel mechanism by which environmental contaminants interfere with EV-mediated communication and influence the behaviour and functions of recipient cells. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Background Extracellular vesicles (EVs) are small, membrane-bound particles secreted by cells that play critical roles in various biological functions, particularly in intercellular communication and in both pathological and physiological processes ( 1 ). All cells secrete extracellular vesicles transporting proteins, lipids, and nucleic acids between cells in various tissues and fluids ( 2 , 3 ). Based on their biogenesis, EVs can be classified in two major classes defined large extracellular vesicles (lEVs), often referred to as microvesicles (MVs), and small extracellular vesicles (sEVs), commonly known as exosomes. MVs generation is a complex cellular process involving the outward budding of the plasma membrane to produce extracellular vesicles typically ranging in size from 50 to 1000 nm. On the other hand, exosomes have an endosomal origin and range in size from 30 to 150 nm ( 4 ). Exosome are generated within endosomes as intraluminal vesicles and are released when these multivesicular endosomes (MVEs) fuse with the plasma membrane ( 5 ). Multiple pathways have been proposed for exosome biogenesis, both dependent and independent of the Endosomal Sorting Complex Required for Transport (ESCRT) machinery ( 6 ). Despite their different biogenesis pathways, the literature often refers to "small" and "large" EVs to describe the two fractions obtained through various methods, such as differential ultracentrifugation (dUC) ( 7 ). However, ordinary understanding of the heterogeneity and diverse functions of EV sub-populations is still a matter of discussion. With reference to inflammatory diseases, upon stimulation, immune cells release more EVs to regulate the body’s immunity and metabolism ( 8 ). As a key regulatory component of the immune response, macrophages secrete a large set of cytokines and a variety of signals, including EVs ( 9 ). The diversity of macrophage functions depends on their high degree of plasticity, which is a key characteristic of this cell type (REF?). In this view, it is challenging to understand the heterogeneity of EVs in response to different stimuli, since EVs are typically analyzed in bulk, capturing only the effects of the entire population. More recently, human EVs have also been investigated in exposure science and toxicology for their capability of influencing cell’s functions upon pollutant treatments ( 10 ). Our research group focused on the effect of an environmental pollutant, the BDE-47 (2,2',4,4'-Tetrabromodiphenyl ether), on human macrophage activity and the modulation of the expression of microRNA cargo in macrophage-derived EVs stimulated with LPS ( 11 , 12 ). BDE-47 is a flame retardant belonging to the PBDE (Polybrominated Diphenyl Ethers) family. It is one of the most common compounds used to enhance the fire resistance of materials such as plastics, electronics, furniture, and textiles ( 13 ). Despite PBDEs have been banned from the market, BDE-47 has been detected in the environment, wildlife, and human tissues, raising concerns about its potential health impacts as endocrine disrupting substances ( 14 ). Regarding their effects on the macrophage immune response, we demonstrated that BDE-47 exerts diverse mechanisms of action, including direct immunotoxic effects on macrophages. Specifically, it impairs the secretion of proinflammatory cytokines in M(LPS) macrophages ( 11 ) while also influencing sEVs biogenesis ( 12 ) and their miRNA cargo ( 10 ). Furthermore, we found that sEVs derived from BDE-47-exposed macrophages can modulate intracellular miRNAs levels and alter the expression of surface markers in naïve resting THP-1 M(0) macrophages, highlighting the immunomodulatory potential of this compound ( 10 ). In this study, we further studied the heterogeneity of EV populations by using differential centrifugation to isolate and enrich small and large EVs derived from BDE-47-treated THP-1 M(LPS) macrophages. Specifically, we fractionated the EVs into two distinct subpopulations: 10K EVs (or large EVs, lEVs) and 100K EVs (or small EVs, sEVs), based on their sedimentation properties. To further characterize these EV subpopulations, we performed a comprehensive analysis focusing on five key aspects: (i) their physical properties, including size distribution and morphology; (ii) their surface marker expression to distinguish vesicle subtypes and determine their cellular origin; (iii) their binding to different human carbohydrates-binding proteins; (iv) their miRNA cargo, assessing potential differences in molecular content and functional implications; and (v) their functional role in modulating the proliferative capacity of prostate cancer LNCaP cells. By elucidating these characteristics, our study provides new insights into how an environmental pollutant such as BDE-47 can alter extracellular vesicle composition, highlighting its potential role in the modulation of EV-mediated intercellular communication within the framework of environmental toxicology. 2. Methods 2.1 Reagents The 2,4,2’,4’-tetrabromodiphenyl ether (BDE-47) was purchased from Toronto Research Chemicals (Ontario, Canada) and dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich, Milan, Italy, cat. n. D2650) at the concentration of 25 mM. E. coli Lipopolysaccharides (LPS, serotype O26:B6) were purchased from Sigma-Aldrich. 2.2 Cell line cultures The human monocytic leukemia THP-1 cell line (ECACC 88081201) was maintained in culture with RPMI 1640 medium (Gibco Life Technologies, Monza, Italy), supplemented with heat inactivated 10% Fetal Bovine Serum (FBS, Gibco, Life Technologies, Monza, Italy) and 1% antibiotic (5.000 U/mL penicillin, 5.000 µg/mL streptomycin sulphate, both from Sigma-Aldrich, Gibco, Life Technologies). The monocytes were treated for 72 hours at 37°C and 5% CO 2 with 200 nM phorbol 12-myristate-13-acetate (PMA, Sigma-Aldrich) to obtain the naïve THP-1 M0 macrophage. The THP-1 M(LPS) phenotype was induced by stimulation with 10 ng/mL of LPS for 24 hours at 37°C and 5% CO 2 . LNCaP cell line (ATCC CRL-1740) was cultured in RPMI 1640 medium supplemented with 10% FBS and 1% antibiotics at 37°C and 5% CO 2 . 2.3 Purification of large and small extracellular vesicles subpopulations from THP-1 M(LPS) treated with BDE-47 Large and small extracellular vesicles (lEVs and sEVs, respectively) were prepared after incubation of the THP-1 M0 naïve macrophages with 3 µM BDE-47 (lEVs BDE−47 and sEVs BDE−47 ) or 0.0125% DMSO (control vehicle, lEVs DMSO and sEVs DMSO ) in RPMI 1640 medium (Gibco Life Technologies, Monza, Italy), supplemented with heat inactivated 10% EV-depleted FBS and 1% antibiotic for 24 hours at 37°C and 5% CO 2 . Thereafter, cells were differentiated in THP-1 M(LPS) phenotype with 10 ng/mL of LPS and incubated for a further 24 hours at 37°C and 5% CO 2 . The supernatants were collected, and EV subpopulations were separated according to differential ultracentrifugation (dUC) method ( 15 , 16 ). Specifically, three independent preparations were used for the subsequent assays. The supernatants were first centrifuged at low speed to remove cells and debris and subsequently centrifuged at 10.000 xg for 30 minutes at 4°C to isolate lEVs. The lEVs were resuspended in a proper volume of microfiltered 1X phosphate buffered saline (PBS) w/o Ca 2+ and Mg 2+ and stored at -80°C until use. Afterwards, sEVs were collected from the supernatants into Beckman Coulter polypropylene open top tubes via centrifugation at 118.000 xg for 70 minutes at 4°C using a Beckman SW28 rotor. The sEVs pellets were washed in 1X PBS w/o Ca 2+ and Mg 2+ , centrifuged and again at 118.000 xg for 70 minutes at 4°C and finally, resuspended in 50 µl of microfiltered 1X PBS w/o Ca 2+ and Mg 2+ . 2.4 Nanoparticle Tracking Analysis (NTA) of THP-1 derived large EVs and small EVs Large and small EVs size distribution and concentration were measured using the NanoSight NS300 (Malvern Panalytical, UK), equipped with a 488 nm laser, a high sensitivity sCMOS camera, a syringe pump, and a second 405 nm laser and a CMOS camera. LEVs and sEVs samples were diluted in particle-free water (Water, HPLC grade, Sigma-Aldrich, filtered by 20 nm using Whatman Anotop filters) to generate a dilution in which 20–120 particles per frame were tracked and to also obtain a concentration within the recommended measurement range (1–10x10 8 particles/mL). Five experiment videos of 60 second duration were analyzed using NTA 3.4 Build 3.4.003 (camera level 15–16). A total of 1500 frames/sample were examined, captured, and analyzed by applying instrument-optimized settings with a suitable detection threshold so that the observed particles were marked with red crosses and no more than 5 blue crosses were visible. Further settings, such as blur size and Max Jump Distance, were set to “automatic” and viscosity was set to that of water (0.841–0.844 cP). 2.5 Dynamic light scattering (DLS) The vesicle solutions were diluted in 1X PBS and centrifuged at 1000 xg for 10 minutes at 4°C to remove any dust particles or aggregates. The supernatant, collected using MilliQ-rinsed pipette tips, was transferred into a quartz cuvette and maintained at 20°C within a temperature-controlled compartment of a BI200-SM goniometer (Brookhaven Instruments, Nashua, NH, USA). Measurements were conducted using a He-Ne laser source tuned at 633 nm (JDS Uniphase 1136 P) and a single-pixel photon counting detector (Hamamatsu C11202-050, Hamamatsu Photonics Deutschland GmbH, Germany). The intensity autocorrelation function, g 2 (t), was acquired at a scattering angle of 90° using a BI-9000 correlator (Brookhaven Instruments, Nashua, NH, USA) and size distribution P q (σ) was calculated by assuming that the diffusion coefficient distribution is shaped as a Schultz distribution, as described in Paterna and coworkers ( 17 ). Two robust parameters were obtained from this analysis: D z (the z-averaged hydrodynamic diameter) and PDI (the polydispersity index, which is an estimate of the distribution width). 2.6 Western blot Cell lysate, lEVs and sEV samples, from control and treated THP-1 M(LPS) macrophages, were mixed with proper volumes of 5X loading buffer [0.25 M Tris-Cl pH 6.8, 10% sodium dodecyl sulphate (SDS), 50% glycerol, 0.25M dithiothreitol (DTT), 0.25% bromophenol blue], heated at 100°C for 5 min and loaded into 10% SDS-PAGE for electrophoretic separation. Then, proteins were blotted onto polyvinylidene fluoride (PVDF) membranes which were blocked with BSA-TBS-T solution [3% powdered with bovine serum albumin in TBST (50 mM Tris HCl pH 8.0, 150 mM NaCl with 0.05% Tween 20)] for 1 hour at room temperature, followed by primary antibody incubation overnight at 4°C. The antibodies used were: anti-Alix (clone 3A9, dil. 1:150 in 3% BSA/1X TBS-T), anti-HSP70 (clone W27 dil. 1:500 in 5% Milk/1X TBS-T), anti-Enolase, anti-βActin (clone A5 and clone AC15, respectively, dil. 1:400 in 3% BSA/1X TBS-T, Santa Cruz Biotechnology, Dallas, Texas, USA), anti-CD63 (rabbit polyclonal, dil.1:500, from Invitrogen), anti-Calnexin (rabbit polyclonal, dil.1:1000, from Novus Biologicals), anti CD81 (clone B11, dil.1:200, from Santa Cruz Biotechnology, USA). The membranes were incubated for 1 hour with the horseradish peroxidase-conjugated secondary anti-mouse or anti-rabbit antibodies according to the manufacturer’s instructions (Cell Signaling Technologies Inc., Beverly, MA, USA) and the signals were revealed using Super Signal™ Pierce™ ECL (Thermo Fisher Scientific, Monza, Italy). LNCaP cell lysates were prepared using RIPA buffer (Cell Signaling Technologies). Then, proteins were blotted onto nitrocellulose membranes. After transfer blocking was performed using Odyssey® Blocking Buffer (OBB, LI-COR) diluted in TBS, followed by incubation with primary antibodies anti-β-actin (Sigma-Aldrich), anti-p21 (Cell Signaling Technologies), anti-p53 and anti-p16 (Santa Cruz Biotechnology) diluted in OBB. Detection was carried out using IRDye® 800CW or Alexa Fluor 680-conjugated secondary antibodies (LI-COR; Invitrogen), and membranes were scanned with the Odyssey IR scanner (LI-COR Biosciences, Lincoln, NE, USA). Band intensities were quantified using Odyssey 3.0 software. 2.7 ELISA-based solid-phase assay A volume of 50 µL from a suspension of vesicles at 2 x 10 7 particles/mL in PBS (10 mM, pH 7.4), was used to coat NUNC MaxiSorp wells (overnight, 4°C). After discarding and washing (2 x 150 µL) with calcium and magnesium-containing buffer TSM (20 mM tris(hydroxymethyl)aminomethane (Tris)-HCl, pH 8.0; 150 mM NaCl; 1 mM CaCl 2 ; 2 mM MgCl 2 ), wells were blocked with 80 µL 1% BSA solution (Sigma-Aldrich, lyophilized powder, ≥ 96%, agarose gel electrophoresis) in TSM at room temperature for 30 min. The blocking solution was discarded and 50 µL of different C-type-lectins including human-Fc Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin (DC-SIGN), Macrophage Galactose-type Lectin (MGL) and mannose receptor (MR) at 1 µg/mL were added. After 1 h at room temperature, wells were washed with TSM (2 x 150 µL) and 100 µL of anti-human horseradish peroxidase (0.3 µg/mL, Goat anti-human IgG-HRP from JacksonImmuno) were added. After 30 min, wells were washed with TSM (2 x 150 µL). Finally, 100 µL of a substrate solution (3,3′,5,5′- tetramethylbenzidine, TMB, in citric/acetate buffer, pH 4, and H 2 O 2 ) were added. After 10 min at room temperature the reaction was stopped with 50 µL of H 2 SO 4 (0.8 M) and the optical density (OD) was measured at 450 nm in an ELISA reader. The experiment was performed in duplicate and data were normalized over the signal at 450 nm from the positive controls used for each C-type lectin. Polyacrylamide polymers, functionalized with different glycans were purchased from Lectinity, MW approx. 20 kDa, carbohydrate content around 20% mol.: GalNAcα-OCH 2 CH 2 CH 2 NH 2 0030-PA (PAA-Tn, positive control for MGL, 20µg/mL). Mannans from Saccharomyces cerevisiae (positive control for DC-SIGN and MR) was purchased from Sigma-Aldrich and used at 10µg/mL to coat the ELISA wells. Statistical analysis was performed by means of two way ANOVA multiple comparison with the Tukey's multiple comparisons test (alpha 0.05), using GraphPad Prism 10. 2.8 MiRNA purification from large EVs and small EVs Purified lEVs and sEVs from control (lEVs DMSO and sEVs DMSO ) and treated (lEVs BDE− 47 and sEVs BDE− 47 ) THP-1 M(LPS) cells were used for miRNA purification. The same number of lEVs and sEVs (3x10 9 particles) was used to extract miRNAs. The samples were diluted up to 200 µL with 1X PBS w/o Ca 2+ and Mg 2+ and then lysed using 1 mL of QIAzol Lysis Reagent (Qiagen, Milan, Italy). The purification of total RNA enriched in miRNAs was performed according to the miRNeasy Serum/Plasma Kit manufacturer's protocol (Qiagen). To control the yield, purity, and integrity of samples, 1µl of a Spike-in mix containing UniSp2 (5’GUACUCGGCUUACGAUCGUAA), UniSp4 (5’GAUGGCAUUCGAUCAGUUCUA) and UniSp5 (5'GAUGCUACGGUCAAUGUCUAAG) miRNAs (Qiagen) was added to the samples before the extraction phases. The miRNA samples were eluted in 15µl of H 2 O DNAse/RNAse free, and concentrations were evaluated by Nanodrop analysis (NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer, Thermo Fisher Scientific, Monza, Italy). 2.8.1 Large EVs and small EVs MiRNA cargo profiling The cDNA synthesis from lEVs and sEVs miRNAs was performed using the miRCURY™ LNA RT kit (Qiagen). A mix containing UniSp6 (5’CUAGUCCGAUCUAAGUCUUCGA) and Caenorhabditis elegans miR-39-3p (Cel miR39-3p, 5’UCACCGGGUGUAAAUCAGCUUG) exogenous controls was added to reactions according to the manufacturer's protocol in a final volume of 20 µl. The retro-transcriptions were performed for 60 minutes at 42°C. Then, the reverse transcriptase enzyme was inactivated for 5 minutes at 95°C. Subsequently, the expression profile of 179 miRNA was evaluated using the miRCURY LNA miRNA Focus PCR Panel (panel code: YAHS-106Y, Qiagen). Specifically, the cDNA template was amplified by Real Time analysis (StepOnePlus™ Real Time PCR System, Applied Biosystems, Milan, Italy) and the miRCURY LNA™ SYBR GREEN PCR kit. The Real Time PCR conditions were an initial heat activation step at 95°C for 2 minutes and 40 cycles of two-step PCR, denaturation at 95°C for 10 seconds, annealing/extension at 56°C for 1 minute. The CT data obtained from control (lEVs DMSO and sEVs DMSO ) and samples (lEVs BDE−47 and sEVs BDE−47 ) extracellular vesicles were analyzed using the Qiagen GeneGlobe miRCURY LNA miRNA PCR Data Analysis software ( https://dataanalysis2.qiagen.com/miRCury ); the data were normalized using the geNorm “Predefined reference miRNA only” function as references. The miRNAs were considered changed between the two groups if the fold change was 2 (up-regulated miRNA). The miRNAs with a quantification cycle (CT) > 35 were considered undetected. The complete list of 179 analyzed miRNAs is reported in Additional Fig. 1 (A.1). 2.8.2 Computational analysis Pathway enrichment analysis was conducted to identify key biological pathways altered due to the deregulation of miRNAs in lEVs BDE−47 . First, the validated gene targets of upregulated and downregulated miRNAs in lEVs BDE−47 compared to sEVs BDE−47 were identified using the multiMiR R package ( 18 ). Specifically, we retrieved only experimentally validated miRNA-target interaction from three publicly available databases: miRecords, miRTarBase and TarBase. To focus on biologically relevant target genes, we applied a graph-based selection approach. A directed graph was constructed, where miRNAs and their target genes were represented as nodes, and interactions between them as edges. To reduce noise and prioritize the most functionally significant targets, we filtered out genes with a low degree of connectivity, retaining only those interacting with a substantial number of miRNAs (n = 4). This step helped refine the analysis by emphasizing genes more likely to play a crucial role in the observed regulatory network. The selected target genes were then subjected to Reactome enrichment analysis using the ReactomePA R package ( 19 ). To streamline the interpretation of enriched pathways, we performed a pairwise similarity analysis based on Jaccard’s similarity index, clustering related pathways and reducing redundancy in the enrichment results. 2.9 LNCaP cell proliferation assay Cell proliferation was assessed by measuring bromodeoxyuridine (BrdU) incorporation into DNA using a colorimetric immunoassay (Roche Diagnostics GmbH, Mannheim, Germany), following the manufacturer's instructions. Briefly, LNCaP were seeded at the concentration of 5.000 cells/well in 96-well plate to test the effect of EVs on cellular proliferation. Cells were left to adhere for 24h. Subsequently, they were incubated with lEVs/sEVs DMSO/BDE−47 respecting the cell-EVs ratio of 1:20 for 72h. BrdU was added 16 h before the end of treatments. Values were expressed as means of OD ± SD of four separate experiments, each performed in triplicate. A statistical analysis was performed using a one-way ANOVA and Bonferroni’s post-hoc analysis test. 2.10 Senescence-associated β-galactosidase assays LNCaP cells (20x10 3 ) were grown on an 8-well chamber slide. At time 0, the medium was replaced with a fresh complete medium with lEVs/sEVs DMSO/BDE−47 for 6 days. After treatments, the cells were fixed and stained for β-galactosidase activity, using a Senescence Cell Staining kit, following the manufacturer’s instructions (Sigma‐Aldrich). The percentage of senescence‐associate β‐gal positive cells was determined by counting the number of blue cells within a sample, using Olympus CKX53 (Olympus Corporation, Tokyo, Japan) microscope with an X20 lens. Ten random fields were photographed for each condition, and the percentage of SA‐β‐gal‐positive cells was calculated. 2.11 RNA isolation, retrotranscription and Digital PCR analysis Total RNA from LNCaP treated with lEVs/sEVsDMSO/BDE-47 was isolated according to RNeasy mini kit protocol (Qiagen). 1 µg of each RNA template was retro-transcribed using the QuantiTect Reverse Transcription Kit (Qiagen). The cDNA was diluted 1:500 and the expression of p16 (or CDKN2A, NM_000077) and p21 (or CDKN1A, NM_000389) genes was evaluated by Digital PCR technology using the QIAcuity One System, the EVA Green detection method and the Quantitect primer assays (Qiagen). Specifically, 40 µl of reaction mixtures were prepared in 96 well plates according to the QIAcuity EG PCR Kit manufacturer’s protocol (Qiagen) and subsequently dispensed into the Qiacuity™ Nanoplate 26k 24-well (Qiagen). Positive (the human β-actin gene, NM_001101) and negative (no template reactions) controls were included in each experiment (n = 4 independent replication). The cycling profile consisted of a denaturation step at 95°C for 2 minutes, 40 cycles at 95°C for 15 seconds, 56°C for 15 seconds and 72°C for 15 seconds, followed by 40°C for 5 minutes. The imaging step was performed selecting the green channel and setting the exposure duration at 300 ms and Gain = 3. Data were analyzed by means of QIAcuity Suite Software version 2.5.0.1 (Qiagen) and the absolute quantities were reported as copy number/µl. Any values above 0 copies/µl were considered as positive. The statistical analysis was performed using one-way ANOVA. 3. Results 3.1. Biophysical characterization of EVs subpopulations derived from BDE-47 treated THP-1 M (LPS) macrophages The conditioned media from three independent preparations of THP-1 M(LPS) macrophages treated with BDE-47 or DMSO (control) were collected. Small and large EVs from each preparation (sEVs BDE−47 /lEVs BDE−47 and sEVs DMSO /lEVs DMSO respectively) were isolated according to the dUC method. To assess whether exposure to the environmental pollutant BDE-47 influences the size distribution and particle concentration of EVs, we performed Nanoparticle Tracking Analysis on both sEVs and lEVs isolated from treated and untreated cells. The size distribution profiles of sEVs and lEVs revealed no significant differences between control (DMSO) and BDE-47-treated samples with overlapping curves indicating comparable particle size distribution profile (Fig. 1 A and B). However, the observed quantitative difference between sEVs BDE−47 /sEVs DMSO particles highlights intrinsic differences in their secretion which may reflect distinct biogenesis pathways or cellular handling mechanisms as previously shown by our group ( 12 ). These findings were also supported by Dynamic Light Scattering analyses (Fig. 1 C and D). In Table 1 and Table 2 we have summarized concentration values and the size distributions of EVs subtypes obtained by NTA and DLS assays. Table 1 EVs subtypes concentrations and size distributions EV subtype EV subtypes Concentration (particles/mL) Size lEVs DMSO 1.6x10 11 ± 1.4x10 10 113,8 ± 12 nm lEVs BDE−47 1.25x10 11 ± 4x10 10 113,6 ± 22 nm sEVs DMSO 3,0x10 11 ± 1.7x10 11 139,7 ± 15 nm sEVs BDE−47 5.9x10 11 ± 1.6x10 11 125,3 ± 22 nm Table 2 EVs parameters derived by Dynamic Light Scattering analyses EVs subtypes D z (nm) PDI lEVs DMSO 265 ± 5 0,22 lEVs BDE−47 265 ± 5 0,25 sEVs DMSO 292 ± 5 0,37 sEVs BDE−47 294 ± 5 0,38 3.2. Large and small EVs specific markers evaluation To support the size distribution data obtained from NTA and DLS analyses, and to exclude potential cross-contamination between large and small EV subpopulations, we performed Western blot assays to assess the expression of specific markers associated with each EV type. This approach allowed us to confirm the identity and purity of the isolated EV fractions based on established molecular signatures. Specifically, we assessed the expression of Enolase-1 (Eno-1), Hsp70, Alix, CD63, CD81 and Calnexin. Our findings are consistent with the MISEV 2018 and MISEV 2024 Guidelines. Indeed, we found a higher expression of CD63 and CD81 in sEVs subtypes (sEVs DMSO /sEVs BDE−47 ) compared to lEVs, while the Calnexin showed greater expression in the large EV subpopulations (lEVs DMSO /lEVs BDE−47 ). No significant differences were found when comparing BDE-47-derived EV subpopulations (lEVs DMSO versus lEVs BDE−47 and sEVs DMSO versus sEVs BDE−47 , respectively). These results suggest that BDE-47 does not interfere with the expression of specific EVs markers. The immunoblot data are shown in Fig. 2 panels A and B. 3.3 Evaluation of BDE-47's effects on large and small EVs binding to human lectins To study lEVs DMSO/BDE−47 and/or sEVs DMSO/BDE−47 binding to human lectins, we employed an ELISA-based solid-phase assay to evaluate EV interactions with the C-type lectins DC-SIGN, Langerin, MR, and MGL. As shown in Fig. 3 , EVs exhibited significant binding to DC-SIGN and MGL, consistent with the presence of high-mannose, fucosylated, or α-GalNAc residues on the N or O-glycan structures on their surface. Interestingly, the MR, which preferentially binds terminal mannoses, shows non-detectable interactions in the tested experimental conditions. These data suggested that in lEVs BDE−47 there is a higher presence of C-type lectins-specific glycan structures compared to both lEVs DMSO and the sEVs DMSO /sEVs BDE−47 groups. Furthermore, we observed a higher interaction between sEVs BDE−47 and MGL than sEVs DMSO . These findings suggest that EVs may utilize C-type lectin recognition to mediate immune modulation, and that BDE-47 may influence the expression of lectin-specific glycan structures on the surfaces of EV subpopulations. 3.4. BDE-47 Modulates miRNA Sorting into Macrophage-Derived sEVs and lEVs and Identifies Associated Pathways In a previous study, we demonstrated that the flame retardant BDE-47 can modulate the expression of miRNAs cargo in macrophage derived sEVs ( 10 ). Here, we have expanded our analysis to focus on BDE-47 ability to rewire miRNA sorting between lEVs and sEVs subpopulations, as well as its impact on the molecular signals channelled by these vesicles to target cells. In these perspectives, we purified lEVs and sEVs subpopulation from conditioned culture media collected from control (lEVs DMSO and sEVs DMSO ) or BDE-47 (lEVs BDE−47 and sEVs BDE−47 ) treated THP-1 M(LPS) according to the dUC gold-standard method. MiRNAs were isolated from lEVs DMSO/BDE−47 and sEVs DMSO/BDE−47 subpopulations and the level of expression of 179 miRNAs (reported in Additional Fig. 1 ) assessed through microarray analyses. Comparative analyses of differentially expressed miRNAs (lEVs DMSO versus sEVs DMSO and lEVs BDE−47 versus sEVs BDE−47 ), were performed and the results summarized in the volcano plots shown in Figs. 4 A and B. Data highlight that lEVs DMSO and sEVs DMSO differ in their miRNA cargo (Fig. 4 A) with 17 significantly modulated miRNAs listed in Table 3 . Conversely, the analyses of EVs subpopulations derived from BDE-47-conditioned media revealed the modulation of 22 miRNA (Fig. 4 B) reported in Table 4 . The miRNAs’ fold changes and p-values are included in both tables. Additionally, the relationships between the two clusters of subpopulations lEVs DMSO /sEVs DMSO and lEVs BDE−47 /sEVs BDE−47 were evaluated and visually represented using a Venn diagram as shown in Fig. 4 C (orange and red circles). It is interesting to note that the two clusters of EVs do not share any common modulated miRNA in the selected set of molecules (indicated by the blue area). To identify the putative processes and pathways targeted by BDE-47-derived EVs subtypes, an in silico analysis was performed. Indeed, using the list of 22 deregulated miRNAs in lEVs BDE−47 versus sEVs BDE−47 , we explored the biological pathways potentially influenced by co-expressed miRNAs, leveraging information on their experimentally validated gene targets. For this purpose, we identified gene target sets associated with the 22 miRNAs querying three publicly available databases: miRecords, miRTarBase and TarBase. These databases provide experimentally validated miRNA-target interactions, with miRecords integrating data from multiple sources, miRTarBase focusing on interactions supported by techniques such as luciferase assays and qPCR, and TarBase offering one of the most comprehensive collections of validated targets with functional annotations. In our analysis we selected target genes that were included in all three databases to ensure high-confidence miRNA-target interactions. To identify key biological pathways associated with selected target genes, an enrichment analysis was performed, and the results were visualized using network and hierarchical clustering approaches. Table 3 Differential expressed miRNAs between lEVs DMSO and sEVs DMSO N. miRNA ID Fold Regulation p-Value 1 hsa-miR-451a 28.75 0.016076 2 hsa-miR-200a-3p -4.97 0.046252 3 hsa-miR-485-3p -7.73 0.017682 4 hsa-miR-154-5p -7.73 0.017682 5 hsa-miR-382-5p -2.64 0.030419 6 hsa-miR-532-5p -2.01 0.002514 7 hsa-miR-497-5p -5.22 0.002520 8 hsa-miR-136-5p -5.87 0.007087 9 hsa-miR-208a-3p -7.73 0.017682 10 hsa-miR-629-5p -3.50 0.034899 11 hsa-miR-22-5p -3.41 0.011020 12 hsa-miR-144-5p -3.87 0.023655 13 hsa-miR-92a-3p -2.15 0.007378 14 hsa-miR-483-5p -7.73 0.017682 15 hsa-miR-151a-3p -14.07 0.005117 16 hsa-miR-136-3p -7.73 0.017682 17 hsa-miR-543 -6.43 0.011120 Table 4 Differential expressed miRNAs between lEVs BDE−47 and sEVs BDE−47 N. miRNA ID Fold Regulation p-Value 1 hsa-miR-486-5p 3.62 0.009088 2 hsa-miR-125b-5p 2.68 0.026690 3 hsa-miR-150-5p 13.35 0.011622 4 hsa-miR-221-3p -5.56 0.005887 5 hsa-let-7f-5p -3.71 0.037120 6 hsa-miR-27b-3p -4.78 0.017988 7 hsa-miR-106a-5p -3.00 0.005136 8 hsa-let-7a-5p -4.47 0.028764 9 hsa-let-7b-3p -2.72 0.041837 10 hsa-let-7b-5p -5.91 0.034871 11 hsa-miR-30e-5p -3.42 0.046563 12 hsa-miR-148b-3p -5.34 0.041511 13 hsa-miR-20a-5p -4.20 0.007227 14 hsa-miR-17-5p -3.75 0.003366 15 hsa-miR-101-3p -3.25 0.000666 16 hsa-miR-23a-3p -2.49 0.032371 17 hsa-miR-223-3p -2.24 0.036191 18 hsa-let-7d-3p -2.44 0.004068 19 hsa-miR-192-5p -3.99 0.001101 20 hsa-miR-18a-5p -2.71 0.016326 21 hsa-miR-140-5p -2.92 0.017724 22 hsa-miR-584-5p -4.61 0.023959 The network diagram in Fig. 5 illustrates the connections between enriched pathways, where nodes represent biological Reactome pathways and edges indicate shared genes between them. The size of each node corresponds to the number of genes involved, while the color scale represents the adjusted p-value (p.adjust), with darker colors indicating higher statistical significance. The pathways are organized into functional groups, with key clusters including senescence-associated processes, cell cycle regulation, and signaling cascades such as NOTCH, AKT, and ALK. Figure 6 presents a hierarchical clustering dendrogram of the same enriched pathways, grouping functionally related pathways based on gene overlap. The clustering reveals distinct modules corresponding to major biological themes, such as senescence regulation, G1/S cell cycle transition, AKT activation, and interleukins signaling. The color-coded background highlights key pathway categories, further emphasizing the functional relationships among the enriched pathways. Overall, the enrichment analysis indicates a strong association of the dataset with cellular senescence, transcriptional regulation, and oncogenic signaling (Fig. 6 ). Furthermore, it has been demonstrated that hsa-miR-106a-5p, hsa-miR-20a-5p, and hsa-miR-17-5p play key roles in regulating the proinflammatory response and are involved in the development of various cancers, including thyroid, prostate, and colorectal cancers, as well as glioma and melanoma ( 20 – 22 ). 3.5. Effects of lEVs BDE−47/DMSO and sEVs BDE−47/DMSO on LNCap cells: analyses of cell proliferation Based on insights from our bioinformatic analysis, a BrdU incorporation assay was conducted to evaluate the potential functional role of EVs in modulating the proliferative capacity of heterologous cell lines, specifically LNCaP cells. To this end, human prostate cancer cell line LNCaP were treated for 72 hours with either lEVs or sEVs derived from BDE-47- or DMSO-treated THP-1 M(LPS) cells. Figure 7 shows that both sEVs BDE−47 and sEVs DMSO significantly reduce LNCaP cell proliferation compared to their respective lEV counterparts. Notably, sEVs BDE−47 exert a more pronounced antiproliferative effect than sEVs DMSO , suggesting that BDE-47 exposure may enhance the functional capacity of sEVs to suppress cell proliferation. 3.6. Effects of lEVs BDE−47/DMSO and sEVs BDE−47/DMSO on LNCap cells: analyses of senescence-markers To further explore the underlying mechanisms contributing to this antiproliferative effect and based on the results of our bioinformatic analysis, we next investigated whether these EV subpopulations might be involved in the induction of cellular senescence. LNCaP cells were exposed to lEVs BDE−47/DMSO or sEVs BDE−47/DMSO for six days, followed by evaluation using the senescence-associated β-galactosidase assay. Microscopic analysis and quantification of β-galactosidase-positive cells revealed a marked reduction in cell number following treatment with sEVs confirming our proliferation assay. Additionally, the remaining cells exhibited a predominant blue coloration, indicative of senescence-associated β-galactosidase activity (Fig. 8 , panel A). Statistical analysis is presented in Fig. 8 , panel B. During cellular senescence, the expression of cyclin-dependent kinase inhibitors p16 and p21 is typically upregulated. To investigate this point, we assessed the expression levels of p16 and p21, encoded by the CDKN2A and CDKN1A genes, respectively. Digital PCR analysis revealed a similar increase in p16 mRNA copy number/µL in LNCaP cells treated with either lEVs BDE−47 or sEVs BDE−47 , compared to their respective controls. In contrast, p21 expression was significantly higher in cells treated with sEVs BDE−47 than in those treated with lEVs BDE−47 or sEVs DMSO , indicating a more robust activation of this senescence marker by sEVs following BDE-47 exposure. Finally, we assessed the expression levels of p16, p21 and p53 proteins in LNCaP cells treated with different EV subpopulations. As shown by the Western blot analysis in Fig. 9 C, treatment with sEVs BDE−47 led to an upregulation of both p16 and p21 compared to the other conditions. Furthermore, the upregulation of p53, along with p21, indicates the involvement of the p53/p21 axis in the induction of cellular senescence ( 23 ). This increased expression is consistent with the induction of a senescent phenotype and supports the role of sEVs BDE−47 in promoting cell cycle arrest. 4. Conclusions Several studies have investigated the association between circulating exosome levels and environmental pollutant exposure, suggesting that fluctuations in exosome abundance may be relevant to serve as indicators of toxicant exposure ( 24 ). However, the analysis of this relationship is complex and influenced by multiple variables, including the type of pollutant, duration and intensity of exposure, and the specific tissues or organs affected ( 25 – 28 ). Our study presents an in vitro comprehensive biophysical, molecular and functional characterization of extracellular vesicle subpopulations, focusing on the characterization of large and small EVs secreted by THP-1 M(LPS) macrophages following exposure to the flame retardant BDE-47. Indeed, functional assays of extracellular vesicles are commonly conducted on total EV preparations, without considering the intrinsic heterogeneity of distinct EV subpopulations. Our findings highlight the ability of environmental toxicants to reshape the molecular landscape of EVs and, in this way, modulate their downstream biological effects. In our experimental set-up, differential ultracentrifugation enabled the efficient isolation of distinct EV subpopulations and, NTA and DLS confirmed consistent size distributions and particle concentrations between control and BDE-47-treated samples as previously shown ( 10 ). While sEVs and lEVs showed similar size ranges, this overlap suggests that physical dimensions alone are insufficient to definitively distinguish these populations. Nonetheless, their distinct sedimentation behaviours under differential ultracentrifugation revealed functional and compositional differences, reinforcing the relevance of separation methods in EV characterization ( 7 ). This statement is supported by immunoblot validation of EV subtype-specific markers, where classical sEVs markers (CD63, CD81) and lEVs-associated markers (e.g., Calnexin) showed expected patterns in both control and BDE-47-treated samples. These findings comply with MISEV 2018 ( 16 ) and MISEV 2024 guidelines ( 15 ) and confirm the absence of significant contamination across EV fractions. However, while BDE-47 exposure did not alter EV markers of expression, our glycoprofiling data revealed notable changes in the EV surface glycan landscape upon stimulation. Surface glycans play a crucial role in mediating intercellular communication via extracellular vesicles, influencing their recognition, targeting, and uptake by recipient cells ( 29 , 30 ), especially in the tumor microenvironment. Carbohydrate-mediated interactions play a crucial role in modulating and controlling cell-cell behavior, particularly in host-pathogen interactions and the tumor microenvironment ( 31 ). Lectins (carbohydrate-binding proteins) often serve as the receptors that regulate carbohydrate-mediated signaling, influencing the behavior of antigen-presenting cells and T-cells. Using a panel of human C-type lectins, we observed that both lEVs and sEVs bind DC-SIGN and MGL, confirming the presence of N or O glycans on the EV’s surface. Interestingly, BDE-47 induced differences in C-type lectins binding on sEVs. These changes suggest that BDE-47 is able to modify the glycosylation patterns of EVs in a subtype-specific manner, potentially influencing their immunomodulatory roles through altered interactions with glycan-binding receptors on recipient cells ( 32 ). At the molecular level, BDE-47 also reprogrammed the miRNA cargo of both sEVs and lEVs. Our miRNA profiling revealed distinct signatures in each EV subtype, with no overlap between the miRNAs differentially expressed in BDE-47-derived lEVs versus sEVs, indicating a marked rewiring of miRNA sorting under toxicant exposure. Our bioinformatic miRNA analyses significantly advanced our understanding of BDE-47 impact on EV cargo showing that the pollutant treatment substantially altered miRNA cargo, evidenced by the differential expression of 22 miRNAs unique to the EV subpopulations derived from treated cells. Remarkably, pathway enrichment analyses identified robust associations between these miRNAs and critical cellular processes such as senescence, cell cycle regulation, and oncogenic signaling pathways (NOTCH, AKT, ALK). To assess the functional relevance of these molecular findings, we performed a proliferation assay on LNCaP prostate cancer cells treated with the different EV subpopulations. Interestingly, both sEVs BDE−47 and sEVs DMSO significantly inhibited LNCaP proliferation more than their lEV counterparts as shown by means of BrdU labelling and B-Gal senescence assay. Moreover, sEVs BDE−47 showed a stronger anti-proliferative effect compared to sEVs DMSO , reinforcing the hypothesis that BDE-47 treatment alters sEVs BDE−47 behaviour in a manner that affects recipient cell response. These findings are consistent with our miRNA enrichment analysis and suggest that the BDE-47 altered EVs may directly impact cell cycle progression and the proliferative capacity of recipient bystander cells. This hypothesis was further supported by transcriptional analysis, which showed a significant increase in the expression of the cell cycle inhibitors p16 and p21 genes in cells treated with EVs derived from BDE-47-exposed macrophages. Both p16 and p21 are key markers of cellular senescence and act by inhibiting cyclin-dependent kinases (CDKs), leading to the inactivation of the retinoblastoma protein (Rb) and arrest of the cell cycle in the G1 phase ( 33 ). While p21 is typically activated in response to DNA damage and p53 signaling, p16 is more often linked to stress-induced or replicative senescence. Notably, in our experimental model, p21 upregulation was more pronounced in cells treated with sEVs BDE−47 compared to controls. This trend was confirmed at the protein level by Western blot analysis, which showed increased p21 expression specifically in sEVs BDE−47 LNCaP treated cells, consistent with the transcriptional findings. This suggests that EV-mediated intercellular communication might represent an important route through which BDE-47 exerts pleiotropic effects on downstream cells. All together, these observations raise intriguing possibilities: the altered surface glycans and miRNA cargo of BDE-47-modified sEVs may allow the biogenesis of EVs with enhanced bioactivity in modulating tumour cell behaviour. Such findings underscore the potential for environmental contaminants to impact not only immune cells perturbating their response as previously shown ( 10 – 12 ) but also influence heterologous cell dynamics via EVs. In particular, this study reveals that BDE-47 selectively affects distinct subsets of macrophage-derived extracellular vesicles, inducing alterations in their glycosylation patterns and miRNA cargo. These modifications are EV subtype-specific and are associated with altered functional outcomes in recipient cells. Together, our findings highlight the importance of dissecting EV heterogeneity to fully understand how environmental insults can shape intercellular communication networks and contribute to pathophysiological processes, including immune dysregulation ( 10 ). The enhanced inhibitory effects of sEVs BDE−47 on LNCaP cell proliferation underscore the functional consequences of these molecular alterations. Indeed, given the stability and bioavailability of EVs in circulation, these vesicles may also serve as potential biomarkers for environmental exposure or as vehicles for systemic signal dissemination. Further research is needed to elucidate how BDE-47 affects the molecular machinery involved in miRNA sorting and the enzymatic pathways that regulate glycosylation, as such insights would significantly advance our understanding of toxicant-induced vesicle reprogramming. Moreover, in vivo studies investigating the biodistribution, immune interactions, and functional consequences of extracellular vesicles derived from BDE-47-exposed macrophages would offer valuable translational perspectives on their potential roles in systemic toxicity and intercellular communication. In conclusion, this study demonstrates that exposure to the flame retardant BDE-47 selectively reshapes the molecular and functional landscape of EVs derived from human macrophages. While EV biogenesis remains intact, significant changes in glycosylation and miRNA cargo are observed, which translate into altered effects on recipient cells. These findings open new approaches for investigating the role of EVs as mediators of environmental toxicity and underscore their potential utility in monitoring pollutant-induced health risks. Abbreviations BrdU - Bromodeoxyuridine BSA - Bovine Serum Albumine BDE-47 - 2,4,2’,4’-tetrabromodiphenyl ether DC SIGN - Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin DTT - dithiothreitol DLS - Dynamic light scattering DMSO – Dimethyl sulfoxide dUC – differential ultracentrifugation Eno-1 - Enolase-1 ESCRT - Endosomal Sorting Complex Required for Transport EVs – Extracellular vesicles FBS – Fetal Bovine Serum lEVs – large Extracellular vesicles LPS - Lipopolysaccharides MGL - Macrophage Galactose-type Lectin MR - mannose receptor MVEs – Multivesicular endosomes MVs – Microvesicles NTA - Nanoparticle Tracking Analysis (NTA) OD - optical density PBDEs – Polybrominated diphenyl ether PBS - phosphate buffered saline PDI - polydispersity index PMA - phorbol 12-myristate-13-acetate PVDF - polyvinylidene fluoride RPMI 1640 - Roswell Park Memorial Institute 1640 SDS - sodium dodecyl sulphate sEVs – small Extracellular vesicles TMB - 3,3′,5,5′- tetramethylbenzidine TSM - tris(hydroxymethyl)aminomethane Declarations Acknowledgement The manuscript was funded by the Next Generation EU, Mission 4 Component 1 PRIN: PROGETTI DI RICERCA DI RILEVANTE INTERESSE NAZIONALE – Bando 2022 PNRR Prot. 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Council","correspondingAuthor":false,"prefix":"","firstName":"Antonella","middleName":"","lastName":"Bongiovanni","suffix":""},{"id":512753501,"identity":"fd3cb7af-9453-4eba-98be-92a9a1ca70c6","order_by":14,"name":"Paolo Colombo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYHACxgMgko+BgY2BoUKCgU0CIsyDTw9YCxsYnUHSgk8PQgtjG5AF1YLTGnP2wwcOfGCwk2Nj70578HGeRT6fdAPj58I9DDL2OLRY9qQlHJzBkGzMxnN2u+HMbRKWbTIHmKVnPMPtMIMDOQaHeRgOJLZJ5G6T5t0mYcAmkcDGzHMAj5bzb5C1zCFGyw0UWxqI0GI54xnQLwZQv8w4BtQic7BZmueABA9QG/YQ408++OBDhZ0cP3vvtgcfauoM5Gc3H/zMc8DGnr0Bh8OQSBhgBKmVwFSLomUUjIJRMApGAT4AAIo4StQz+TUtAAAAAElFTkSuQmCC","orcid":"","institution":"National Research Council","correspondingAuthor":true,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Colombo","suffix":""},{"id":512753507,"identity":"013d627d-43bc-4956-afcc-0ed9e67378b7","order_by":15,"name":"Valeria Longo","email":"","orcid":"","institution":"National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Longo","suffix":""}],"badges":[],"createdAt":"2025-08-28 12:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7480117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7480117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91169569,"identity":"490b8173-3cfd-43af-9470-fe196311efb3","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228427,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of EVs subtypes derived from THP-1 M(LPS) cells. Representative size distribution of large and small EVs (panel A and B) derived respectively from control (DMSO) and BDE-47 treated cells, by Nanoparticles Tracking Analysis. Large and small EVs size distributions were evaluated also by Dynamic Light Scattering analyses (panel C and D).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/d04a90690b1aaed5d503de36.png"},{"id":91169571,"identity":"c300f849-0f1f-4543-90f4-cf8f35921e1c","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197954,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoblot analyses of sEVs and lEVs biomarkers. Analyses of Alix, β-actin, enolase (Eno), and Hsp70, CD63, CD81 and Calnexin in EVs\u003csup\u003eBDE-47/DMSO\u003c/sup\u003e subpopulation. Equal amounts of total proteins were loaded for THP-1 M(LPS) cell lysates and EVs samples (10 μg). Lysate from C2C12 cells (8 μg) was used as positive control (A). Ponceau staining is shown as a control of the total protein loaded per lane (B).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/84e65ee7586d386517374d76.png"},{"id":91170427,"identity":"9cff7bbb-909d-4436-b46f-ac8566c8ee62","added_by":"auto","created_at":"2025-09-12 11:26:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59726,"visible":true,"origin":"","legend":"\u003cp\u003eBinding of small and large extracellular vesicles from BDE-47 or DMSO treated THP-1 macrophages to human C-type lectins. Data were normalized over signal from the lectins-positive controls (glycosylated polyacrylamide polymers or Mannans). Error bars indicate standard deviations. Two way ANOVA multiple comparison was performed with the Tukey's multiple comparisons test (alpha 0.05), using GraphPad Prism 10. (*: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ****: p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/fb4402fdfec3c542ef01103e.png"},{"id":91169573,"identity":"425b163c-bb21-45a3-817b-11aaf164e361","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171875,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expressed miRNA between EVs subpopulations. Comparative analyses of differentially expressed miRNA between lEVs and sEVs derived from THP-1M(LPS) control cells (A), lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e respectively, and between EVs derived from BDE-47 treatment (B), namely lEVs\u003csup\u003eBDE-47\u003c/sup\u003e and sEVs\u003csup\u003eBDE-47\u003c/sup\u003e. The Volcano Plot identifies significant miRNA expression changes by plotting the log2 of the fold changes in miRNA expression on the x-axis versus their statistical significance on the y-axis. The center vertical line indicates unchanged miRNAs, while the two outer vertical lines indicate the selected fold regulation threshold. The horizontal line indicates the selected p-value threshold. MiRNA in the far upper left (green dots) are down-regulated, instead those in the far upper right (red dots) are up-regulated. The Venn diagram (C) highlights the significantly modulated miRNA in control (orange circle, n=17) and in BDE-47-derived (red circle, n=22) subpopulations. No common miRNAs have been identified between the two clusters of subpopulations (blue area).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/545cf89e428da5ae113a980b.png"},{"id":91169582,"identity":"f08aee98-8a05-4627-9e74-08f516643ad1","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":317423,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork diagram illustrates the connections between enriched pathways, where nodes represent biological Reactome pathways and edges indicate shared genes between them.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/9d06fe4ea4ab04e2ba2ddf21.png"},{"id":91170429,"identity":"43588830-e64f-45ad-85e0-c5b08bccc94a","added_by":"auto","created_at":"2025-09-12 11:26:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478521,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment analysis using the Reactome database. Three plots illustrate the relationship between various biological pathways grouped by hierarchical clustering of enriched terms. The size of nodes indicates the number of miRNA-target genes associated with each pathway. The nodes colour gradient represents the adjusted p-value, with blue indicating higher values of 5x10\u003csup\u003e-4\u003c/sup\u003e and red lower values of 5x10\u003csup\u003e-4\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/21616a9575e7b6d639e89a62.png"},{"id":91170426,"identity":"ab92b706-d944-44e4-be2c-4ffd433af01f","added_by":"auto","created_at":"2025-09-12 11:26:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89829,"visible":true,"origin":"","legend":"\u003cp\u003eCell proliferation assay. DNA synthesis was measured by BrdU incorporation into DNA. LNCaP cells were treated with large/small EV\u003csup\u003eDMSO/BDE-47 \u003c/sup\u003efor 72h respecting a ratio 1:20. Data are expressed as absorbance at 450 nm of the means ± SD of four separate experiments, each of which was performed in triplicate(****p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/f086b445570bf6d4a618dbac.png"},{"id":91169590,"identity":"4016a450-ce42-4957-8242-2b783887a3cb","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":498063,"visible":true,"origin":"","legend":"\u003cp\u003eCell morphology and senescence-associated β-galactosidase (SA-β-Gal) activity in LNCaP cells treated with lEVs/sEVs\u003csup\u003eDMSO/BDE-47 \u003c/sup\u003efor 6 days (A). The graph displays the percentages of SA-β-Gal-positive cells (B, ***p\u0026lt;0.005, ****p\u0026lt;0.001 respectively).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/a1efcc7e24ab22c620347e82.png"},{"id":91169584,"identity":"8c92036c-9562-4b39-ac81-c9721210bc66","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":313629,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of cyclin-dependent kinase inhibitor expression, p16 and p21 in LNCaP cells treated withlEVs\u003csup\u003eBDE-47/DMSO\u003c/sup\u003e or sEVs\u003csup\u003eBDE-47/DMSO\u003c/sup\u003e by digital PCR (A and B, **p\u0026lt;0.005, ****p\u0026lt;0.0001) and Western Blot assays (C) of p16, p21 and p53 in LNCaP cells treated with lEVs\u003csup\u003eBDE-47/DMSO\u003c/sup\u003e or sEVs\u003csup\u003eBDE-47/DMSO\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/4a4d004fbf2445be9cd3b335.png"},{"id":93314094,"identity":"bdaaf0e4-c50d-450d-aa9d-06ec8f69fa70","added_by":"auto","created_at":"2025-10-11 22:31:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3690439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/85a994d7-8365-4e0e-ad21-ac86055a3971.pdf"},{"id":91169575,"identity":"31bbd145-69ad-4744-a29d-3364471ef3d0","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1691588,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/90eb9700e6b0e05473261ee4.tif"},{"id":91170428,"identity":"2d46cd4d-d173-4410-a77c-5bc9e91eefec","added_by":"auto","created_at":"2025-09-12 11:26:24","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2063676,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/c5a4040282098b06701b0dcd.tif"},{"id":91169606,"identity":"46d32c1a-ab82-425d-a280-c4aed00456de","added_by":"auto","created_at":"2025-09-12 11:18:24","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7953447,"visible":true,"origin":"","legend":"","description":"","filename":"UncroppedWBFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/de2e499a4d3640335d1dab18.png"},{"id":91170430,"identity":"5b2b6436-9e5c-4218-b07b-8d96c3551b62","added_by":"auto","created_at":"2025-09-12 11:26:24","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":414219,"visible":true,"origin":"","legend":"","description":"","filename":"uncroppedWBfigure9panelC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7480117/v1/091987eb4024dd9f0829277e.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"BDE-47 Alters Surface Glycans and microRNA Cargo of Macrophage-Derived Vesicle Subpopulations Modulating EV-Mediated Senescence Signaling","fulltext":[{"header":"1. Background","content":"\u003cp\u003eExtracellular vesicles (EVs) are small, membrane-bound particles secreted by cells that play critical roles in various biological functions, particularly in intercellular communication and in both pathological and physiological processes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). All cells secrete extracellular vesicles transporting proteins, lipids, and nucleic acids between cells in various tissues and fluids (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Based on their biogenesis, EVs can be classified in two major classes defined large extracellular vesicles (lEVs), often referred to as microvesicles (MVs), and small extracellular vesicles (sEVs), commonly known as exosomes. MVs generation is a complex cellular process involving the outward budding of the plasma membrane to produce extracellular vesicles typically ranging in size from 50 to 1000 nm. On the other hand, exosomes have an endosomal origin and range in size from 30 to 150 nm (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Exosome are generated within endosomes as intraluminal vesicles and are released when these multivesicular endosomes (MVEs) fuse with the plasma membrane (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Multiple pathways have been proposed for exosome biogenesis, both dependent and independent of the Endosomal Sorting Complex Required for Transport (ESCRT) machinery (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite their different biogenesis pathways, the literature often refers to \"small\" and \"large\" EVs to describe the two fractions obtained through various methods, such as differential ultracentrifugation (dUC) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, ordinary understanding of the heterogeneity and diverse functions of EV sub-populations is still a matter of discussion. With reference to inflammatory diseases, upon stimulation, immune cells release more EVs to regulate the body\u0026rsquo;s immunity and metabolism (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). As a key regulatory component of the immune response, macrophages secrete a large set of cytokines and a variety of signals, including EVs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The diversity of macrophage functions depends on their high degree of plasticity, which is a key characteristic of this cell type (REF?). In this view, it is challenging to understand the heterogeneity of EVs in response to different stimuli, since EVs are typically analyzed in bulk, capturing only the effects of the entire population. More recently, human EVs have also been investigated in exposure science and toxicology for their capability of influencing cell\u0026rsquo;s functions upon pollutant treatments (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Our research group focused on the effect of an environmental pollutant, the BDE-47 (2,2',4,4'-Tetrabromodiphenyl ether), on human macrophage activity and the modulation of the expression of microRNA cargo in macrophage-derived EVs stimulated with LPS (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). BDE-47 is a flame retardant belonging to the PBDE (Polybrominated Diphenyl Ethers) family. It is one of the most common compounds used to enhance the fire resistance of materials such as plastics, electronics, furniture, and textiles (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Despite PBDEs have been banned from the market, BDE-47 has been detected in the environment, wildlife, and human tissues, raising concerns about its potential health impacts as endocrine disrupting substances (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Regarding their effects on the macrophage immune response, we demonstrated that BDE-47 exerts diverse mechanisms of action, including direct immunotoxic effects on macrophages. Specifically, it impairs the secretion of proinflammatory cytokines in M(LPS) macrophages (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) while also influencing sEVs biogenesis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and their miRNA cargo (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, we found that sEVs derived from BDE-47-exposed macrophages can modulate intracellular miRNAs levels and alter the expression of surface markers in na\u0026iuml;ve resting THP-1 M(0) macrophages, highlighting the immunomodulatory potential of this compound (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In this study, we further studied the heterogeneity of EV populations by using differential centrifugation to isolate and enrich small and large EVs derived from BDE-47-treated THP-1 M(LPS) macrophages. Specifically, we fractionated the EVs into two distinct subpopulations: 10K EVs (or large EVs, lEVs) and 100K EVs (or small EVs, sEVs), based on their sedimentation properties. To further characterize these EV subpopulations, we performed a comprehensive analysis focusing on five key aspects: (i) their physical properties, including size distribution and morphology; (ii) their surface marker expression to distinguish vesicle subtypes and determine their cellular origin; (iii) their binding to different human carbohydrates-binding proteins; (iv) their miRNA cargo, assessing potential differences in molecular content and functional implications; and (v) their functional role in modulating the proliferative capacity of prostate cancer LNCaP cells. By elucidating these characteristics, our study provides new insights into how an environmental pollutant such as BDE-47 can alter extracellular vesicle composition, highlighting its potential role in the modulation of EV-mediated intercellular communication within the framework of environmental toxicology.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Reagents\u003c/h2\u003e\u003cp\u003eThe 2,4,2\u0026rsquo;,4\u0026rsquo;-tetrabromodiphenyl ether (BDE-47) was purchased from Toronto Research Chemicals (Ontario, Canada) and dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich, Milan, Italy, cat. n. D2650) at the concentration of 25 mM. \u003cem\u003eE. coli\u003c/em\u003e Lipopolysaccharides (LPS, serotype O26:B6) were purchased from Sigma-Aldrich.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Cell line cultures\u003c/h2\u003e\u003cp\u003eThe human monocytic leukemia THP-1 cell line (ECACC 88081201) was maintained in culture with RPMI 1640 medium (Gibco Life Technologies, Monza, Italy), supplemented with heat inactivated 10% Fetal Bovine Serum (FBS, Gibco, Life Technologies, Monza, Italy) and 1% antibiotic (5.000 U/mL penicillin, 5.000 \u0026micro;g/mL streptomycin sulphate, both from Sigma-Aldrich, Gibco, Life Technologies). The monocytes were treated for 72 hours at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e with 200 nM phorbol 12-myristate-13-acetate (PMA, Sigma-Aldrich) to obtain the na\u0026iuml;ve THP-1 M0 macrophage. The THP-1 M(LPS) phenotype was induced by stimulation with 10 ng/mL of LPS for 24 hours at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eLNCaP cell line (ATCC CRL-1740) was cultured in RPMI 1640 medium supplemented with 10% FBS and 1% antibiotics at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Purification of large and small extracellular vesicles subpopulations from THP-1 M(LPS) treated with BDE-47\u003c/h2\u003e\u003cp\u003eLarge and small extracellular vesicles (lEVs and sEVs, respectively) were prepared after incubation of the THP-1 M0 na\u0026iuml;ve macrophages with 3 \u0026micro;M BDE-47 (lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e) or 0.0125% DMSO (control vehicle, lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e) in RPMI 1640 medium (Gibco Life Technologies, Monza, Italy), supplemented with heat inactivated 10% EV-depleted FBS and 1% antibiotic for 24 hours at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Thereafter, cells were differentiated in THP-1 M(LPS) phenotype with 10 ng/mL of LPS and incubated for a further 24 hours at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. The supernatants were collected, and EV subpopulations were separated according to differential ultracentrifugation (dUC) method (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Specifically, three independent preparations were used for the subsequent assays. The supernatants were first centrifuged at low speed to remove cells and debris and subsequently centrifuged at 10.000 xg for 30 minutes at 4\u0026deg;C to isolate lEVs. The lEVs were resuspended in a proper volume of microfiltered 1X phosphate buffered saline (PBS) w/o Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e and stored at -80\u0026deg;C until use. Afterwards, sEVs were collected from the supernatants into Beckman Coulter polypropylene open top tubes via centrifugation at 118.000 xg for 70 minutes at 4\u0026deg;C using a Beckman SW28 rotor. The sEVs pellets were washed in 1X PBS w/o Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e, centrifuged and again at 118.000 xg for 70 minutes at 4\u0026deg;C and finally, resuspended in 50 \u0026micro;l of microfiltered 1X PBS w/o Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Nanoparticle Tracking Analysis (NTA) of THP-1 derived large EVs and small EVs\u003c/h2\u003e\u003cp\u003eLarge and small EVs size distribution and concentration were measured using the NanoSight NS300 (Malvern Panalytical, UK), equipped with a 488 nm laser, a high sensitivity sCMOS camera, a syringe pump, and a second 405 nm laser and a CMOS camera. LEVs and sEVs samples were diluted in particle-free water (Water, HPLC grade, Sigma-Aldrich, filtered by 20 nm using Whatman Anotop filters) to generate a dilution in which 20\u0026ndash;120 particles per frame were tracked and to also obtain a concentration within the recommended measurement range (1\u0026ndash;10x10\u003csup\u003e8\u003c/sup\u003e particles/mL). Five experiment videos of 60 second duration were analyzed using NTA 3.4 Build 3.4.003 (camera level 15\u0026ndash;16). A total of 1500 frames/sample were examined, captured, and analyzed by applying instrument-optimized settings with a suitable detection threshold so that the observed particles were marked with red crosses and no more than 5 blue crosses were visible. Further settings, such as blur size and Max Jump Distance, were set to \u0026ldquo;automatic\u0026rdquo; and viscosity was set to that of water (0.841\u0026ndash;0.844 cP).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Dynamic light scattering (DLS)\u003c/h2\u003e\u003cp\u003eThe vesicle solutions were diluted in 1X PBS and centrifuged at 1000 xg for 10 minutes at 4\u0026deg;C to remove any dust particles or aggregates. The supernatant, collected using MilliQ-rinsed pipette tips, was transferred into a quartz cuvette and maintained at 20\u0026deg;C within a temperature-controlled compartment of a BI200-SM goniometer (Brookhaven Instruments, Nashua, NH, USA). Measurements were conducted using a He-Ne laser source tuned at 633 nm (JDS Uniphase 1136 P) and a single-pixel photon counting detector (Hamamatsu C11202-050, Hamamatsu Photonics Deutschland GmbH, Germany). The intensity autocorrelation function, g\u003csub\u003e2\u003c/sub\u003e(t), was acquired at a scattering angle of 90\u0026deg; using a BI-9000 correlator (Brookhaven Instruments, Nashua, NH, USA) and size distribution P\u003csub\u003eq\u003c/sub\u003e(σ) was calculated by assuming that the diffusion coefficient distribution is shaped as a Schultz distribution, as described in Paterna and coworkers (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Two robust parameters were obtained from this analysis: D\u003csub\u003ez\u003c/sub\u003e (the z-averaged hydrodynamic diameter) and PDI (the polydispersity index, which is an estimate of the distribution width).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Western blot\u003c/h2\u003e\u003cp\u003eCell lysate, lEVs and sEV samples, from control and treated THP-1 M(LPS) macrophages, were mixed with proper volumes of 5X loading buffer [0.25 M Tris-Cl pH 6.8, 10% sodium dodecyl sulphate (SDS), 50% glycerol, 0.25M dithiothreitol (DTT), 0.25% bromophenol blue], heated at 100\u0026deg;C for 5 min and loaded into 10% SDS-PAGE for electrophoretic separation. Then, proteins were blotted onto polyvinylidene fluoride (PVDF) membranes which were blocked with BSA-TBS-T solution [3% powdered with bovine serum albumin in TBST (50 mM Tris HCl pH 8.0, 150 mM NaCl with 0.05% Tween 20)] for 1 hour at room temperature, followed by primary antibody incubation overnight at 4\u0026deg;C. The antibodies used were: anti-Alix (clone 3A9, dil. 1:150 in 3% BSA/1X TBS-T), anti-HSP70 (clone W27 dil. 1:500 in 5% Milk/1X TBS-T), anti-Enolase, anti-βActin (clone A5 and clone AC15, respectively, dil. 1:400 in 3% BSA/1X TBS-T, Santa Cruz Biotechnology, Dallas, Texas, USA), anti-CD63 (rabbit polyclonal, dil.1:500, from Invitrogen), anti-Calnexin (rabbit polyclonal, dil.1:1000, from Novus Biologicals), anti CD81 (clone B11, dil.1:200, from Santa Cruz Biotechnology, USA). The membranes were incubated for 1 hour with the horseradish peroxidase-conjugated secondary anti-mouse or anti-rabbit antibodies according to the manufacturer\u0026rsquo;s instructions (Cell Signaling Technologies Inc., Beverly, MA, USA) and the signals were revealed using Super Signal\u0026trade; Pierce\u0026trade; ECL (Thermo Fisher Scientific, Monza, Italy).\u003c/p\u003e\u003cp\u003eLNCaP cell lysates were prepared using RIPA buffer (Cell Signaling Technologies). Then, proteins were blotted onto nitrocellulose membranes. After transfer blocking was performed using Odyssey\u0026reg; Blocking Buffer (OBB, LI-COR) diluted in TBS, followed by incubation with primary antibodies anti-β-actin (Sigma-Aldrich), anti-p21 (Cell Signaling Technologies), anti-p53 and anti-p16 (Santa Cruz Biotechnology) diluted in OBB. Detection was carried out using IRDye\u0026reg; 800CW or Alexa Fluor 680-conjugated secondary antibodies (LI-COR; Invitrogen), and membranes were scanned with the Odyssey IR scanner (LI-COR Biosciences, Lincoln, NE, USA). Band intensities were quantified using Odyssey 3.0 software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 ELISA-based solid-phase assay\u003c/h2\u003e\u003cp\u003eA volume of 50 \u0026micro;L from a suspension of vesicles at 2 x 10\u003csup\u003e7\u003c/sup\u003e particles/mL in PBS (10 mM, pH 7.4), was used to coat NUNC MaxiSorp wells (overnight, 4\u0026deg;C). After discarding and washing (2 x 150 \u0026micro;L) with calcium and magnesium-containing buffer TSM (20 mM tris(hydroxymethyl)aminomethane (Tris)-HCl, pH 8.0; 150 mM NaCl; 1 mM CaCl\u003csub\u003e2\u003c/sub\u003e; 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e), wells were blocked with 80 \u0026micro;L 1% BSA solution (Sigma-Aldrich, lyophilized powder, \u0026ge;\u0026thinsp;96%, agarose gel electrophoresis) in TSM at room temperature for 30 min. The blocking solution was discarded and 50 \u0026micro;L of different C-type-lectins including human-Fc Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin (DC-SIGN), Macrophage Galactose-type Lectin (MGL) and mannose receptor (MR) at 1 \u0026micro;g/mL were added. After 1 h at room temperature, wells were washed with TSM (2 x 150 \u0026micro;L) and 100 \u0026micro;L of anti-human horseradish peroxidase (0.3 \u0026micro;g/mL, Goat anti-human IgG-HRP from JacksonImmuno) were added. After 30 min, wells were washed with TSM (2 x 150 \u0026micro;L). Finally, 100 \u0026micro;L of a substrate solution (3,3\u0026prime;,5,5\u0026prime;- tetramethylbenzidine, TMB, in citric/acetate buffer, pH 4, and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) were added. After 10 min at room temperature the reaction was stopped with 50 \u0026micro;L of H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (0.8 M) and the optical density (OD) was measured at 450 nm in an ELISA reader. The experiment was performed in duplicate and data were normalized over the signal at 450 nm from the positive controls used for each C-type lectin. Polyacrylamide polymers, functionalized with different glycans were purchased from Lectinity, MW approx. 20 kDa, carbohydrate content around 20% mol.: GalNAcα-OCH\u003csub\u003e2\u003c/sub\u003eCH\u003csub\u003e2\u003c/sub\u003eCH\u003csub\u003e2\u003c/sub\u003eNH\u003csub\u003e2\u003c/sub\u003e 0030-PA (PAA-Tn, positive control for MGL, 20\u0026micro;g/mL). Mannans from \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e (positive control for DC-SIGN and MR) was purchased from Sigma-Aldrich and used at 10\u0026micro;g/mL to coat the ELISA wells. Statistical analysis was performed by means of two way ANOVA multiple comparison with the Tukey's multiple comparisons test (alpha 0.05), using GraphPad Prism 10.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 MiRNA purification from large EVs and small EVs\u003c/h2\u003e\u003cp\u003ePurified lEVs and sEVs from control (lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e) and treated (lEVs\u003csup\u003eBDE\u0026minus;\u0026thinsp;47\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;\u0026thinsp;47\u003c/sup\u003e) THP-1 M(LPS) cells were used for miRNA purification. The same number of lEVs and sEVs (3x10\u003csup\u003e9\u003c/sup\u003e particles) was used to extract miRNAs. The samples were diluted up to 200 \u0026micro;L with 1X PBS w/o Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e and then lysed using 1 mL of QIAzol Lysis Reagent (Qiagen, Milan, Italy). The purification of total RNA enriched in miRNAs was performed according to the miRNeasy Serum/Plasma Kit manufacturer's protocol (Qiagen). To control the yield, purity, and integrity of samples, 1\u0026micro;l of a Spike-in mix containing UniSp2 (5\u0026rsquo;GUACUCGGCUUACGAUCGUAA), UniSp4 (5\u0026rsquo;GAUGGCAUUCGAUCAGUUCUA) and UniSp5 (5'GAUGCUACGGUCAAUGUCUAAG) miRNAs (Qiagen) was added to the samples before the extraction phases. The miRNA samples were eluted in 15\u0026micro;l of H\u003csub\u003e2\u003c/sub\u003eO DNAse/RNAse free, and concentrations were evaluated by Nanodrop analysis (NanoDrop\u0026trade; One/OneC Microvolume UV-Vis Spectrophotometer, Thermo Fisher Scientific, Monza, Italy).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.8.1 Large EVs and small EVs MiRNA cargo profiling\u003c/h2\u003e\u003cp\u003eThe cDNA synthesis from lEVs and sEVs miRNAs was performed using the miRCURY\u0026trade; LNA RT kit (Qiagen). A mix containing UniSp6 (5\u0026rsquo;CUAGUCCGAUCUAAGUCUUCGA) and \u003cem\u003eCaenorhabditis elegans\u003c/em\u003e miR-39-3p (Cel miR39-3p, 5\u0026rsquo;UCACCGGGUGUAAAUCAGCUUG) exogenous controls was added to reactions according to the manufacturer's protocol in a final volume of 20 \u0026micro;l. The retro-transcriptions were performed for 60 minutes at 42\u0026deg;C. Then, the reverse transcriptase enzyme was inactivated for 5 minutes at 95\u0026deg;C. Subsequently, the expression profile of 179 miRNA was evaluated using the miRCURY LNA miRNA Focus PCR Panel (panel code: YAHS-106Y, Qiagen). Specifically, the cDNA template was amplified by Real Time analysis (StepOnePlus\u0026trade; Real Time PCR System, Applied Biosystems, Milan, Italy) and the miRCURY LNA\u0026trade; SYBR GREEN PCR kit. The Real Time PCR conditions were an initial heat activation step at 95\u0026deg;C for 2 minutes and 40 cycles of two-step PCR, denaturation at 95\u0026deg;C for 10 seconds, annealing/extension at 56\u0026deg;C for 1 minute. The CT data obtained from control (lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e) and samples (lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e) extracellular vesicles were analyzed using the Qiagen GeneGlobe miRCURY LNA miRNA PCR Data Analysis software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dataanalysis2.qiagen.com/miRCury\u003c/span\u003e\u003cspan address=\"https://dataanalysis2.qiagen.com/miRCury\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); the data were normalized using the geNorm \u0026ldquo;Predefined reference miRNA only\u0026rdquo; function as references. The miRNAs were considered changed between the two groups if the fold change was \u0026lt;\u0026thinsp;0.5 (down-regulated miRNA) or the fold change was \u0026gt;\u0026thinsp;2 (up-regulated miRNA). The miRNAs with a quantification cycle (CT)\u0026thinsp;\u0026gt;\u0026thinsp;35 were considered undetected. The complete list of 179 analyzed miRNAs is reported in Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.8.2 Computational analysis\u003c/h2\u003e\u003cp\u003ePathway enrichment analysis was conducted to identify key biological pathways altered due to the deregulation of miRNAs in lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e. First, the validated gene targets of upregulated and downregulated miRNAs in lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e compared to sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e were identified using the multiMiR R package (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specifically, we retrieved only experimentally validated miRNA-target interaction from three publicly available databases: miRecords, miRTarBase and TarBase. To focus on biologically relevant target genes, we applied a graph-based selection approach. A directed graph was constructed, where miRNAs and their target genes were represented as nodes, and interactions between them as edges. To reduce noise and prioritize the most functionally significant targets, we filtered out genes with a low degree of connectivity, retaining only those interacting with a substantial number of miRNAs (n\u0026thinsp;=\u0026thinsp;4). This step helped refine the analysis by emphasizing genes more likely to play a crucial role in the observed regulatory network. The selected target genes were then subjected to Reactome enrichment analysis using the ReactomePA R package (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). To streamline the interpretation of enriched pathways, we performed a pairwise similarity analysis based on Jaccard\u0026rsquo;s similarity index, clustering related pathways and reducing redundancy in the enrichment results.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.9 LNCaP cell proliferation assay\u003c/h2\u003e\u003cp\u003eCell proliferation was assessed by measuring bromodeoxyuridine (BrdU) incorporation into DNA using a colorimetric immunoassay (Roche Diagnostics GmbH, Mannheim, Germany), following the manufacturer's instructions. Briefly, LNCaP were seeded at the concentration of 5.000 cells/well in 96-well plate to test the effect of EVs on cellular proliferation. Cells were left to adhere for 24h. Subsequently, they were incubated with lEVs/sEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e respecting the cell-EVs ratio of 1:20 for 72h. BrdU was added 16 h before the end of treatments. Values were expressed as means of OD\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of four separate experiments, each performed in triplicate. A statistical analysis was performed using a one-way ANOVA and Bonferroni\u0026rsquo;s post-hoc analysis test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Senescence-associated β-galactosidase assays\u003c/h2\u003e\u003cp\u003eLNCaP cells (20x10\u003csup\u003e3\u003c/sup\u003e) were grown on an 8-well chamber slide. At time 0, the medium was replaced with a fresh complete medium with lEVs/sEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e for 6 days. After treatments, the cells were fixed and stained for β-galactosidase activity, using a Senescence Cell Staining kit, following the manufacturer\u0026rsquo;s instructions (Sigma‐Aldrich). The percentage of senescence‐associate β‐gal positive cells was determined by counting the number of blue cells within a sample, using Olympus CKX53 (Olympus Corporation, Tokyo, Japan) microscope with an X20 lens. Ten random fields were photographed for each condition, and the percentage of SA‐β‐gal‐positive cells was calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.11 RNA isolation, retrotranscription and Digital PCR analysis\u003c/h2\u003e\u003cp\u003eTotal RNA from LNCaP treated with lEVs/sEVsDMSO/BDE-47 was isolated according to RNeasy mini kit protocol (Qiagen). 1 \u0026micro;g of each RNA template was retro-transcribed using the QuantiTect Reverse Transcription Kit (Qiagen). The cDNA was diluted 1:500 and the expression of p16 (or CDKN2A, NM_000077) and p21 (or CDKN1A, NM_000389) genes was evaluated by Digital PCR technology using the QIAcuity One System, the EVA Green detection method and the Quantitect primer assays (Qiagen). Specifically, 40 \u0026micro;l of reaction mixtures were prepared in 96 well plates according to the QIAcuity EG PCR Kit manufacturer\u0026rsquo;s protocol (Qiagen) and subsequently dispensed into the Qiacuity\u0026trade; Nanoplate 26k 24-well (Qiagen). Positive (the human β-actin gene, NM_001101) and negative (no template reactions) controls were included in each experiment (n\u0026thinsp;=\u0026thinsp;4 independent replication). The cycling profile consisted of a denaturation step at 95\u0026deg;C for 2 minutes, 40 cycles at 95\u0026deg;C for 15 seconds, 56\u0026deg;C for 15 seconds and 72\u0026deg;C for 15 seconds, followed by 40\u0026deg;C for 5 minutes. The imaging step was performed selecting the green channel and setting the exposure duration at 300 ms and Gain\u0026thinsp;=\u0026thinsp;3. Data were analyzed by means of QIAcuity Suite Software version 2.5.0.1 (Qiagen) and the absolute quantities were reported as copy number/\u0026micro;l. Any values above 0 copies/\u0026micro;l were considered as positive. The statistical analysis was performed using one-way ANOVA.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Biophysical characterization of EVs subpopulations derived from BDE-47 treated THP-1 M (LPS) macrophages\u003c/h2\u003e\u003cp\u003eThe conditioned media from three independent preparations of THP-1 M(LPS) macrophages treated with BDE-47 or DMSO (control) were collected. Small and large EVs from each preparation (sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e/lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e/lEVs\u003csup\u003eDMSO\u003c/sup\u003e respectively) were isolated according to the dUC method. To assess whether exposure to the environmental pollutant BDE-47 influences the size distribution and particle concentration of EVs, we performed Nanoparticle Tracking Analysis on both sEVs and lEVs isolated from treated and untreated cells. The size distribution profiles of sEVs and lEVs revealed no significant differences between control (DMSO) and BDE-47-treated samples with overlapping curves indicating comparable particle size distribution profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). However, the observed quantitative difference between sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e/sEVs\u003csup\u003eDMSO\u003c/sup\u003e particles highlights intrinsic differences in their secretion which may reflect distinct biogenesis pathways or cellular handling mechanisms as previously shown by our group (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These findings were also supported by Dynamic Light Scattering analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D). In Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we have summarized concentration values and the size distributions of EVs subtypes obtained by NTA and DLS assays.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEVs subtypes concentrations and size distributions EV subtype\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEV subtypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConcentration (particles/mL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003elEVs\u003c/b\u003e \u003csup\u003e\u003cb\u003eDMSO\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6x10\u003csup\u003e11\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4x10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113,8\u0026thinsp;\u0026plusmn;\u0026thinsp;12 nm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003elEVs\u003c/b\u003e \u003csup\u003e\u003cb\u003eBDE\u0026minus;47\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25x10\u003csup\u003e11\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4x10\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113,6\u0026thinsp;\u0026plusmn;\u0026thinsp;22 nm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003esEVs\u003c/b\u003e \u003csup\u003e\u003cb\u003eDMSO\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,0x10\u003csup\u003e11\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7x10\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139,7\u0026thinsp;\u0026plusmn;\u0026thinsp;15 nm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003esEVs\u003c/b\u003e \u003csup\u003e\u003cb\u003eBDE\u0026minus;47\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.9x10\u003csup\u003e11\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6x10\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125,3\u0026thinsp;\u0026plusmn;\u0026thinsp;22 nm\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\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEVs parameters derived by Dynamic Light Scattering analyses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEVs subtypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD\u003csub\u003ez\u003c/sub\u003e (nm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePDI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elEVs\u003csup\u003eDMSO\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e265\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e265\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esEVs\u003csup\u003eDMSO\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e292\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e294\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Large and small EVs specific markers evaluation\u003c/h2\u003e\u003cp\u003eTo support the size distribution data obtained from NTA and DLS analyses, and to exclude potential cross-contamination between large and small EV subpopulations, we performed Western blot assays to assess the expression of specific markers associated with each EV type. This approach allowed us to confirm the identity and purity of the isolated EV fractions based on established molecular signatures. Specifically, we assessed the expression of Enolase-1 (Eno-1), Hsp70, Alix, CD63, CD81 and Calnexin. Our findings are consistent with the MISEV 2018 and MISEV 2024 Guidelines. Indeed, we found a higher expression of CD63 and CD81 in sEVs subtypes (sEVs\u003csup\u003eDMSO\u003c/sup\u003e/sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e) compared to lEVs, while the Calnexin showed greater expression in the large EV subpopulations (lEVs\u003csup\u003eDMSO\u003c/sup\u003e/lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e). No significant differences were found when comparing BDE-47-derived EV subpopulations (lEVs\u003csup\u003eDMSO\u003c/sup\u003e versus lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e versus sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e, respectively). These results suggest that BDE-47 does not interfere with the expression of specific EVs markers. The immunoblot data are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e panels A and B.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Evaluation of BDE-47's effects on large and small EVs binding to human lectins\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo study lEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e and/or sEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e binding to human lectins, we employed an ELISA-based solid-phase assay to evaluate EV interactions with the C-type lectins DC-SIGN, Langerin, MR, and MGL. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, EVs exhibited significant binding to DC-SIGN and MGL, consistent with the presence of high-mannose, fucosylated, or α-GalNAc residues on the N or O-glycan structures on their surface. Interestingly, the MR, which preferentially binds terminal mannoses, shows non-detectable interactions in the tested experimental conditions. These data suggested that in lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e there is a higher presence of C-type lectins-specific glycan structures compared to both lEVs\u003csup\u003eDMSO\u003c/sup\u003e and the sEVs \u003csup\u003eDMSO\u003c/sup\u003e/sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e groups. Furthermore, we observed a higher interaction between sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and MGL than sEVs\u003csup\u003eDMSO\u003c/sup\u003e. These findings suggest that EVs may utilize C-type lectin recognition to mediate immune modulation, and that BDE-47 may influence the expression of lectin-specific glycan structures on the surfaces of EV subpopulations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4. BDE-47 Modulates miRNA Sorting into Macrophage-Derived sEVs and lEVs and Identifies Associated Pathways\u003c/h2\u003e\u003cp\u003eIn a previous study, we demonstrated that the flame retardant BDE-47 can modulate the expression of miRNAs cargo in macrophage derived sEVs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Here, we have expanded our analysis to focus on BDE-47 ability to rewire miRNA sorting between lEVs and sEVs subpopulations, as well as its impact on the molecular signals channelled by these vesicles to target cells. In these perspectives, we purified lEVs and sEVs subpopulation from conditioned culture media collected from control (lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e) or BDE-47 (lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e) treated THP-1 M(LPS) according to the dUC gold-standard method. MiRNAs were isolated from lEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eDMSO/BDE\u0026minus;47\u003c/sup\u003e subpopulations and the level of expression of 179 miRNAs (reported in Additional Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) assessed through microarray analyses. Comparative analyses of differentially expressed miRNAs (lEVs\u003csup\u003eDMSO\u003c/sup\u003e versus sEVs\u003csup\u003eDMSO\u003c/sup\u003e and lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e versus sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e), were performed and the results summarized in the volcano plots shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B. Data highlight that lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e differ in their miRNA cargo (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) with 17 significantly modulated miRNAs listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Conversely, the analyses of EVs subpopulations derived from BDE-47-conditioned media revealed the modulation of 22 miRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The miRNAs\u0026rsquo; fold changes and p-values are included in both tables. Additionally, the relationships between the two clusters of subpopulations lEVs\u003csup\u003eDMSO\u003c/sup\u003e/sEVs\u003csup\u003eDMSO\u003c/sup\u003e and lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e/sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e were evaluated and visually represented using a Venn diagram as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC (orange and red circles). It is interesting to note that the two clusters of EVs do not share any common modulated miRNA in the selected set of molecules (indicated by the blue area).\u003c/p\u003e\u003cp\u003eTo identify the putative processes and pathways targeted by BDE-47-derived EVs subtypes, an \u003cem\u003ein silico\u003c/em\u003e analysis was performed. Indeed, using the list of 22 deregulated miRNAs in lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e versus sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e, we explored the biological pathways potentially influenced by co-expressed miRNAs, leveraging information on their experimentally validated gene targets. For this purpose, we identified gene target sets associated with the 22 miRNAs querying three publicly available databases: miRecords, miRTarBase and TarBase. These databases provide experimentally validated miRNA-target interactions, with miRecords integrating data from multiple sources, miRTarBase focusing on interactions supported by techniques such as luciferase assays and qPCR, and TarBase offering one of the most comprehensive collections of validated targets with functional annotations. In our analysis we selected target genes that were included in all three databases to ensure high-confidence miRNA-target interactions. To identify key biological pathways associated with selected target genes, an enrichment analysis was performed, and the results were visualized using network and hierarchical clustering approaches.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential expressed miRNAs between lEVs\u003csup\u003eDMSO\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emiRNA ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFold Regulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-451a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-200a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-485-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-154-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-382-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-532-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-497-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-136-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-208a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-629-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-22-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-144-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-92a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-483-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-151a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-14.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-136-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011120\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\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferential expressed miRNAs between lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003emiRNA ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFold Regulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-486-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-125b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-150-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-221-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005887\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-let-7f-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-27b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-106a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-let-7a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-let-7b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-let-7b-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-30e-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-148b-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-20a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-17-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-101-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-23a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-223-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.036191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-let-7d-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-192-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-18a-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-140-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehsa-miR-584-5p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023959\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\u003eThe network diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the connections between enriched pathways, where nodes represent biological Reactome pathways and edges indicate shared genes between them. The size of each node corresponds to the number of genes involved, while the color scale represents the adjusted p-value (p.adjust), with darker colors indicating higher statistical significance. The pathways are organized into functional groups, with key clusters including senescence-associated processes, cell cycle regulation, and signaling cascades such as NOTCH, AKT, and ALK. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a hierarchical clustering dendrogram of the same enriched pathways, grouping functionally related pathways based on gene overlap. The clustering reveals distinct modules corresponding to major biological themes, such as senescence regulation, G1/S cell cycle transition, AKT activation, and interleukins signaling. The color-coded background highlights key pathway categories, further emphasizing the functional relationships among the enriched pathways. Overall, the enrichment analysis indicates a strong association of the dataset with cellular senescence, transcriptional regulation, and oncogenic signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, it has been demonstrated that hsa-miR-106a-5p, hsa-miR-20a-5p, and hsa-miR-17-5p play key roles in regulating the proinflammatory response and are involved in the development of various cancers, including thyroid, prostate, and colorectal cancers, as well as glioma and melanoma (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Effects of lEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e on LNCap cells: analyses of cell proliferation\u003c/h2\u003e\u003cp\u003eBased on insights from our bioinformatic analysis, a BrdU incorporation assay was conducted to evaluate the potential functional role of EVs in modulating the proliferative capacity of heterologous cell lines, specifically LNCaP cells. To this end, human prostate cancer cell line LNCaP were treated for 72 hours with either lEVs or sEVs derived from BDE-47- or DMSO-treated THP-1 M(LPS) cells. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that both sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e significantly reduce LNCaP cell proliferation compared to their respective lEV counterparts. Notably, sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e exert a more pronounced antiproliferative effect than sEVs\u003csup\u003eDMSO\u003c/sup\u003e, suggesting that BDE-47 exposure may enhance the functional capacity of sEVs to suppress cell proliferation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Effects of lEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e and sEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e on LNCap cells: analyses of senescence-markers\u003c/h2\u003e\u003cp\u003eTo further explore the underlying mechanisms contributing to this antiproliferative effect and based on the results of our bioinformatic analysis, we next investigated whether these EV subpopulations might be involved in the induction of cellular senescence. LNCaP cells were exposed to lEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e or sEVs\u003csup\u003eBDE\u0026minus;47/DMSO\u003c/sup\u003e for six days, followed by evaluation using the senescence-associated β-galactosidase assay. Microscopic analysis and quantification of β-galactosidase-positive cells revealed a marked reduction in cell number following treatment with sEVs confirming our proliferation assay. Additionally, the remaining cells exhibited a predominant blue coloration, indicative of senescence-associated β-galactosidase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, panel A). Statistical analysis is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, panel B.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring cellular senescence, the expression of cyclin-dependent kinase inhibitors p16 and p21 is typically upregulated. To investigate this point, we assessed the expression levels of p16 and p21, encoded by the \u003cem\u003eCDKN2A\u003c/em\u003e and \u003cem\u003eCDKN1A\u003c/em\u003e genes, respectively. Digital PCR analysis revealed a similar increase in p16 mRNA copy number/\u0026micro;L in LNCaP cells treated with either lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e or sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e, compared to their respective controls. In contrast, p21 expression was significantly higher in cells treated with sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e than in those treated with lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e or sEVs\u003csup\u003eDMSO\u003c/sup\u003e, indicating a more robust activation of this senescence marker by sEVs following BDE-47 exposure. Finally, we assessed the expression levels of p16,\u0026nbsp;p21 and p53 proteins in LNCaP cells treated with different EV subpopulations. As shown by the Western blot analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, treatment with sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e led to an upregulation of both p16 and p21 compared to the other conditions. Furthermore, the upregulation of p53, along with p21, indicates the involvement of the p53/p21 axis in the induction of cellular senescence (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This increased expression is consistent with the induction of a senescent phenotype and supports the role of sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e in promoting cell cycle arrest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eSeveral studies have investigated the association between circulating exosome levels and environmental pollutant exposure, suggesting that fluctuations in exosome abundance may be relevant to serve as indicators of toxicant exposure (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, the analysis of this relationship is complex and influenced by multiple variables, including the type of pollutant, duration and intensity of exposure, and the specific tissues or organs affected (\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our study presents an \u003cem\u003ein vitro\u003c/em\u003e comprehensive biophysical, molecular and functional characterization of extracellular vesicle subpopulations, focusing on the characterization of large and small EVs secreted by THP-1 M(LPS) macrophages following exposure to the flame retardant BDE-47. Indeed, functional assays of extracellular vesicles are commonly conducted on total EV preparations, without considering the intrinsic heterogeneity of distinct EV subpopulations. Our findings highlight the ability of environmental toxicants to reshape the molecular landscape of EVs and, in this way, modulate their downstream biological effects. In our experimental set-up, differential ultracentrifugation enabled the efficient isolation of distinct EV subpopulations and, NTA and DLS confirmed consistent size distributions and particle concentrations between control and BDE-47-treated samples as previously shown (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). While sEVs and lEVs showed similar size ranges, this overlap suggests that physical dimensions alone are insufficient to definitively distinguish these populations. Nonetheless, their distinct sedimentation behaviours under differential ultracentrifugation revealed functional and compositional differences, reinforcing the relevance of separation methods in EV characterization (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This statement is supported by immunoblot validation of EV subtype-specific markers, where classical sEVs markers (CD63, CD81) and lEVs-associated markers (e.g., Calnexin) showed expected patterns in both control and BDE-47-treated samples. These findings comply with MISEV 2018 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and MISEV 2024 guidelines (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and confirm the absence of significant contamination across EV fractions. However, while BDE-47 exposure did not alter EV markers of expression, our glycoprofiling data revealed notable changes in the EV surface glycan landscape upon stimulation. Surface glycans play a crucial role in mediating intercellular communication via extracellular vesicles, influencing their recognition, targeting, and uptake by recipient cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), especially in the tumor microenvironment. Carbohydrate-mediated interactions play a crucial role in modulating and controlling cell-cell behavior, particularly in host-pathogen interactions and the tumor microenvironment (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Lectins (carbohydrate-binding proteins) often serve as the receptors that regulate carbohydrate-mediated signaling, influencing the behavior of antigen-presenting cells and T-cells. Using a panel of human C-type lectins, we observed that both lEVs and sEVs bind DC-SIGN and MGL, confirming the presence of N or O glycans on the EV\u0026rsquo;s surface. Interestingly, BDE-47 induced differences in C-type lectins binding on sEVs. These changes suggest that BDE-47 is able to modify the glycosylation patterns of EVs in a subtype-specific manner, potentially influencing their immunomodulatory roles through altered interactions with glycan-binding receptors on recipient cells (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). At the molecular level, BDE-47 also reprogrammed the miRNA cargo of both sEVs and lEVs. Our miRNA profiling revealed distinct signatures in each EV subtype, with no overlap between the miRNAs differentially expressed in BDE-47-derived lEVs versus sEVs, indicating a marked rewiring of miRNA sorting under toxicant exposure. Our bioinformatic miRNA analyses significantly advanced our understanding of BDE-47 impact on EV cargo showing that the pollutant treatment substantially altered miRNA cargo, evidenced by the differential expression of 22 miRNAs unique to the EV subpopulations derived from treated cells. Remarkably, pathway enrichment analyses identified robust associations between these miRNAs and critical cellular processes such as senescence, cell cycle regulation, and oncogenic signaling pathways (NOTCH, AKT, ALK). To assess the functional relevance of these molecular findings, we performed a proliferation assay on LNCaP prostate cancer cells treated with the different EV subpopulations. Interestingly, both sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and sEVs\u003csup\u003eDMSO\u003c/sup\u003e significantly inhibited LNCaP proliferation more than their lEV counterparts as shown by means of BrdU labelling and B-Gal senescence assay. Moreover, sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e showed a stronger anti-proliferative effect compared to sEVs\u003csup\u003eDMSO\u003c/sup\u003e, reinforcing the hypothesis that BDE-47 treatment alters sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e behaviour in a manner that affects recipient cell response. These findings are consistent with our miRNA enrichment analysis and suggest that the BDE-47 altered EVs may directly impact cell cycle progression and the proliferative capacity of recipient bystander cells. This hypothesis was further supported by transcriptional analysis, which showed a significant increase in the expression of the cell cycle inhibitors p16 and p21 genes in cells treated with EVs derived from BDE-47-exposed macrophages. Both p16 and p21 are key markers of cellular senescence and act by inhibiting cyclin-dependent kinases (CDKs), leading to the inactivation of the retinoblastoma protein (Rb) and arrest of the cell cycle in the G1 phase (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). While p21 is typically activated in response to DNA damage and p53 signaling, p16 is more often linked to stress-induced or replicative senescence. Notably, in our experimental model, p21 upregulation was more pronounced in cells treated with sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e compared to controls. This trend was confirmed at the protein level by Western blot analysis, which showed increased p21 expression specifically in sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e LNCaP treated cells, consistent with the transcriptional findings. This suggests that EV-mediated intercellular communication might represent an important route through which BDE-47 exerts pleiotropic effects on downstream cells.\u003c/p\u003e\u003cp\u003eAll together, these observations raise intriguing possibilities: the altered surface glycans and miRNA cargo of BDE-47-modified sEVs may allow the biogenesis of EVs with enhanced bioactivity in modulating tumour cell behaviour. Such findings underscore the potential for environmental contaminants to impact not only immune cells perturbating their response as previously shown (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) but also influence heterologous cell dynamics via EVs. In particular, this study reveals that BDE-47 selectively affects distinct subsets of macrophage-derived extracellular vesicles, inducing alterations in their glycosylation patterns and miRNA cargo. These modifications are EV subtype-specific and are associated with altered functional outcomes in recipient cells. Together, our findings highlight the importance of dissecting EV heterogeneity to fully understand how environmental insults can shape intercellular communication networks and contribute to pathophysiological processes, including immune dysregulation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The enhanced inhibitory effects of sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e on LNCaP cell proliferation underscore the functional consequences of these molecular alterations. Indeed, given the stability and bioavailability of EVs in circulation, these vesicles may also serve as potential biomarkers for environmental exposure or as vehicles for systemic signal dissemination. Further research is needed to elucidate how BDE-47 affects the molecular machinery involved in miRNA sorting and the enzymatic pathways that regulate glycosylation, as such insights would significantly advance our understanding of toxicant-induced vesicle reprogramming. Moreover, \u003cem\u003ein vivo\u003c/em\u003e studies investigating the biodistribution, immune interactions, and functional consequences of extracellular vesicles derived from BDE-47-exposed macrophages would offer valuable translational perspectives on their potential roles in systemic toxicity and intercellular communication.\u003c/p\u003e\u003cp\u003eIn conclusion, this study demonstrates that exposure to the flame retardant BDE-47 selectively reshapes the molecular and functional landscape of EVs derived from human macrophages. While EV biogenesis remains intact, significant changes in glycosylation and miRNA cargo are observed, which translate into altered effects on recipient cells. These findings open new approaches for investigating the role of EVs as mediators of environmental toxicity and underscore their potential utility in monitoring pollutant-induced health risks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBrdU - Bromodeoxyuridine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBSA - Bovine Serum Albumine\u003c/p\u003e\n\u003cp\u003eBDE-47 - 2,4,2\u0026rsquo;,4\u0026rsquo;-tetrabromodiphenyl ether\u003c/p\u003e\n\u003cp\u003eDC SIGN - Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin\u003c/p\u003e\n\u003cp\u003eDTT - dithiothreitol\u003c/p\u003e\n\u003cp\u003eDLS - Dynamic light scattering\u003c/p\u003e\n\u003cp\u003eDMSO \u0026ndash; Dimethyl sulfoxide\u003c/p\u003e\n\u003cp\u003edUC \u0026ndash; differential ultracentrifugation\u003c/p\u003e\n\u003cp\u003eEno-1 - Enolase-1\u003c/p\u003e\n\u003cp\u003eESCRT - Endosomal Sorting Complex Required for Transport\u003c/p\u003e\n\u003cp\u003eEVs \u0026ndash; Extracellular vesicles\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFBS \u0026ndash; Fetal Bovine Serum\u003c/p\u003e\n\u003cp\u003elEVs \u0026ndash; large Extracellular vesicles\u003c/p\u003e\n\u003cp\u003eLPS - Lipopolysaccharides\u003c/p\u003e\n\u003cp\u003eMGL - Macrophage Galactose-type Lectin\u003c/p\u003e\n\u003cp\u003eMR - mannose receptor\u003c/p\u003e\n\u003cp\u003eMVEs \u0026ndash; Multivesicular endosomes\u003c/p\u003e\n\u003cp\u003eMVs \u0026ndash; Microvesicles\u003c/p\u003e\n\u003cp\u003eNTA - Nanoparticle Tracking Analysis (NTA)\u003c/p\u003e\n\u003cp\u003eOD - optical density\u003c/p\u003e\n\u003cp\u003ePBDEs \u0026ndash; Polybrominated diphenyl ether\u003c/p\u003e\n\u003cp\u003ePBS - phosphate buffered saline\u003c/p\u003e\n\u003cp\u003ePDI - polydispersity index\u003c/p\u003e\n\u003cp\u003ePMA - phorbol 12-myristate-13-acetate\u003c/p\u003e\n\u003cp\u003ePVDF - polyvinylidene fluoride\u003c/p\u003e\n\u003cp\u003eRPMI 1640 - Roswell Park Memorial Institute 1640\u003c/p\u003e\n\u003cp\u003eSDS - sodium dodecyl sulphate\u003c/p\u003e\n\u003cp\u003esEVs \u0026ndash; small Extracellular vesicles\u003c/p\u003e\n\u003cp\u003eTMB - 3,3\u0026prime;,5,5\u0026prime;- tetramethylbenzidine\u003c/p\u003e\n\u003cp\u003eTSM - tris(hydroxymethyl)aminomethane\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript was funded by the Next Generation EU, Mission 4 Component 1 PRIN: PROGETTI DI RICERCA DI RILEVANTE INTERESSE NAZIONALE \u0026ndash; Bando 2022 PNRR Prot. P2022L82YR CUP B53D23024450001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We are grateful to Mrs Antonina Azzolina and Dr. Gaetano Spinelli for their excellent technical support.\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMargolis L, Sadovsky Y. The biology of extracellular vesicles: The known unknowns. PLoS Biol. 2019;17(7):e3000363.\u003c/li\u003e\n\u003cli\u003eMiceli RT, Chen TY, Nose Y, Tichkule S, Brown B, Fullard JF, et al. Extracellular vesicles, RNA sequencing, and bioinformatic analyses: Challenges, solutions, and recommendations. J Extracell Vesicles. 2024;13(12):e70005.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Angelo G, Stahl PD, Raposo G. The cell biology of Extracellular Vesicles: A jigsaw puzzle with a myriad of pieces. Curr Opin Cell Biol. 2025;94:102519.\u003c/li\u003e\n\u003cli\u003eKumar MA, Baba SK, Sadida HQ, Marzooqi SA, Jerobin J, Altemani FH, et al. 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Placenta. 2020;89:42-9.\u003c/li\u003e\n\u003cli\u003eSoleimanifar N, Assadiasl S, Kalateh E, Hassanvand MS, Sadr M, Mojtahedi H, et al. Circulating Exosomes and Ambient Air Pollution Exposure in COPD. Chronic Obstr Pulm Dis. 2023;10(4):412-21.\u003c/li\u003e\n\u003cli\u003eDusoswa SA, Horrevorts SK, Ambrosini M, Kalay H, Paauw NJ, Nieuwland R, et al. Glycan modification of glioblastoma-derived extracellular vesicles enhances receptor-mediated targeting of dendritic cells. J Extracell Vesicles. 2019;8(1):1648995.\u003c/li\u003e\n\u003cli\u003eWilliams C, Pazos R, Royo F, Gonzalez E, Roura-Ferrer M, Martinez A, et al. Assessing the role of surface glycans of extracellular vesicles on cellular uptake. Scientific reports. 2019;9(1):11920.\u003c/li\u003e\n\u003cli\u003eBucior I, Burger MM. Carbohydrate-carbohydrate interactions in cell recognition. Curr Opin Struct Biol. 2004;14(5):631-7.\u003c/li\u003e\n\u003cli\u003eFiani ML, Barreca V, Sargiacomo M, Ferrantelli F, Manfredi F, Federico M. Exploiting Manipulated Small Extracellular Vesicles to Subvert Immunosuppression at the Tumor Microenvironment through Mannose Receptor/CD206 Targeting. Int J Mol Sci. 2020;21(17).\u003c/li\u003e\n\u003cli\u003eStein GH, Drullinger LF, Soulard A, Dulic V. Differential roles for cyclin-dependent kinase inhibitors p21 and p16 in the mechanisms of senescence and differentiation in human fibroblasts. Mol Cell Biol. 1999;19(3):2109-17.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7480117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7480117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eExtracellular vesicles (EVs) are key mediators of intercellular communication, and their molecular cargo and surface properties can be profoundly influenced by external stimuli. In the context of inflammation, immune cells increase EV release to regulate immunity and metabolism. We previously demonstrated that the widespread environmental pollutant BDE-47 modulates macrophage innate immune responses through inflammasome inhibition and EVs biogenesis. In the present study, we investigated how BDE-47 exposure influences the heterogeneity of EVs released by THP-1 M(LPS) macrophages by characterizing their physical properties and surface markers. We also assessed the impact of BDE-47 on the microRNA cargo of different EV subtypes and investigated EVs functional role in modulating senescence-associated processes in heterologous LNCaP target cells\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eEV populations were isolated from BDE-47-treated THP-1 M(LPS) macrophages by differential centrifugation, allowing the separation of small (sEVs) and large EVs (lEVs). Their size, concentration, and surface characteristics were assessed through NTA, DLS, Western blotting, and lectin-binding assays, while EV-associated miRNAs were profiled by microarray. Pathway enrichment analysis was conducted to identify key biological pathways altered due to EVs treatment. The downstream effects of different subtypes of EVs (sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e) were evaluated on LNCaP cells by BrdU incorporation and β-galactosidase senescence assays. In addition, transcriptional and Western Blot analyses were performed to investigate the expression of genes involved in cell cycle regulation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur results show that BDE-47 does not alter EV size but profoundly reshapes their molecular identity. Specifically, we observed changes in glycan surface expression and a selective modulation of miRNA sorting in both sEVs and lEVs. Bioinformatic analysis revealed a distinct BDE-47\u0026ndash;associated EV-miRNA signature linked to the regulation of cell cycle checkpoint pathways. Functional assays further demonstrated that sEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e and lEVs\u003csup\u003eBDE\u0026minus;47\u003c/sup\u003e differentially influence proliferation, induction of senescence, and the expression of p16 and p21 genes in LNCaP cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings highlight EVs as central targets and mediators of pollutant-induced cellular effects, unveiling a novel mechanism by which environmental contaminants interfere with EV-mediated communication and influence the behaviour and functions of recipient cells.\u003c/p\u003e","manuscriptTitle":"BDE-47 Alters Surface Glycans and microRNA Cargo of Macrophage-Derived Vesicle Subpopulations Modulating EV-Mediated Senescence Signaling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 11:18:19","doi":"10.21203/rs.3.rs-7480117/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e69d90ad-ffc8-475b-b171-0c7f95e29af8","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-15T19:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 11:18:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7480117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7480117","identity":"rs-7480117","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}