Parkinson’s associated protein DJ-1 regulates intercellular communication via extracellular vesicles in oxidative stress.

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Thomas Page, Clara Alice Musi, Saskia E. Bakker, David R. Jenkins, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5669239/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 Mutations in DJ-1 cause autosomal recessive Parkinson’s disease. Several functions have been attributed to DJ-1, including a key role in the protection from oxidative stress, however how this protein contributes to PD pathogenesis is still unclear. Recently, DJ-1 has been identified at higher concentration in extracellular vesicles (EV) from biological fluids of PD patients, providing a link between EV and a protein associated with PD. EV were isolated from the medium of control and rotenone-treated wild-type and DJ-1 KO differentiated SH-SY5Y cells, their number was evaluated by flow cytometry and the proteomic signature of their cargo was investigated by mass spectrometry analysis. Migration of THP-1 derived macrophages was used a read out for functional EV. The results obtained were validated in iPSC-derived neuronal cells. We identified an altered EV response to rotenone in DJ-1 KO cells compared to wild-type. Mass spectrometry analysis identified 116 proteins with significantly different concentrations between the two genotypes, suggesting a link between DJ-1 and EV cargo in response to oxidative stress. Additionally, we showed that DJ-1 KO alters the ability of EV to stimulate macrophage migration, thus implying functional consequences for DJ-1 absence in the EV mediated response to oxidative stress. The altered EV response to rotenone was confirmed in iPSC-derived neurons lacking DJ-1 compared to isogenic controls. Our results indicate a clear DJ-1 role in intercellular communication in oxidative stress, underlying a new EV mediated DJ-1 function that may be relevant to PD pathogenesis. neurodegeneration DJ-1 extracellular vesicles proteomic signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Mutations in DJ-1, encoded by the Park7 gene, cause autosomal recessive Parkinson’s disease (PD)[1]. Despite a huge number of studies to elucidate the exact role of DJ-1 in the pathogenesis of PD, the key molecular mechanisms are not yet clear. While DJ-1 is known to play a role in the protection against oxidative stress, it is also implicated in mitochondrial homeostasis, regulation of apoptosis and autophagy, dopamine synthesis and reuptake, and regulation of the immune system [2, 3]. Recently, DJ-1 has been identified at higher concentration in extracellular vesicles (EV)[4–6] from biological fluids of PD patients, providing a link between EV and a protein associated with PD. EV are small bilipid layer-enclosed vesicles, produced by a wide variety of cells and secreted into the extracellular environment, with a key role in intercellular communication. They contain a broad spectrum of proteins, lipids, and nucleic acids that are cell and context specific [7]. In the CNS, EV can be secreted by all types of brain cells [8] and play a role in synaptic function, synaptic plasticity and myelin production, neuronal development and maturation [9, 10]. Interestingly, increasing literature has reported roles of EV in the occurrence and progression of neurodegenerative disorders including PD [11, 12]. The increased presence of DJ-1 in EV derived from PD patients is intriguing for two reasons: first, exosomal DJ-1 could represent a viable PD biomarker [13] and second, it could inform new molecular mechanisms responsible for PD pathogenesis. Here, we investigated the role of DJ-1 in EV mediated inter-cellular communication and assessed the consequences of DJ-1 absence in such communication in differentiated SH-SY5Y cells upon oxidative stress. Using mass spectrometry, we identified a distinct proteomic signature in EV derived from DJ-1-deficient cells compared to those from wild-type cells. Furthermore, we demonstrated that EV from DJ-1 KO cells exposed to oxidative stress exhibit functional differences from those of wild-type cells in their impact on immune cell migration. Notably, we observed that the EV response to oxidative stress in DJ-1 knockout iPSC-derived neurons differs from that in wild-type cells, further validating the findings from our in vitro model. Methods SH-SY5Y cell culture, differentiation, and oxidative stress treatment Wild-type SH-SY5Y were purchased from ATCC, product code ATCC-CRL-2266. Park7 (DJ-1) knock-out cell line was genetically engineered via CRISPR by Synthego (Redwood City, California) with a guide sequence of CAGGACAAAUGACCACAUCA. SH-SY5Y cells were grown in DMEM/ F-12 (1:1) Glutamax medium (Gibco, UK) supplemented with 10 % v/v FBS (Gibco, UK), 100 units/ml penicillin (Gibco, UK), and 100 μg/ml streptomycin (Gibco, UK) in a 95% air/5% CO2 atmosphere. Cells were plated on laminin (Corning, UK) coated (10 ug/ml) 6 well plates (1 × 10 5 cells per well) for EV flow cytometry analysis, coverslips (1 × 10 5 cells per well) for immunofluorescence studies or 12 well plate (5 × 10 4 cells per well) for rotenone treatment. For differentiation, 48 h after plating cells were treated with 10 mM Retinoic acid (Sigma Aldrich, UK) in DMEM F-12 Glutamax supplemented with FBS and P/S for 5 days, followed by 5 days further treatment with 50 ng/ml BDNF (Peprotech 450-02) in FBS free, but otherwise identical medium, refreshed every 48h. Treatment of cells with rotenone (Sigma Aldrich, UK) was carried out from a 1000x stock solution in DMSO so that no more than 0.1 % DMSO was present in cell growth medium. Cells were treated with toxin concentrations ranging from 0-250 nM rotenone for 24 h. THP-1 monocytes Human THP-1 monocytes (ATCC; LGC Standards, Middlesex, UK; product code ATCC-TIB-202) were cultured in RPMI 1640 medium (Sigma Aldrich, UK) supplemented with 10% (v/v) FBS (Gibco, UK), 1% Penicillin–Streptomycin and 1% L-glutamine (Sigma Aldrich, UK) and incubated at 37 °C and 5% CO2. Fresh medium was added upon expansion to a cell density of 5 × 10 5 –1 × 10 6 /ml. For differentiation into macrophage-like cells, THP-1 monocytes were centrifuged at 300 x g for 5 min and resuspended in fresh complete RPMI 1640 medium at a density of 5 × 10 5 cells/ml before differentiation was stimulated with 100 nM dihydroxyvitamin D3 (VD3; Enzo Life Sciences, UK) and incubation at 37 °C for 48 h to allow for complete differentiation into macrophage-like cells. Cell viability analysis Following rotenone treatment, ReadyProbes TM Cell viability kit (Invitrogen, UK) was used according to manufacturer’s instructions to evaluate blue (total) and green (necrotic) nuclei. Incubation was for 5 min at 37°C and 5 % CO2. After staining cells were imaged on a Cytation 5 microscope (BioTek) via an automated protocol using the fluorescent channel settings for DAPI and GFP. Each well was imaged at 9 regions of interest (ROI) evenly spaced around the well centre. Autofocus with scan occurred at each new image location in the DAPI channel. Counts of nuclei stained with ReadyProbe TM blue (total) and ReadyProbe TM green (necrotic), and analysis of nuclear morphology (size and shape) was performed in FIJI (ImageJ). Necrotic nuclei were counted via the same method as total nuclei counting and morphology analysis was undertaken by using the Stardist deep learning plugin for nuclei ROI creation (https://github.com/stardist/stardist-imagej) with the following parameters: model = “versatile (fluorescent nuclei)”; probability threshold = 0.4; allowed object overlap proportion = 0.4. The predictive model employed for necrotic nuclei percentage was as follows: 𝑑𝑒𝑝𝑒𝑛𝑑𝑎𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 ~ 𝑅𝑜𝑡𝑒𝑛𝑜𝑛𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 ∗ 𝐺𝑒𝑛𝑜𝑡𝑦𝑝𝑒 + (1|𝐵𝑎𝑡𝑐ℎ) +(𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛|𝐵𝑎𝑡𝑐ℎ). For nuclei circularity and integrated density instead the model used was: 𝑑𝑒𝑝𝑒𝑛𝑑𝑎𝑛𝑡 𝑣𝑎𝑟𝑖a𝑏𝑙𝑒 ~ 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 + 𝐺𝑒𝑛𝑜𝑡𝑦𝑝𝑒 + (1|𝐵𝑎𝑡𝑐ℎ). For the purposes of statistical analysis toxin concentration was considered discrete. All mixed effects models were fitted via the REML method. Immunostaining Cells were fixed in 4% w/v paraformaldehyde in PBS for 20 min and then incubated in 1% w/v bovine serum albumin (BSA) in PBS 0.2% v/v Triton (blocking solution) for 30 min at room temperature. Cells were then incubated with 1:1000 anti-DOPA-decarboxylase (mouse monoclonal; Abcam, ab211535) or 1:400 anti-NFH antibody (mouse monoclonal; Cell Signalling, mAb #2836), or anti-b-tubulin antibody (rabbit monoclonal; Cell Signalling, #2128) in blocking solution and incubated overnight at 4 °C. After washing in PBS, cells were incubated for 2 min in 1:2000 Hoechst 33342 trihydrochloride, 10 mg/ml solution (Invitrogen), in PBS. Incubation with the secondary antibodies (Alexa-488-conjugated goat anti-mouse Thermo Fisher, A11001; or Alexa-488-conjugated goat anti-rabbit (Thermo Fisher, A11034), for DOPA-decarboxylase, NFH and β-tubulin respectively, was in blocking buffer for 1 hr at room temperature. Finally, cells were rinsed in PBS and the final wash was replaced with Ibidi immersion oil (Ibidi, 50101) before imaging. For colocalization studies on endosomes the following primary and secondary antibodies were used: 1:100 anti-Rab7 antibody (Rabbit monoclonal antibody; Cell Signalling # 9367) and Alexa-488-conjugated goat anti-rabbit (Goat monoclonal; Thermo fisher, A11034) or 1:200 anti-EEA1 antibody (Mouse monoclonal antibody, BD transduction, 610457) and Alexa-488-conjugated goat anti-mouse (Goat monoclonal; Thermo fisher, A11001). For the characterization of iPSC-derived neuronal cells the following primary antibodies were used: Ki67 (rabbit polyclonal, Abcam ab15580), SOX2 (mouse monoclonal, R&D systems MAB2018), PAX6 (rabbit polyclonal, BioLegend 901301), Nestin (mouse monoclonal, Sigma-Aldrich, MAB5326), GFAP (mouse monoclonal, Sigma Aldrich, MAB360), MAP2 (chicken polyclonal, Abcam ab5392). Following incubation with the respective secondary antibodies, slides were mounted in Mowiol (Sigma Aldrich). Imaging For SH-SY5Y differentiation brightfield and fluorescence images were then taken of 7 different fields in each well on a Biotek Cytation 5 system (Agilent) at 20 X magnification. Neurite length was measured in a b-tubulin-stained field using the simple neurite tracer plugin in ImageJ (FIJI). n=3, with 3 wells analysed each time per condition. 100 cells were analysed per well. EV detection by flow cytometry Growth medium was collected, centrifuged at 300 x g at 4 °C for 5min to remove dead cells, and the supernatant was then centrifuged again at 2000 x g for 20 at 4 °C to remove cellular debris. EV in collected supernatant were stained overnight by 5 µM Bodipy FL-SE (Invitrogen, UK) or 1h at room temperature with 40nM Memglow (Universal Biologicals, UK). The following day EV concentration and sizes were analysed by flow cytometry using a Beckman Coulter Cytoflex S. The detectors employed were FITC and violet light side scatter (SSC_1). Megamix-Plus SSC and Megamix-Plus FSC standardisation beads (1:1 mix) (BioCytex, UK) ranging from 100 nm to 900 nm diameter were employed to generate EV gates. The acquisition settings were as follows: SSC_1 threshold of 18,000 and gain of 400; FITC gain of 250. The flow rate was set to 10 µl/min and sample analysis stopped at 30,000 total fluorescent positive EV events detected. EV count was normalised to cell protein amount or cell number in each well, depending on the experiment. EV Cryo-EM and confocal microscopy Three T-75 laminin coated flasks per genotype were seeded with 800,000 WT or DJ-1 KO SH-SY5Y cells in 16 ml DMEM F-12 glutamax medium supplemented with FBS and P/S. Cells were then differentiated and EV were collected as described above. EV supernatant was harvested and concentrated to 500 µl via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 °C. EV were then purified via IZON qEV size exclusion chromatography columns according to the manufacturer’s instructions. Pure EV samples were then concentrated again via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 °C from 3 ml to 200 µl and placed on ice. 5 µL of each sample was applied to a freshly glow-discharged lacey carbon grid and plunge-frozen using a Leica GP2 plunge-freezer. Grids were imaged using a JEOL 2200 FS with a Gatan K2 camera. For confocal microscopy, concentrated pure EV samples were stained with 2.5 mM Memglow TM Green for 1 hr at RT. 10 µl of EV sample was then imaged on a Leica SP8 Falcon confocal microscope using the Alexa 488 dye assistant settings. Western blot Cells were washed twice with sterile PBS and then lysed on ice for 10 min in lysis buffer [17]. Lysates were centrifuged at 13,000 rpm for 10 min at 4 °C. Supernatants were collected and protein concentration was determined by the Bradford method. Samples were stored at − 80 °C until used. Proteins were separated on a Biorad 4–20% Mini-PROTEAN® TGX Stain-Free™ gel, (10 μg of total proteins per well) and transferred to a PVDF membrane by using a Trans-blot Turbo Transfer System (Biorad). Membranes were then blocked with Biorad EveryBlot blocking buffer TM for 5 min and incubated overnight at 4 °C with primary antibodies. Primary antibodies and dilutions were as follows: DJ-1 (rabbit poly; Novus Biologicals; 1:2000) and GAPDH (mouse mono; Santa Cruz sc-265062; 1:500 dilution). Blots were developed using horseradish peroxidase (HRP)-conjugated secondary antibodies (1:10000; Vector Laboratories) and the ECL chemiluminescence system (SuperSignal West Dura Extended Duration Substrate, Thermo Scientific). Endosome analysis Cells were plated on laminin coated IBIDI dishes (10 5 cells per dish), differentiated, and stained with 300 nM Nile red for 15 min at 37 °C at 5 % CO 2 before being imaged on a Leica SP8 Falcon Confocal microscope with environmental control box set to 37 °C and 5 % CO 2 . Nile red was imaged in living cells using the Alexa 555 dye assistant settings (n = 6 spread evenly across 3 weeks of cultures, and for each n at least 200 cells were imaged). For the evaluation of endosomes an automatic pipeline was built in FIJI (ImageJ) to analyse endosome count and size. Briefly, a top hat filter of radius 1.5 was applied to images of Nile red staining, followed by binarisation via the Find Maxima command (parameters = exclude, strict, maxima within tolerance output) with prominence set as the minimum pixel intensity determined by the default auto-threshold command. Particles of area 0-10 mm 2 and circularity > 0.5 were selected as endosomes and passed to the Analyze Particles command. Endosome count was normalized to the total stain area determined via Huang auto threshold of the auto-scaled (equivalent to brightness and contrast > auto in FIJI) raw Nile red image. For endosome count and size the model “Count ~ Genotype ∗ Toxin + (1|Genotype + Toxin|Batch)” was used, least squares means were calculated for count and area data by the emmeans R package. Pairwise contrasts were calculated and their significance determined using emmeans “contrast” function. Mitochondria morphology analysis Differentiated SH-SY5Y cells were stained with 250 nM MitoSpy™ Orange CMTMRos (BioLegend 424804) for 30 min at 37 °C then washed once with PBS. Cells were then fixed with 4 % w/v paraformaldehyde and immunostaining was performed as described before using anti-ATP5-alpha primary antibody (Mouse monoclonal; Abcam, 14748) and Alexa-488-conjugated goat anti-mouse (Goat mono; Thermo fisher, A11001). Nuclei, ATP5-alpha and Mitospy orange were visualised on a Leica SP8 confocal microscope system at 20X and 40X magnification using the DAPI, Alexa 488 and Alexa 532 dye assistant settings with laser and detector settings kept consistent for all images of each marker. 10 images were taken per culture, with n = 4 spread evenly across 2 weeks of cultures, and for each condition at least 600 cells were imaged. Mitochondrial morphology analysis was performed in FIJI (ImageJ) via an automatic analysis pipeline. Nuclei morphology was analysed using the StarDist (https://github.com/stardist/stardist-imagej) deep learning plugin for nuclei ROI creation with the following settings: modelChoice, Versatile (fluorescent nuclei); normalizeInput, true; percentileBottom, 1.0; percentileTop, 99.0; probThresh, 0.8; nmsThresh, 0.3; excludeBoundary, 2. Preprocessing was performed on all mitochondria stain images as follows: max intensity projection (Z project command); rolling ball background subtraction of radius 5; unsharp mask of radius 0.5 and mask 0.3; enhance local contrast with block size 199, histogram of 256, and maximum of 1.5; and finally median filter of radius 0.5. Fluorescence intensity was measured on a thresholded image of mitochondria with min and max pixel values of 40 and 255 and was normalised against nuclei counts. Mitochondria branch morphology was analysed in ImageJ as follows: mitochondria selective staining was selected via Otsu auto thresholding, followed by skeletonisation using Skeletonize command, and skeleton analysis via the Analyze skeleton (2D/3D) command with prune set to none. EV isolation for mass spectrometry analysis Differentiated WT and DJ-1 KO SH-SY5Y cells were grown in T-75 flasks seeded at a density of 8x 10 5 cells / flask. 3 EV samples per condition were prepared by pooling the growth media of 5 T-75 flasks per sample, spread equally across 3 weeks. EV were collected from the medium as described above, then the supernatant was concentrated to 500 ml via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 °C. EV were then purified via size exclusion chromatography columns (qEV original, Izon) according to the manufacturer’s instructions. Purified EV fractions were then concentrated via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 °C from 3 ml to ~ 100 ml and stored at -20°C prior use. Mass spectrometry Extracellular vesicle (EV) samples purified by size exclusion chromatography (SEC) were analyzed for protein concentration using the Bradford reagent. When feasible, protein equivalents of 10-30 µg were lysed in reducing Laemmli buffer (Alfa Aesar, J61337) at 65 °C for 15 minutes. The reduced samples were then loaded and separated on a 10% SDS-PAGE gel. Resolved proteins were stained overnight at 4 °C with Coomassie Brilliant Blue G-250 (0.5% w/v in 40% aqueous methanol and 10% glacial acetic acid). After destaining, each gel was divided into five uniform molecular weight bands, providing five gel sections per sample. The sections were diced and transferred into PCR-clean, low protein binding polypropylene tubes (Eppendorf, Hamburg, Germany). The gel pieces in separate tubes were fully destained with 50% acetonitrile in 50 mM ammonium bicarbonate (50% MeCN v/v in 50 mM ABC), dehydrated with pure acetonitrile, and vacuum dried for 30 minutes using a vacuum concentrator (Eppendorf, Hamburg, Germany). For in-gel proteolytic digestion, the dried gel pieces were rehydrated with 40 µL of trypsin solution (Sequencing grade, Promega, UK) in 6 mM ABC (at a 25:1 protein to trypsin ratio) at room temperature for 5 minutes and overlaid with 200 µL of 6 mM ammonium bicarbonate to prevent dehydration during overnight digestion at 37 °C on an orbital thermoshaker (700× g). Tryptic peptides were sequentially extracted from the gel using 30% and 50% acetonitrile in 50 mM ABC for 15 minutes in an ultrasonic bath, followed by full dehydration in pure acetonitrile. Peptide extracts from each sample section were pooled into a single polypropylene tube, vacuum dried, and stored at −20 °C until mass spectrometry analysis. For LC-MS analysis, the dried samples were reconstituted in 30 µL of 1% aqueous acetonitrile with 0.1% formic acid for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Peptide samples (5 µL) were injected onto a trap column (nanoEase M/Z Symmetry C18 Trap Column, 100Å, 5 µm, 180 µm × 20 mm, Waters, UK) on an nUPLC system (Acquity M Class, Waters, UK) operating in single-pump trapping mode at a flow rate of 5 µL/min using eluent B (acetonitrile in aqueous 0.1% formic acid). Peptides were separated at a flow rate of 0.5 µL/min on an analytical column (nanoEase M/Z ACQUITY UPLC BEH C18, 1.7 µm, 100 Å, 75 µm × 150 mm, Waters, UK) with the following gradient of eluent B: 0–45 minutes, 1–45% B; 45–49 minutes, 45–90% B; 49–52 minutes, 90% B; 52–67 minutes, 1% B. Peptide electrospray was formed at 2200 V using a PicoTip™ emitter (New Objective, Germany), and charged peptides were analyzed on a 5600 TripleTOF mass spectrometer (AB Sciex, Framingham, MA, USA) in information-dependent acquisition mode. The 10 most intense ions from each high-resolution MS survey scan were selected for high-sensitivity MS/MS, with a 30-second exclusion window for previously acquired peptide ions. The mass spectrometer was calibrated before acquisition to ensure high mass accuracy at both MS and MS/MS levels. Relative protein quantification was performed using Progenesis QI for proteomics software (version 4.1, Nonlinear Dynamics, Newcastle, UK). Each sample sub-group (within the same molecular weight range) was aligned on a retention time vs m / z plot, ensuring the alignment of identical peptides across different samples. Data was further in-silico normalized (based on the peptide distribution) to allow for the high-accuracy relative quantification. All sub-groups were combined using a multi-fraction setup to build up representative samples. Relative quantification included only protein-unique peptides. Merged data were exported as an .mgf file to Mascot search engine (Mascot Daemon platform, ver 2.5) which was searched against the curated SwissProt database with the following parameters: mass tolerance of 0.1 Da for MS and 0.5 Da for MS/MS spectra, a maximum of two trypsin missed-cleavages, Homo Sapiens taxonomy, and variable modifications of methionine oxidation and cysteine carbamidomethylation. Mascot searches were filtered to include peptides identified with minimum of 95% confidence including only scores with confirmed peptide identity (usually 31 or higher). Identified peptide list was exported back to Progenesis for the protein relative quantification between the samples. All keratins were treated as contamination and were excluded from the analysis. Exported protein relative quantification data tables were used for the quantitative gene ontology analysis using FunRich software tool. Bioinformatic analysis of the EV proteome Principal component analysis was carried out on EV samples using their protein quantity signatures to form the principal components via the R programming language. Kmeans clustering was carried out via the package ClusterR in the R programming language. The mini batch kmeans algorithm was employed with a kmeans ++ initialiser. Optimal centroids were decided by running kmeans algorithms with increasing cluster numbers until substantial diminishing returns in the decreasing “sum of squares” was observed. GO term enrichment and fold change compared to the Uniprot total Homo sapiens dataset and between analysed conditions was analysed via the FunRich software. EV collection for migration assay SH-SY5Y cells were seeded at a density of 8x 10 5 cells in T75 flasks, differentiated and treated with 10nM rotenone as described above. Cells were removed by centrifugation at 300 x g for 5 min at 4 °C, and the supernatant harvested. EV-containing supernatant was centrifuged at 2000 x g for 20 min at 4 °C to remove cellular debris, the supernatant harvested and next concentrated to a volume of 500 µL using 30 kDa Amicon vertical centrifugal filter column at 4°C. EV were purified from the resulting supernatant via size exclusion chromatography (qEV 70 nm columns, IZON science) according to the manufacturer’s instructions. Pure EV samples were subsequently concentrated to ~ 150 µl via a 30 kDa Amicon vertical centrifugal filter column at 4°C4. THP-1 derived macrophage migration assay THP-1 monocytes were differentiated into macrophages via 48 hour treatment with 100 nM dihydroxy vitamin D3 at a cell density of 1x10 6 cells/ml. 8x 10 4 macrophages were subsequently seeded into each porous transwell insert in 300 µl serum-free RPMI medium, with each transwell situated in a well of 24 well Corning tissue culture companion plate containing 700 µl of EV (derived from WT or DJ-1 KO differentiated SH-SY5Y in control or 10 nM rotenone treated conditions), or negative control (Serum-free RPMI). 8x10 4 THP-1 derived macrophages were also seeded directly into wells in 700 µl serum-free RPMI without transwell insert as positive controls. Vertical migration of THP-1 derived macrophages was monitored on a Cytation 5 automatic microscope system. Positive controls were used to define the focal height employed for all imaging by focussing on the macrophages directly seeded into the wells and thus residing at the migration finish focal height. Bright-field images were captured at 4 X magnification for 12 h with 4 images being captured per well every 30 min. Each set of 4 images was subsequentially stitched together. A minimum pixel intensity threshold of 3000 was applied to generate a mask, holes in masks were filled and touching objects split. Cells were defined as objects with a diameter ranging from 10 to 50 µm, and cell counts generated. For this experiment n = 3 individual cultures. Differences in the trend line intercepts and overall rate of migration were assessed via a mixed effect model of the following form: Migrated cells ~ polynomial(Time, 2nd degree) ∗ condition +(1 + polynomial(Time, 2nd degree)|culture). iPSC-derived neurons iPSC with a 1bp deletion in the PARK7 gene and its isogenic control (A18945, DJ1 WT and A18945, DJ1 KO, 2B10 clone) were kindly provided by Dr Mark Cookson, NIH. iPSCs were maintained under a feeder-free condition with Essential 8 medium (Gibco) on 10 µg/mL vitronectin (Gibco)-coated plates. Cells were fed daily with full media changes and passaged using EDTA 0.5 mM (Lonza) at 80% confluence. Neuronal Induction: After 24 hours from the plating, the medium was changed into neural induction media (NIM) composed of E6 (ThermoFisher), 2µM XAV-939 (HelloBio), 10µM SB431542 (HelloBio), 0.1µM LDN193189 (HelloBio). For cell seeding medium was supplemented with 10 µM RHO/ROCK pathway inhibitor Y-27632, and after 24 hours, this was removed. Cells were fed daily for 12 days. At around day 5, neural rosettes began to form. Neural Precursor Cells (NPCs) Differentiation : Cells were detached using Accutase (Sigma Aldrich) at 37°C for 4 minutes. The cell suspension was then centrifuged at 200g for 5 minutes and the pellet resuspended in Neural Maintenance Media (NMM) composed of Advanced DMEM: F12 (ThermoFisher), Neurobasal Media (ThermoFisher), 1% Glutamax (ThermoFisher), 0.5X N2 (ThermoFisher), 0.5X B27 (ThermoFisher) and bMercaptoethanol (1:1000, ThermoFisher). Cells were seeded at a density of 100,000 cells/cm 2 on 6-well plates or coverslips to perform immunostaining, coated with 20 µg/mL of Poly-L-Ornithine (ThermoFisher) and 10 µg/mL of laminin (Biolamina, LN511-0502). For cell seeding medium was supplemented with 10 µM Rock-Inhibitor, and after 24 hours, this was removed. Cells were then fed every 3-4 days and passaged at around 90% confluence. On day 7, FGF2 (1:10000, Qkine) was added to the medium. Neural Differentiation: At passage 2 or 3, NPCs were transferred to the final plating format. They were seeded at 285,000 cells/well on coverslips in 24-well plates coated with 20 µg/mL of Poly-L-Ornithine (ThermoFisher) and 10 µg/mL of laminin (Biolamina, LN511-0502). Cells were seeded into NMM with 10µM Rock inhibitor. The day after, the media was changed into BrainPhys, SM1 (Stem Cell Technologies), 20ng/mL BDNF (Qkine), 20ng/mL GDNF (Qkine), 2µM Compound E (Stem Cell Technologies). Every 3 days, half media change was performed. After 6 days in the presence of Compound E, this was removed, and cells were fed every 3 days with BrainPhys, SM1, 20ng/mL BDNF and 20ng/mL GDNF. At DIV12, cells were used for the experimental procedures. Rotenone treatment At DIV12 iPSC-derived neuronal cells were treated with 1mM of Rotenone in DMSO for 24 hours and then fixed as previously described for immunofluorescence analysis. EV from iPSC-derived neurons Before fixation, the media was collected to isolate EV. EV concentration and size were analysed by nano flow cytometry using a NanoFCM Nanoanalyser system. The detectors employed were green fluorescence and violet light side scatter, and the system was set up according to manufacturer’s instructions. EV count was normalised to true nuclei count in each condition assessed by total culture growth area Tilescan, capturing images using the DAPI dye assistant settings of a Leica SP8 confocal microscope of the entire culture area and quantification of nuclei in FIJI (ImageJ). Results DJ-1 KO affects EV concentration in differentiated SH-SY5Y cells To investigate DJ-1 role in intercellular communication, we first characterized the EV population produced by wild-type and DJ-1 KO differentiated SH-SY5Y cells (Supplementary Figure. 1) in control conditions. To date, studies on SH-SY5Y EV have been narrowly focussed on exosomes [14-16], identified by the standard exosome markers described in MISEV 2014 [17]. We here took a wider approach and interrogated EV based on size, considering EV below 200 nm diameter as small EV and those above 200 nm as large, and making no assumptions of biosynthetic origin as recommended in MISEV 2024 [18]. EV present in the cell medium were stained with BODIPY FL-SE and detected by flow cytometry (Fig. 1A, B). To account for variations in the number of donor cells in each condition, we normalised EV per µg of protein lysates. Our results show an increase in the number of total EV upon differentiation both in wild-type cells and DJ-1KO cells, primarily driven by changes in small EV (Fig.1D). Interestingly, DJ-1 KO cells showed higher small EV counts than wild-type cells (1.18-fold change) irrespective of differentiation state. Cryo-EM studies confirmed structural details and morphological featured of EV obtained from SH-SY5Y cells (Fig.1 C). EV response to rotenone is different in WT and DJ-1 KO differentiated SH-SY5Y Given the critical role of oxidative stress in PD pathogenesis [19] and the well-established function of DJ-1 in the cellular response to oxidative stress [20, 21] we next examined whether the EV differences observed under control conditions were maintained during oxidative stress. To this aim, we first identified the rotenone concentration that minimally affects differentiated SH-SY5Y cells by analysing parameters related to different aspects of cell death: total nuclei, necrotic nuclei percentage, nuclei circularity. An increase in circularity indicates enhanced apoptosis, as nuclear shrinkage and condensation, hallmark features of apoptosis, lead to a more uniform, circular shape [22]. No differences were found in the percentage of necrotic nuclei upon rotenone treatment for both genotypes (Suppl. Fig.2A), suggesting that all tested treatments of rotenone are not inducing primary necrosis, and that apoptotic events are primarily non-necrotic and largely early stage. Nuclear circularity slightly increased upon rotenone treatment in both WT and DJ-1 KO genotypes at 5, 10, and 50 nM rotenone compared to their respective control (Supplementary Figure 2B), indicating that the rotenone concentrations used were only causing minimal apoptosis with no further increase in DJ-1 KO cells compared to wild type cells. Interestingly, the EV response to this range of rotenone concentrations was different in WT and DJ-1 KO cells (Figure 2). As previously observed in control conditions (Figure 1), the proportion of the EV population designated as large (> 200 nm diameter) was substantially lower than the small population, so we focussed our attention on small EV (<200 nm). Our data show an increase of 2.25-fold at 5nM rotenone (p = 0.006) and 2.36-fold at 10 nM rotenone (p = 0.003) compared to control conditions in small EV from WT cells. However, in DJ-1 KO, small EV significantly increased at 10nM rotenone (3.48-fold, p < 0.0001) and 25nM rotenone (2.19-fold, p = 0.006) (Figure 2) compared to their respective control. At 5 nM rotenone, WT cells showed a significantly higher number of small EV than DJ-1 KO cells (1.52-fold). However, this trend reversed at 10 nM rotenone, where DJ-1 KO cells produced 1.56-fold more small EV compared to WT cells (Figure 2). Since treatment with 10 nM rotenone was the only concentration to produce significant differences when compared to the control in each genotype and between the genotypes, this concentration was used for further investigations. 10nM rotenone treatment results in genotype dependent changes in mitochondria morphology The results obtained on EV response to 10nM rotenone prompted us to further investigate the effect of this rotenone concentration on the morphology of mitochondria in differentiated SH-SY5Y cells. Mitochondrial stress induced by the toxin was studied by using a double readout of mitochondria healthy state: the polarised (healthy) mitochondria selective dye Mitospy orange and the immunofluorescent staining of the mitochondrial protein ATP5-alpha, known to undergo changes upon oxidative stress [23]. Confocal microscopy of both ATP5-alpha and Mitospy orange revealed strong and clear staining of mitochondrial structures in control conditions for WT and DJ-1 KO differentiated SH-SY5Y with a decrease in fluorescence observed upon rotenone treatment in both genotypes (Figure 3A). To confirm these qualitative data, we quantified mitochondria staining in both ATP5-alpha and Mitospy orange labelled cells (Figure 3B, C) and we analysed the average mitochondrial branch length upon Mitospy orange staining, as this staining was stronger than ATP5 (Figure 3D). ATP5-alpha immunofluorescence integrated density per cell decreased 2.88-fold in WT differentiated SH-SY5Y when treated with rotenone (p = 0 .038), while Mitospy integrated density per cell decreased 2.8-fold in WT (p = 0.05) and 10.6-fold (p = 0.037) in DJ-1 KO cells upon rotenone treatment (Figure 3B), thus confirming a higher effect of 10nM rotenone on mitochondria in cells lacking DJ-1. The quantification of mitochondria area/cell produced similar but not identical results, with a 2.9-fold decrease in area for ATP5-alpha in WT cells (p = 0.028), and a 9.2-fold decrease for Mitospy orange in DJ-1 KO cells (p = 0.032) (Figure 3C). Lastly, network branch analysis of Mitospy labelled mitochondria showed that rotenone treatment reduced by 1.2-fold the maximum mitochondria branch length of DJ-1 KO cells, but not wild-type cells (p = 0.0248) (Figure 3D). These results clearly showed that 10nM rotenone affects mitochondria in both genotypes, with a stronger effect on DJ-1 KO cells. 10nM rotenone treatment results in genotype dependent changes in endosome size/count per cell The EV response observed at 10nM rotenone in DJ-1 KO cells (increased number of EV) could be the results of an increased EV production or / and decreased uptake. To answer this question, we investigated the differences in the size and number of endosomes upon 10 nM rotenone treatment by live cell imaging using Nile Red (Figure 4A and B). The only significant difference in endosome count per cell was detected between DJ-1 KO cells treated with 10 nM rotenone and respective control (13% decrease, p = 0.038, Figure 4B), thus suggesting a reduced EV uptake may contribute to the increased number of EV in the medium of DJ-1 KO cells under rotenone conditions. On the other hand, the only difference in endosome area was detected between WT cells treated with rotenone and its control (9% decrease, (p = 0.006), Figure 4.B). To further confirm the endosomal nature of Nile Red positive structures immunofluorescence staining was carried out for Early Endosome Antigen 1 (EEA1) and late Ras-related protein Rab-7a (Rab7) endosome markers (Figure 4C). Our results show that Nile red colocalises with EEA1, i.e early endosomes, and to a lesser extent with the late endosomes marked by Rab7. Proteomics analysis of EV from WT and DJ-1 KO differentiated SH-SY5Y in oxidative stress reveals a specific DJ-1 dependent signature To truly understand the consequences of a change in the number of EV upon 10nM rotenone treatment, we next explored EV cargo by mass spectrometry analysis. Oxidative stress can affect EV cargo in three ways: addition or removal of specific cargo such as anti-oxidants; change in the concentration of cargo, or chemical modification of cargo (protein oxidations, post-translational modifications [24]). Analysis of the EV proteome from rotenone treated WT and DJ-1 KO cells successfully identified 574 distinct proteins. Principal components analysis (PCA) showed a clear separation of WT and DJ-1 KO EV samples, primarily along PC1 and to a lesser extent along PC2 (Supplementary Figure 3). Furthermore, out of the total 574 identified proteins, 116 or 20.2 % possessed significantly different quantities (P < 0.05), and within this group 50 were overexpressed in EV from DJ-1 KO and 66 in EV from WT (Fig 5A). Notably, cellular compartment GO enrichment analysis against the Uniprot human database supported isolation and analysis of pure EV. Indeed, the “Exosomes” cellular compartment GO term was the most common term in the total dataset along with “Cytoplasm” (55 % of proteins, Figure 5C). However, the “Exosomes” term showed a substantially higher enrichment compared to the background than cytoplasm, with a fold enrichment of 3.9 vs 1.4 (Figure 5C). Furthermore, other significantly enriched terms linked to EV were present, including “Extracellular”, “Extracellular region”, “Extracellular space”, “Plasma membrane”, and “Lysosome” with fold enrichments of 2.5, 4.8, 4.1, 1.4, and 3.5 respectively (Figure 5C). K-means clustering of the significantly different proteins (increased in either EV from WT or DJ-1KO) resulted in 5 clusters (Figure 5D): cluster 1, 1 protein absent in EV from DJ-1 KO; cluster 2, 7 proteins highly enriched in EV from DJ-1 KO; cluster 3, 43 proteins lowly enriched in EV from DJ-1 KO; cluster 4, 59 proteins lowly enriched in EV from WT; and cluster 5, 6 proteins highly enriched in EV from WT. The vast majority of EV proteins were either in cluster 3 or 4, which together represent 87.9 % of significantly different EV proteins. GO analysis of biological process for the fold change derived protein groupings showed that each cluster retained a relevant theme of interest. Cluster 1 was represented by a single protein, RUVBL2, involved in regulation of DNA transcription and repair, and histone and chromatin modification [25]. Cluster 2 highlighted the theme of blood coagulation, and a secondary theme of synaptic regulation. Cluster 3 was enriched in proteins involved in immunoregulation and protein localisation/transport; cluster 4 presented proteins playing a role in immunoregulation and synaptic vesicle regulation, and cluster 5 showed the themes of cell adhesion and differentiation. EV effects on macrophage migration are dependent on DJ-1 and rotenone-induced oxidative stress in donor SH-SY5Y cells The data shown so far indicate a clear DJ-1 effect in regulating the amount and cargo of EV present in the medium upon oxidative stress. This is only biologically relevant if linked to a different response from recipient cells to such EV. As DJ-1 has been reported to modulate the activation of several immune cells including macrophages, mast cells, and T cells [26], we next investigated the ability of EV obtained from wild-type cells or DJ-1 KO cells to promote THP-1-derived macrophage migration (Figure 6). Interestingly, when comparing the effect of EV from wild-type and DJ-1 KO cells in control condition (Figure 6A and B), we observed a stronger effect of EV from wild-type cells compared to DJ-1 KO on THP-1 derived macrophages migration (p= 0.016), consistent with a decreased efficiency of EV mediated signal in the absence of DJ-1 in donor cells despite no change in EV number between the two genotypes (Fig.2). However, when we compared the effect of EV from wild-type and DJ-1 KO cells upon rotenone treatment, a much stronger effect on THP1 migration was observed for DJ-1 KO cells than wild-type (p=0.008). These results clearly indicate DJ-1 involvement in the modulation of macrophage migration via EV. DJ-1 KO alters EV response to rotenone in iPSC-derived neuronal cells The results obtained with the SH-SY5Y cell line show a clear role of DJ-1 in intercellular communication upon oxidative stress. However, to verify their relevance in a cellular model not derived from a cancer cell line, the effect of rotenone on EV populations was also studied in iPSC-derived neuronal cells. Neuronal cells were differentiated from iPSC with a 1bp deletion in the PARK7 gene and their isogenic control, both kindly provided by Dr Mark Cookson, NIH (Figure 7A). We first identified a suitable rotenone concentration, to account for differences in cell sensitivity to oxidative stress compared to SH-SY5Y cells. To this aim, iPSC-derived neurons were treated with varying concentrations of rotenone for 24 hr and the number of necrotic cells was analysed as described for SH-SY5Y cells by using a membrane-impermeable fluorescent dye. Of the treatments tested, 1 µM rotenone appeared the most suitable both by visual observation of cultures and assessment of necrotic percentage as it had minimal effect on the death of B-10 iPSC-derived neurons (data not shown). The number of EV per iPSC-derived neuron in untreated/treated B-10 and isogenic control cultures (MAP2 positive, Sox2 negative) was assessed via flow cytometry on a NanoFCM nanoanalyser system. Our results showed that rotenone treatment increased the number of EV in the isogenic control cultures by a factor of 2.25 from 177 to 399 (p = 0.013, t test, Bonferroni corrected) (Figure 7B). This effect on the isogenic control coupled with the lack of effect in B-10 cultures resulted in 185 more EV per cell in rotenone treated control compared to B10 (p = 0.035 t test, Bonferroni corrected), thus confirming DJ-1 key role in the EV response to oxidative stress in this iPSC-derived neuronal cells. Discussion In the CNS, EV released by neurons and glial cells contribute to different processes essential to brain health including neuronal maintenance and repair, myelination, synaptic activity, and stress response [27–29]. This study highlights a role for DJ-1 in EV-mediated intercellular communication by demonstrating that neuronal EV number and relative protein cargo are different between WT and DJ-1 KO cells upon oxidative stress. While substantia nigra dopaminergic neurons vulnerability to oxidative stress [30] and DJ-1 involvement in the protection from oxidative stress are well established, the role played by EV in this context is still unclear. EV increased production may represent a protective cell mechanism against oxidative stress: via their cargo EV can stimulate pro-survival responses in recipient cells [31]. Alternatively, EV released in oxidative stress may exert a detrimental effect on recipient cells via their oxidized lipids and proteins cargo [32] though this may represent a mechanism by which a donor cell may seek “self-protection” through discard of oxidised components. Intrigued by this key EV role in the ability of cells to deal with oxidative stress, we analyzed the EV response to rotenone treatment in differentiated SH-SY5Y cells lacking DJ-1. We observed an increase in EV upon rotenone treatment in both wild type and DJ-1 KO cells, confirming the involvement of EV in the oxidative stress response. Strikingly, DJ-1 knockout cells required a higher concentration of rotenone to elicit an enhanced small EV response compared to wild-type cells (10 nM rotenone for DJ-1KO, 5 nM for WT). This aligns with the ability of cellular DJ-1 to act as an oxidative stress sensor [20] and respond to a lower level of oxidative stress in wild type cells compared to DJ-1 KO, and could explain why in the absence of DJ-1 the EV response to such low level of oxidative stress was absent. At 10nm rotenone the EV response was significantly different for each genotype compared to the respective control and between genotypes, with DJ-1 KO cells exhibiting a marked increase in detectable EV. Interestingly, a much smaller but still significant difference in EV between the two genotypes was also observed in control condition (Fig. 1 ). Thus, EV may represent a cellular mechanism for managing oxidative stress, which is already higher in DJ-1 KO cells compared to controls, even in the absence of rotenone. This is supported by our data on mitochondrial morphology and polarization state (Fig. 3 ). Since EV cargo determines the biological response, we next analysed the EV protein content using mass spectrometry. Our data show clearly that DJ-1 not only regulates the quantity of EV, but also influences the protein composition of EV under oxidative stress, indicating a DJ-1 dependent proteomic signature of EV upon oxidative stress. Significant proteins identified as different in DJ-1 KO EV compared to wild type EV were distributed among 5 clusters based on their fold change (Fig. 5 D). Notably, GO analysis of each of each cluster highlighted biological processes linked to PD. In cluster 2 (Blood coagulation/ cell adhesion) APOH (Beta-2-glycoprotein 1) was the protein with the highest difference between the two genotypes, with an increase of 26-fold in DJ-1 KO EV compared to wild type EV. APOH is a multifunctional protein that can both upregulate and downregulate the coagulation and complement systems in response to external stimuli [33], expressed by different cell types including neurons [34]. It is known that PD may also be accompanied by changes in the normal clotting of blood [35]: a blood transcriptome analysis in idiopathic and LRRK2 G2019S PD patients [36] showed complement and coagulation cascade as one of the only four common dysregulated pathway in both idiopathic and LRRK2 patients compared to controls, thus highlighting such dysregulation as a common mechanism in sporadic and familial PD cases. Sharma et al [37] also showed that a subset of genes that play an active role in blood coagulation-fibrinolysis are altered and contribute significantly to PD-associated key biological pathways. In cluster 3, lysosomal-trafficking regulator (LYST) was the protein with the highest fold change, with a 4.33-fold increase in EV from DJ-1 KO compared to wild type. LYST plays a key role in the regulation of membrane dynamics and intracellular trafficking of lysosomes [38], a process essential to maintaining proteostasis, which is disrupted in Parkinson’s disease. The theme of intracellular trafficking returns in cluster 4 as its most interesting candidate, synaptotagmin 11, reduced (0.13 fold change) in DJ-1 KO EV, is involved in regulating vesicle dynamics, including endo and exocytosis [39]. Differently from other synaptotagmin isoforms, Syt11 is not localized on synaptic vesicles but on trafficking endosomes and its function is crucial for development and synaptic plasticity [40]. GWAS studies have identified the synaptotagmin-11 (SYT11) locus as being linked to an increased risk of Parkinson’s disease (PD) [41, 42]. Lastly, TSG101 in cluster 5 (Tumour susceptibility gene 101 protein), depleted in DJ1 KO EV (fold change = 0.051) is involved in the regulation of exosome biogenesis [43]. These data indicate that dysregulated vesicle trafficking is a key mechanism disrupted in the absence of DJ-1. The narrative around RUVBL2, the only significant protein undetectable in DJ-1 KO EV and sole component of cluster 1, takes a different direction. Unlike other EV protein candidates implicated in intracellular trafficking, RUVBL2 is a highly conserved AAA + ATPase that forms a hetero–hexameric complex with the closely related protein RUVBL1 [25]. This hetero-hexameric ring main function is to provide a scaffold on which the other subunits of the INO80 nucleosome remodeller complex are assembled [44]. The INO80 complex alters transcription via regulating the chromatin structure by repositioning, sliding, ejecting, dis/assembling the nucleosome as well as exchanging histone variants [45]. Notably, a recent study by Yuan et al.[46] has shown that in yeast INO80 contributes to stress adaptation by binding to DNA and creating open chromatin regions at specific sites, facilitating efficient transcription initiation. To link the proteomic signature to a functional read out, we proved that EV from cells lacking DJ-1 in oxidative stress condition have a dramatically different effect in stimulating macrophage migration. We identified two distinct effects of EV obtained from wild type or DJ-1 KO cells under control conditions compared to rotenone treatment. Under control conditions, EV from wild-type cells showed a stronger ability to induce macrophage migration than those from DJ-1 KO cells. However, following rotenone treatment, EV from DJ-1 KO cells exhibited a significantly stronger effect. These results suggest that a low level of oxidative stress such as the one present in wild type cells treated with 10nM rotenone or in DJ-1 KO cells in control conditions (increased oxidative stress level exclusively due to DJ-1 absence) consistently reduces migration, as both EV from DJ-1 KO control and rotenone treated WT cells are less capable of inducing migration compared to EV from wild type cells. This could be easily explained by the loss of function of EV cargo molecules that are modified upon mild oxidative stress conditions. Interestingly, this is no longer observed when both DJ-1 knockout and rotenone treatment (10nM) are applied together as a much stronger effect in inducing cell migration was observed. The observed differences in migration in this case indicate that the effect cannot be attributed solely to the number of EV produced. Although treatment with 10 nM rotenone increases the EV count in both genotypes, an increase in migration is only seen when the EV originate from DJ-1 KO cells. This suggests that the cargo of the EV—rather than their quantity—is influencing the migration. Furthermore, it implies that a certain threshold of oxidative damage may be necessary to activate an alternative signalling pathway, potentially related to the absence of DJ-1, which influences the observed cellular behaviour. Macrophage migration in our experiments modelled the responsive ability of the CNS resident macrophage population, microglial cells. However, research has also shown that infiltration of monocyte-derived macrophages into the CNS can occur under certain conditions [47]. To further understand the effect of EV released from neuronal cells upon oxidative stress on the macrophage population future work is needed to clarify the phagocytic ability and polarization state of the recipient cells (pro-inflammatory versus pro-resolving). Conclusions In summary, our results identify a new role for DJ-1 in EV mediated intercellular communication: DJ-1 regulates the number, cargo and functional effects of EV released from neuronal cells upon oxidative stress. Our data underscore altered intercellular communication via EV as a core aspect of Parkinson's disease biology, with consequences not only in understanding molecular mechanisms in DJ-1 associated forms of PD but also sporadic PD forms, offering new insights into disease progression and novel therapeutic targets. Abbreviations PD: Parkinson’s disease EV: extracellular vesicles CNS: central nervous system iPSC: induced pluripotent stem cells Cryo-EM: cryogenic electron microscopy MS: mass spectrometry Bodipy FL-SE: Bodipy FL Succinimidyl Ester WT: wild type KO: knock-out ROS: Reactive oxygen species GO: Gene ontology Declarations Funding: This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) and Aston University funded Midlands Integrative Biosciences Training Partnership (MIBTP) (BB/T00746X/1). MR acknowledge ARUK Midlands Network for funding the iPSC work. AD and IM acknowledge support from the BBSRC (BB/S00324X/1 and BB/S01943X/1). The authors also acknowledge funding support from the Midlands Regional Cryo-EM Facility, hosted at the Warwick Advanced Bioimaging Research Technology Platform, for use of the JEOL 2100Plus, supported by MRC award reference MC_PC_17136. Access was funded by the Warwick Analytical Science Centre EPSRC grant code (EP/V007688/1). The Aston Institute for Membrane Excellence (AIME) is funded by UKRI’s Research England as part of their Expanding Excellence in England (E3) fund. Competing interest: The authors declare that they have no conflict of interest. Acknowledgments We acknowledge Dr. Mark Cookson for kindly providing the iPSC cells (A18945, DJ1 WT and A18945, DJ1 KO, 2B10 clone). We thank Dr Ann Vernallis for critical reading of the manuscript. Author Contributions Conceptualization was done by MR and AD. Experiments were performed by TP, CAM, SEB, DRJ, IM and MR. Data analysis was done by TP and MR. Resources were provided by MR, AD, EJH and TB. Writing of the original draft was done by MR and TP. Review and editing were done by AD, EJH, DRJ, IM and TB. Funding acquisition was done by MR and AD. Supervision was done by MR and AD. Availability of data and materials The MS datasets used and analysed during the current study are available from the corresponding author on request. Ethics approval and consent to participate : not applicable. Consent to publish : not applicable. References Bonifati V, Rizzu P, van Baren MJ, Schaap O, Breedveld GJ, Krieger E, Dekker MC, Squitieri F, Ibanez P, Joosse M, et al: Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science 2003, 299: 256-259. Skou LD, Johansen SK, Okarmus J, Meyer M: Pathogenesis of DJ-1/PARK7-Mediated Parkinson's Disease. Cells 2024, 13 . Repici M, Giorgini F: DJ-1 in Parkinson's Disease: Clinical Insights and Therapeutic Perspectives. J Clin Med 2019, 8 . Ho DH, Yi S, Seo H, Son I, Seol W: Increased DJ-1 in Urine Exosome of Korean Males with Parkinson's Disease. Biomed Res Int 2014, 2014 . Jang J, Jeong S, Lee SI, Seol W, Seo H, Son I, Ho DH: Oxidized DJ-1 Levels in Urine Samples as a Putative Biomarker for Parkinson's Disease. Parkinsons Dis-Us 2018, 2018 . Zhao ZH, Chen ZT, Zhou RL, Zhang X, Ye QY, Wang YZ: Increased DJ-1 and α-Synuclein in Plasma Neural-Derived Exosomes as Potential Markers for Parkinson's Disease. Front Aging Neurosci 2019, 10 . Xia X, Wang Y, Zheng JC: Extracellular vesicles, from the pathogenesis to the therapy of neurodegenerative diseases. Transl Neurodegener 2022, 11: 53. Budnik V, Ruiz-Canada C, Wendler F: Extracellular vesicles round off communication in the nervous system. Nat Rev Neurosci 2016, 17: 160-172. Gassama Y, Favereaux A: Emerging Roles of Extracellular Vesicles in the Central Nervous System: Physiology, Pathology, and Therapeutic Perspectives. Front Cell Neurosci 2021, 15: 626043. Lizarraga-Valderrama LR, Sheridan GK: Extracellular vesicles and intercellular communication in the central nervous system. FEBS Lett 2021, 595: 1391-1410. Shi M, Liu CQ, Cook TJ, Bullock KM, Zhao YC, Ginghina C, Li YF, Aro P, Dator R, He CM, et al: Plasma exosomal α-synuclein is likely CNS-derived and increased in Parkinson's disease. Acta Neuropathol 2014, 128: 639-650. Danzer KM, Kranich LR, Ruf WP, Cagsal-Getkin O, Winslow AR, Zhu LY, Vanderburg CR, McLean PJ: Exosomal cell-to-cell transmission of alpha synuclein oligomers. Mol Neurodegener 2012, 7 . Nila IS, Sumsuzzman DM, Khan ZA, Jung JH, Kazema AS, Kim SJ, Hong Y: Identification of exosomal biomarkers and its optimal isolation and detection method for the diagnosis of Parkinson's disease: A systematic review and meta-analysis. Ageing Res Rev 2022, 82: 101764. Zhou T, Zhang M, Xie Y, Chen Y, Peng S, Jing X, Lei M, Tao E, Liang Y: Effects of miRNAs in exosomes derived from α-synuclein overexpressing SH-SY5Y cells on autophagy and inflammation of microglia. Cellular Signalling 2022, 89 . Yuyama K, Sun H, Mikami D, Mioka T, Mukai K, Igarashi Y: Lysosomal‐associated transmembrane protein 4B regulates ceramide‐induced exosome release. The FASEB Journal 2020, 34: 16022-16033. Tortolici F, Vumbaca S, Incocciati B, Dayal R, Aquilano K, Giovanetti A, Rufini S: Ionizing Radiation-Induced Extracellular Vesicle Release Promotes AKT-Associated Survival Response in SH-SY5Y Neuroblastoma Cells. Cells 2021, 10 . Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin‐Smith GK, et al: Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. Journal of Extracellular Vesicles 2018, 7 . Welsh JA, Goberdhan DCI, O'Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai HJ, Di Vizio D, Driedonks TAP, Erdbrügger U, et al: Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. Journal of Extracellular Vesicles 2024, 13 . Dias V, Junn E, Mouradian MM: The role of oxidative stress in Parkinson's disease. J Parkinsons Dis 2013, 3: 461-491. Wilson MA: The role of cysteine oxidation in DJ-1 function and dysfunction. Antioxid Redox Signal 2011, 15: 111-122. Ariga H, Takahashi-Niki K, Kato I, Maita H, Niki T, Iguchi-Ariga SM: Neuroprotective function of DJ-1 in Parkinson's disease. Oxid Med Cell Longev 2013, 2013: 683920. Doonan F, Cotter TG: Morphological assessment of apoptosis. Methods 2008, 44: 200-204. Ebanks B, Chakrabarti L: Mitochondrial ATP Synthase is a Target of Oxidative Stress in Neurodegenerative Diseases. Front Mol Biosci 2022, 9 . Chiaradia E, Tancini B, Emiliani C, Delo F, Pellegrino RM, Tognoloni A, Urbanelli L, Buratta S: Extracellular Vesicles under Oxidative Stress Conditions: Biological Properties and Physiological Roles. Cells 2021, 10 . Dauden MI, López-Perrote A, Llorca O: RUVBL1-RUVBL2 AAA-ATPase: a versatile scaffold for multiple complexes and functions. Curr Opin Struc Biol 2021, 67: 78-85. Zhang L, Wang J, Wang J, Yang B, He Q, Weng Q: Role of DJ-1 in Immune and Inflammatory Diseases. Frontiers in Immunology 2020, 11 . Antonucci F, Turola E, Riganti L, Caleo M, Gabrielli M, Perrotta C, Novellino L, Clementi E, Giussani P, Viani P, et al: Microvesicles released from microglia stimulate synaptic activity via enhanced sphingolipid metabolism. The EMBO Journal 2012, 31: 1231-1240. Frühbeis C, Kuo-Elsner WP, Müller C, Barth K, Peris L, Tenzer S, Möbius W, Werner HB, Nave KA, Fröhlich D, Krämer-Albers EM: Oligodendrocytes support axonal transport and maintenance via exosome secretion. Plos Biol 2020, 18 . Lino MM, Simoes S, Tomatis F, Albino I, Barrera A, Vivien D, Sobrino T, Ferreira L: Engineered extracellular vesicles as brain therapeutics. J Control Release 2021, 338: 472-485. Surmeier DJ, Guzman JN, Sanchez-Padilla J, Schumacker PT: The role of calcium and mitochondrial oxidant stress in the loss of substantia nigra pars compacta dopaminergic neurons in Parkinson's disease. Neuroscience 2011, 198: 221-231. Borras C, Mas-Bargues C, Sanz-Ros J, Roman-Dominguez A, Gimeno-Mallench L, Ingles M, Gambini J, Vina J: Extracellular vesicles and redox modulation in aging. Free Radic Biol Med 2020, 149: 44-50. Benedikter BJ, Weseler AR, Wouters EFM, Savelkoul PHM, Rohde GGU, Stassen FRM: Redox-dependent thiol modifications: implications for the release of extracellular vesicles. Cell Mol Life Sci 2018, 75: 2321-2337. McDonnell T, Wincup C, Buchholz I, Pericleous C, Giles I, Ripoll V, Cohen H, Delcea M, Rahman A: The role of beta-2-glycoprotein I in health and disease associating structure with function: More than just APS. Blood Rev 2020, 39 . Caronti B, Calderaro C, Alessandri C, Conti F, Tinghino R, Palladini G, Valesini G: Beta2-glycoprotein I (beta2-GPI) mRNA is expressed by several cell types involved in anti-phospholipid syndrome-related tissue damage. Clin Exp Immunol 1999, 115: 214-219. Rosenbaum H, Aharon-Peretz J, Brenner B: Hypercoagulability, parkinsonism, and Gaucher disease. Semin Thromb Hemost 2013, 39: 928-934. Infante J, Prieto C, Sierra M, Sánchez-Juan P, González-Aramburu I, Sánchez-Quintana C, Berciano J, Combarros O, Sainz J: Comparative blood transcriptome analysis in idiopathic and G2019S-associated Parkinson's disease. Neurobiol Aging 2016, 38 . Sharma A, Muller J, Schuetze K, Rolfes V, Bissinger R, Rosero N, Ahmad A, Franklin BS, Zur B, Frohlich H, et al: Comprehensive Profiling of Blood Coagulation and Fibrinolysis Marker Reveals Elevated Plasmin-Antiplasmin Complexes in Parkinson's Disease. Biology (Basel) 2021, 10 . Turner ME, Che J, Mirhaidari GJM, Kennedy CC, Blum KM, Rajesh S, Zbinden JC, Breuer CK, Best CA, Barker JC: The lysosomal trafficking regulator "LYST": an 80-year traffic jam. Front Immunol 2024, 15: 1404846. Wolfes AC, Dean C: The diversity of synaptotagmin isoforms. Curr Opin Neurobiol 2020, 63: 198-209. Shimojo M, Madara J, Pankow S, Liu XR, Yates J, Südhof TC, Maximov A: Synaptotagmin-11 mediates a vesicle trafficking pathway that is essential for development and synaptic plasticity. Gene Dev 2019, 33: 365-376. International Parkinson Disease Genomics C, Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simon-Sanchez J, Schulte C, Lesage S, et al: Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet 2011, 377: 641-649. Sesar A, Cacheiro P, López-López M, Camiña-Tato M, Quintáns B, Monroy-Jaramillo N, Alonso-Vilatela ME, Cebrián E, Yescas-Gómez P, Ares B, et al: Synaptotagmin XI in Parkinson's disease: New evidence from an association study in Spain and Mexico. J Neurol Sci 2016, 362: 321-325. Gurung S, Perocheau D, Touramanidou L, Baruteau J: The exosome journey: from biogenesis to uptake and intracellular signalling. Cell Commun Signal 2021, 19: 47. Poli J, Gasser SM, Papamichos-Chronakis M: The INO80 remodeller in transcription, replication and repair. Philos Trans R Soc Lond B Biol Sci 2017, 372 . Jiang D, Li T, Guo C, Tang TS, Liu H: Small molecule modulators of chromatin remodeling: from neurodevelopment to neurodegeneration. Cell Biosci 2023, 13: 10. Yuan B, Wang WB, Wang XQ, Liu CG, Hasunuma T, Kondo A, Zhao XQ: The chromatin remodeler Ino80 regulates yeast stress tolerance and cell metabolism through modulating nitrogen catabolite repression. Int J Biol Macromol 2024, 258 . Perry VH, Teeling J: Microglia and macrophages of the central nervous system: the contribution of microglia priming and systemic inflammation to chronic neurodegeneration. Semin Immunopathol 2013, 35: 601-612. Supplementary Files Pageetal.Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5669239","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398815156,"identity":"5d89833e-6ef0-4356-a8a3-3ef12f00524b","order_by":0,"name":"Thomas Page","email":"","orcid":"","institution":"Aston University School of Life and Health Sciences: Aston University College of Health and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Page","suffix":""},{"id":398815157,"identity":"611214a0-726d-4f93-b786-dff3f10c1a74","order_by":1,"name":"Clara Alice Musi","email":"","orcid":"","institution":"Mario Negri Institute for Pharmacological Research Branch of Milan: Istituto di Ricerche Farmacologiche Mario Negri","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Alice","lastName":"Musi","suffix":""},{"id":398815158,"identity":"2df0248c-581a-4dfc-9c72-e625dce6782c","order_by":2,"name":"Saskia E. Bakker","email":"","orcid":"","institution":"University of Warwick - Coventry Campus: University of Warwick","correspondingAuthor":false,"prefix":"","firstName":"Saskia","middleName":"E.","lastName":"Bakker","suffix":""},{"id":398815159,"identity":"1daa9572-4219-4f62-bebb-16d86a082420","order_by":3,"name":"David R. Jenkins","email":"","orcid":"","institution":"Aston University School of Life and Health Sciences: Aston University College of Health and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"R.","lastName":"Jenkins","suffix":""},{"id":398815160,"identity":"548b7f10-b1b9-44d7-bfd2-05fe254df2da","order_by":4,"name":"Eric J. Hill","email":"","orcid":"","institution":"Loughborough University","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"J.","lastName":"Hill","suffix":""},{"id":398815161,"identity":"a21700cb-546b-44c7-8871-5f82201b7e50","order_by":5,"name":"Tiziana Borsello","email":"","orcid":"","institution":"University of Milan Department of Pharmacological Sciences: Universita degli Studi di Milano Dipartimento di Scienze Farmacologiche e Biomolecolari","correspondingAuthor":false,"prefix":"","firstName":"Tiziana","middleName":"","lastName":"Borsello","suffix":""},{"id":398815162,"identity":"316d5bad-c100-47e7-b5e1-0e32b0109f7d","order_by":6,"name":"Ivana Milic","email":"","orcid":"","institution":"Aston University School of Life and Health Sciences: Aston University College of Health and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ivana","middleName":"","lastName":"Milic","suffix":""},{"id":398815163,"identity":"60ba5ea1-75e8-4901-97c6-1425e4ba010f","order_by":7,"name":"Andrew Devitt","email":"","orcid":"","institution":"Aston University School of Life and Health Sciences: Aston University College of Health and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Devitt","suffix":""},{"id":398815164,"identity":"ee3f8b2d-d809-460b-8154-f825ed4ca8a8","order_by":8,"name":"Mariaelena Repici","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYNACAwYefiQuM3FaJBsYGBtI0ALSdYBYLboNvA8fFxTYyRgfP/78wccdDPL8DTzGBvi0mB1gNzaeYZDMY3Ymx7Bx5hkGwxkHeIwT8GthY5PmMTjAY3Ygh7GZt42BcQMDj/EBAlrYf4O0GPc/fwjSYk+MFjZmkBYDiQRDkJZEkBb8DjvMxiwN8ovEjTeGM2e2SSTPOMxWjN/7x9sYPxf8sbPn709/8OFjm41tf3vzZgl8WkBxgBwNEsRFJJGRPQpGwSgYBSMWAAAg9D487L18swAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9420-528X","institution":"Aston University School of Life and Health Sciences: Aston University College of Health and Life Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mariaelena","middleName":"","lastName":"Repici","suffix":""}],"badges":[],"createdAt":"2024-12-18 12:04:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5669239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5669239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73517069,"identity":"794ac696-19b3-4bac-8661-451ba4472488","added_by":"auto","created_at":"2025-01-10 17:51:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":782295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoth differentiation and DJ-1 KO increase the amount of EV in SH-SY5Y cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Flow cytometry gating strategy (Beckman Coulter CytoFLEX S flow cytometer). Left dot plot: 1:1 mix of Megamix-Plus SSC and Megamix-Plus FSC beads (BioCytex, UK) ranging from 100 nm to 900 nm diameter were used to determine subsequent size gating. Two distinct populations (small particles, \u0026lt; 200nm and large particles, \u0026gt; 200 nm) are defined and thus gates can be set based on the properties of beads of known size. Right dot plot: this gating identified small and large EV present in the medium of differentiated SH-SY5Y cells.\u003c/p\u003e\n\u003cp\u003eB. Comparison of the flow cytometry data obtained for standard sized green fluorescent beads, unstained EV and Bodipy-FL stained EV, showing how correct gating of EV was achieved. Y axis = violet light side scatter. X axis = Bodipy fluorescence. EV gate is indicated by red bordered rectangle. Local density of points represented by colour with blue = lowest and red = highest density. All data were generated on Beckman Coulter CytoFLEX S flow cytometer. Fluorescence data were transformed using a biexponential function.\u003c/p\u003e\n\u003cp\u003eC. Upper panels: Cryo-EM images of the variety of EV shapes obtained from differentiated SH-SY5Y cells. Scale bar = 100nm. Lower panel:Confocal microscopy images of EV isolated from differentiated SH-SY5Y cells stained with MemglowTM 488. Left image: full image at native resolution, right image: zoomed in selection of the left image. Clear independent fluorescent objects were observed within the size range expected of EV (\u0026lt; 1 µm) with little to no background noise. Scale bar = 10µm.\u003c/p\u003e\n\u003cp\u003eD. Number of EV per µg of protein detected by flow cytometry on Beckman Coulter CytoFLEX S flow cytometer. EV counts are shown as a total (right panel), small (\u0026lt; 200 nm diameter, middle panel), large (\u0026gt; 200 nm diameter, left panel). Columns represent least square means calculated using emmeans R package, from data modelled as a linear mixed effects model of the form: Response variable ~ Genotype*Differentiation state+(1│Batch) using lme R package. n= 9. Differentiation increased the number of EV present in the medium (2.9-fold-change in wild-type cells, p \u0026lt;0.0001; 1.96 fold-change in DJ-1KO cells, p \u0026lt;0.0001, Tukey HSD for multiple comparisons). When comparing differentiated cells only, an increase of EV in DJ-1 KO cells compared to wild-type was observed (1.18-fold change, p= 0.0083, Tukey HSD for multiple comparisons). Error bars: standard error of the emmeans least square mean calculation. (* = p\u0026lt;0.05, ** = p \u0026lt; 0.005, *** = p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/97cf01dc2da798886dd895fa.png"},{"id":73517811,"identity":"dcbaebe8-0674-4ac1-ba31-c51d5c1ce105","added_by":"auto","created_at":"2025-01-10 17:59:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRotenone treatment results in a genotype dependent increase in small EV in differentiated SH-SY5Y cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmount of small EV per µl of media detected by flow cytometry from differentiated WT and DJ-1 KO SH-SY5Y cells with or without rotenone treatment. EV were detected on Cytoflex S system, EV size was assessed by violet-light side scatter. EV events were separated from noise by the fluorescence of Bodipy-FL-SE staining. Analyses of small EV number were carried out using the emmeans package in R on a mixed effect model of the form: EV number ~ Rotenone Concentration ∗ Genotype + (1|batch) using lme R package. n=9. Column height = least square mean calculated by the emmeans R package. EV from WT cells were significantly different from control at treatments of 5 (p = 0.006) and 10 (p = 0.003) nM rotenone, increasing by factors of 2.25 and 2.36 respectively. However, in DJ-1 KO, small EV significantly increased at treatments of 10 (p \u0026lt; 0.0001) and 25 (p = 0.006) nM rotenone by factors of 3.48 and 2.19 respectively.\u003c/p\u003e\n\u003cp\u003eFurthermore at 5 nM rotenone significantly higher numbers of small EV were detected in WT cells, 1.52 times higher than the amount detected in DJ-1 KO EV (p = 0.05), whereas this effect is\u003c/p\u003e\n\u003cp\u003eflipped at 10 nM rotenone treatment with 1.56 7mes more small EV being detected from DJ-1 KO cells compared to WT. Error bars = standard error. * p \u0026lt; 0.05; ** p \u0026lt;0.01, and *** p \u0026lt; 0.001. Tukey HSD for multiple comparisons.\u003c/p\u003e","description":"","filename":"Figure2Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/80812b4dabc532b11c6de3ea.png"},{"id":73517810,"identity":"fa44fbf4-b463-487d-b66e-d2c5b79f0a17","added_by":"auto","created_at":"2025-01-10 17:59:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1474320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRotenone treatment causes genotype dependent changes in mitochondria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emorphology.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Confocal microscopy of ATP5 alpha and Mitospy orange labelled mitochondria in differentiated WT and DJ-1 KO SH-SY5Y with or without rotenone treatment captured on a Leica SP8 confocal microscope. Scale bar = 6µm.\u003c/p\u003e\n\u003cp\u003eQuantification of mitochondria morphology as integrated density (B), mitochondria area (C) and average mitochondrial branch length (D). Significant differences were determined via the emmeans R package, employing Dunnet’s test.\u003c/p\u003e\n\u003cp\u003eATP5-alpha immunofluorescence integrated density per cell decreased 2.88-fold in WT differentiated SH-SY5Y when treated with rotenone (p = 0.038). Mitospy orange integrated density per cell decreased 2.8-fold in WT (p = 0.05) and 10.6-fold in DJ-1 KO (p = 0.037) cells upon rotenone treatment. Upon rotenone treatment, mitochondria area/cell quantification showed a 2.9-fold for ATP5-alpha in WT cells (p = 0.0284), and a 9.2-fold decrease for Mitospy orange in DJ-1 KO cells (p = 0.0032). Rotenone treatment reduced the maximum mitochondria branch length (1.2 fold) of DJ-1 KO cells but not wild-type cells (p = 0.0248). n=4.\u003c/p\u003e","description":"","filename":"Figure3Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/a84423fe79b6c6c04e3ca49e.png"},{"id":73517071,"identity":"90f4bb18-30e6-4f35-91ac-54be6ed2f589","added_by":"auto","created_at":"2025-01-10 17:51:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":617854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenotype dependent changes in endosome size/ count per cell were observed upon rotenone treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Sequence of images showing the processing and analysis pipeline applied to raw images\u003c/p\u003e\n\u003cp\u003eof Nile red staining to generate quantitative data. BF =bright-field; Nile red = raw Nile red staining; Pre-processed = image after application of Top Hat filter; Maxima = output of Find Maxima FIJI command; Selected particles = particles selected and analysed via Analyze ParEcles FIJI command. Scale bar = 50µm.\u003c/p\u003e\n\u003cp\u003eB. Normalised endosome count and endosome area Columns = least squares mean. Error bars = standard error. n = 6. *: p value \u0026lt; 0.05. 13% decrease in endosome count was observed between DJ-1 KO cells treated with 10 nM rotenone and respective control (p = 0.038). The only difference in endosome area was detected between WT cells treated with rotenone and its control (9% decrease, (p = 0.006). For endosome count and size the model “Count ~ Genotype ∗ Toxin + (1|Genotype + Toxin|Batch)” was used, least squares means were calculated for count and area data by the emmeans R package. Pairwise contrasts were calculated via the emmeans “contrast” function using the Tukey HSD.\u003c/p\u003e\n\u003cp\u003eC. Nile red colocalizes with early endosome marker EEA1 and late endosome marker Rab7. Scale bar=10µm.\u003c/p\u003e","description":"","filename":"Figure4Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/76cc03842e7c25f082f39d20.png"},{"id":73517072,"identity":"281d7092-3eb8-4109-9453-cb49b79827eb","added_by":"auto","created_at":"2025-01-10 17:51:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":714358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMass spectrometry analysis reveals a DJ-1 dependent proteomic EV signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of protein fold changes in DJ-1 KO EV compared to WT EV upon rotenone treatment; log2 fold change +0.0001 (+ 0.0001 removes errors for proteins with quanEty of 0 in DJ-1 KO) on x axis and -log10 p value on y axis; red line = p value threshold, proteins above line have p value \u0026lt; 0.05, thus are significantly different.\u003c/p\u003e\n\u003cp\u003eB. Heatmap of differentially expressed proteins by unsupervised hierarchical clustering. Each column represents an individual EV sample, each row represents all the differentially expressed proteins. p value of less than 0.05. Samples and proteins are clustered based on euclidean distance.\u003c/p\u003e\n\u003cp\u003eC. Fold enrichment of cellular component terms in the total identified protein set. Y axis =percentage of genes (% of proteins in the total protein set with the specific associated term). Colour gradient = fold enrichment compared to Uniprot human protein dataset.\u003c/p\u003e\n\u003cp\u003eD. Kmeans clustering of significantly different proteins by grouping on fold change.\u003c/p\u003e\n\u003cp\u003eSignificantly different proteins clustered by mini-batch kmeans algorithm with kmeans++ initializer; clusters on y axis and log2 fold change on x axis; each point = 1 protein.\u003c/p\u003e\n\u003cp\u003eE. Top 5 proteins based on fold change for each Kmeans defined cluster, ranked by log2 (fold change +0.001) for each kmeans defined protein cluster. Proteins are represented by their Accessions on the y axis and log2 (fold change + 0.001) on the x axis. Colours according to cluster colours from clustering graph.\u003c/p\u003e","description":"","filename":"Figure5Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/0e1f84f89b8ae0c435140c30.png"},{"id":73517070,"identity":"00128904-cfd8-4da8-9c4f-417b0f04ba9e","added_by":"auto","created_at":"2025-01-10 17:51:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePromotion of macrophage migration by EV is dependent on DJ-1 and rotenone-induced oxidative stress in EV source cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.\u003cstrong\u003e \u003c/strong\u003ePlots of THP-1-derived macrophage migration promoted by EV from WT and DJ-1 KO differentiated SH-SY5Y cells in control conditions and upon rotenone-induced oxidative stress. Top panel: EV from healthy control cells, middle panel: negative control, bottom panel: EV from rotenone treated cells n=3. Differences in the trend line intercepts and overall rate of migration were assessed via a mixed effect model of the following form: Migrated cells ~ polynomial (Time, 2nd degree) ∗ condition +(1 + polynomial(Time, 2nd degree)|culture). Tukey HSD was used for multiple comparisons.\u003c/p\u003e\n\u003cp\u003eB. LOESS smoothing of migrated cell counts over time for each condition excluding positive control; error indicated by grey region surrounding lines.\u003c/p\u003e","description":"","filename":"Figure6Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/591cfa47aa6511d66d934fc5.png"},{"id":73517813,"identity":"cc75e15e-d050-4124-8481-d1494e8c4b4b","added_by":"auto","created_at":"2025-01-10 17:59:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":460904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRotenone treatment of iPSC-derived neurons reveals genotype dependent differences in the EV response.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Representative image of iPSC-derived neurons 12DIV captured using a Leica SP8 confocal microscope; with fluorescence labelling of nuclei (Hoechst 33342, blue), canonical neuronal marker MAP2 (yellow) and pluripotency marker Sox2 (red). Scale bar= 10µm.\u003c/p\u003e\n\u003cp\u003eB. Number of EV per cell detected on a NanoFCM nanoanalyser system. P =\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eC. Representative DJ-1 immunoblot in lysates of neuronal precursors from isogenic control and B10 cells used for this experiment (10µg of protein per lane).\u003c/p\u003e","description":"","filename":"Figure7Pageetal.png","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/4fc5a3aa7ee7e7e306000a28.png"},{"id":75130661,"identity":"abd40b86-5739-4900-bd97-4ec2aff72d6f","added_by":"auto","created_at":"2025-01-30 23:29:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7611540,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/3cbe9017-54bb-48b9-b68c-a9f8441188cf.pdf"},{"id":73517827,"identity":"435e8823-d890-45ab-8587-06d95190d167","added_by":"auto","created_at":"2025-01-10 17:59:03","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":8837717,"visible":true,"origin":"","legend":"","description":"","filename":"Pageetal.Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5669239/v1/6ea989549a4d47a8c97b7e94.docx"}],"financialInterests":"","formattedTitle":"Parkinson’s associated protein DJ-1 regulates intercellular communication via extracellular vesicles in oxidative stress.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMutations in DJ-1, encoded by the \u003cem\u003ePark7\u003c/em\u003e gene, cause autosomal recessive Parkinson\u0026rsquo;s disease (PD)[1]. Despite a huge number of studies to elucidate the exact role of DJ-1 in the pathogenesis of PD, the key molecular mechanisms are not yet clear. While DJ-1 is known to play a role in the protection against oxidative stress, it is also implicated in mitochondrial homeostasis, regulation of apoptosis and autophagy, dopamine synthesis and reuptake, and regulation of the immune system [2, 3]. Recently, DJ-1 has been identified at higher concentration in extracellular vesicles (EV)[4\u0026ndash;6] from biological fluids of PD patients, providing a link between EV and a protein associated with PD.\u003c/p\u003e \u003cp\u003eEV are small bilipid layer-enclosed vesicles, produced by a wide variety of cells and secreted into the extracellular environment, with a key role in intercellular communication. They contain a broad spectrum of proteins, lipids, and nucleic acids that are cell and context specific [7]. In the CNS, EV can be secreted by all types of brain cells [8] and play a role in synaptic function, synaptic plasticity and myelin production, neuronal development and maturation [9, 10]. Interestingly, increasing literature has reported roles of EV in the occurrence and progression of neurodegenerative disorders including PD [11, 12]. The increased presence of DJ-1 in EV derived from PD patients is intriguing for two reasons: first, exosomal DJ-1 could represent a viable PD biomarker [13] and second, it could inform new molecular mechanisms responsible for PD pathogenesis.\u003c/p\u003e \u003cp\u003eHere, we investigated the role of DJ-1 in EV mediated inter-cellular communication and assessed the consequences of DJ-1 absence in such communication in differentiated SH-SY5Y cells upon oxidative stress. Using mass spectrometry, we identified a distinct proteomic signature in EV derived from DJ-1-deficient cells compared to those from wild-type cells. Furthermore, we demonstrated that EV from DJ-1 KO cells exposed to oxidative stress exhibit functional differences from those of wild-type cells in their impact on immune cell migration. Notably, we observed that the EV response to oxidative stress in DJ-1 knockout iPSC-derived neurons differs from that in wild-type cells, further validating the findings from our in vitro model.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSH-SY5Y cell culture, differentiation, and oxidative stress treatment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWild-type SH-SY5Y were purchased from ATCC, product code ATCC-CRL-2266. \u003cem\u003ePark7\u0026nbsp;\u003c/em\u003e(DJ-1) knock-out cell line was genetically engineered via CRISPR by Synthego (Redwood City, California) with a guide sequence of CAGGACAAAUGACCACAUCA. SH-SY5Y cells were grown in DMEM/ F-12 (1:1) Glutamax\u003csup\u003e\u0026nbsp;\u0026nbsp;\u003c/sup\u003emedium (Gibco, UK) supplemented with 10 % v/v FBS (Gibco, UK), 100 units/ml penicillin (Gibco, UK), and 100 \u0026mu;g/ml streptomycin (Gibco, UK) in a 95% air/5% CO2 atmosphere. Cells were plated on laminin (Corning, UK) coated (10 ug/ml) 6 well plates (1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003ecells per well) for EV flow cytometry analysis, coverslips\u0026nbsp;(1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e5\u0026nbsp;\u003c/sup\u003ecells per well) for immunofluorescence studies or 12 well plate (5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e4\u003c/sup\u003e cells per well) for rotenone treatment. For differentiation, 48 h after plating cells were treated with 10 mM Retinoic acid (Sigma Aldrich, UK) in DMEM F-12 Glutamax supplemented with\u003csub\u003e\u0026nbsp;\u003c/sub\u003eFBS and P/S for 5 days, followed by 5 days further treatment with 50 ng/ml BDNF (Peprotech 450-02) in FBS free, but otherwise identical medium, refreshed every 48h. Treatment of cells with rotenone (Sigma Aldrich, UK) was carried out from a 1000x stock solution in DMSO so that no more than 0.1 % DMSO was present in cell growth medium.\u0026nbsp;Cells were treated with toxin concentrations ranging from 0-250 nM rotenone for 24 h.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTHP-1 monocytes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman THP-1 monocytes (ATCC; LGC Standards, Middlesex, UK; product code ATCC-TIB-202) were cultured in RPMI 1640 medium (Sigma Aldrich, UK) supplemented with 10% (v/v) FBS (Gibco, UK), 1% Penicillin\u0026ndash;Streptomycin and 1% L-glutamine (Sigma Aldrich, UK) and incubated at 37 \u0026deg;C and 5% CO2. Fresh medium was added upon expansion to a cell density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u0026ndash;1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e/ml. For differentiation into macrophage-like cells, THP-1 monocytes were centrifuged at 300 x g for 5 min and resuspended in fresh complete RPMI 1640 medium at a density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003ecells/ml before differentiation was stimulated with 100 nM dihydroxyvitamin D3 (VD3; Enzo Life Sciences, UK) and incubation at 37 \u0026deg;C for 48 h to allow for complete differentiation into macrophage-like cells.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCell viability analysis\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing rotenone treatment, ReadyProbes\u003csup\u003eTM\u003c/sup\u003e Cell viability kit (Invitrogen, UK) was used according to manufacturer\u0026rsquo;s instructions to evaluate blue (total) and green (necrotic) nuclei. Incubation was for 5 min at 37\u0026deg;C\u0026nbsp;and 5 % CO2. After staining cells were imaged on a Cytation 5 microscope (BioTek) via an automated protocol using the fluorescent channel settings for DAPI and GFP. Each well was imaged at 9 regions of interest (ROI) evenly spaced around the well centre. Autofocus with scan occurred at each new image location in the DAPI channel. Counts of nuclei stained with ReadyProbe\u003csup\u003eTM\u003c/sup\u003e blue (total) and ReadyProbe\u003csup\u003eTM\u003c/sup\u003e green (necrotic), and analysis of nuclear morphology (size and shape) was performed in FIJI (ImageJ). Necrotic nuclei were counted via the same method as total nuclei counting and morphology analysis was undertaken by using the Stardist deep learning plugin for nuclei ROI creation (https://github.com/stardist/stardist-imagej) with the following parameters: model = \u0026ldquo;versatile (fluorescent nuclei)\u0026rdquo;; probability threshold = 0.4; allowed object overlap proportion = 0.4. The predictive model employed for necrotic nuclei percentage was as follows: 𝑑𝑒𝑝𝑒𝑛𝑑𝑎𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 ~ 𝑅𝑜𝑡𝑒𝑛𝑜𝑛𝑒 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 \u0026lowast; 𝐺𝑒𝑛𝑜𝑡𝑦𝑝𝑒 + (1|𝐵𝑎𝑡𝑐ℎ) +(𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛|𝐵𝑎𝑡𝑐ℎ). For nuclei circularity and integrated density instead the model used was:\u003c/p\u003e\n\u003cp\u003e𝑑𝑒𝑝𝑒𝑛𝑑𝑎𝑛𝑡\u0026nbsp;𝑣𝑎𝑟𝑖a𝑏𝑙𝑒\u0026nbsp;~\u0026nbsp;𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛\u0026nbsp;+\u0026nbsp;𝐺𝑒𝑛𝑜𝑡𝑦𝑝𝑒\u0026nbsp;+ (1|𝐵𝑎𝑡𝑐ℎ). For the purposes of statistical\u003c/p\u003e\n\u003cp\u003eanalysis toxin concentration was considered discrete. All mixed effects models were fitted via the\u003c/p\u003e\n\u003cp\u003eREML method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImmunostaining\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were fixed in 4% w/v paraformaldehyde in PBS for 20 min and then incubated in 1% w/v bovine serum albumin (BSA) in PBS 0.2% v/v Triton (blocking solution) for 30 min at room temperature. Cells were then incubated with 1:1000 anti-DOPA-decarboxylase (mouse monoclonal; Abcam, ab211535)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eor 1:400 anti-NFH antibody (mouse monoclonal; Cell Signalling, mAb #2836), or anti-b-tubulin antibody (rabbit monoclonal; Cell Signalling, #2128) in blocking solution and incubated overnight at 4 \u0026deg;C. After washing in PBS, cells were incubated for 2 min in 1:2000 Hoechst 33342 trihydrochloride, 10 mg/ml solution (Invitrogen), in PBS. Incubation with the secondary antibodies (Alexa-488-conjugated goat anti-mouse Thermo Fisher, A11001; or Alexa-488-conjugated goat anti-rabbit (Thermo Fisher, A11034), for DOPA-decarboxylase, NFH and\u0026nbsp;\u0026beta;-tubulin respectively, was in blocking buffer for 1 hr at room temperature. Finally, cells were rinsed in PBS and the final wash was replaced with Ibidi immersion oil (Ibidi, 50101) before imaging.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor colocalization studies on endosomes the following primary and secondary antibodies were used: 1:100 anti-Rab7 antibody (Rabbit monoclonal antibody; Cell Signalling # 9367) and Alexa-488-conjugated goat anti-rabbit (Goat monoclonal; Thermo fisher, A11034) or 1:200 anti-EEA1 antibody (Mouse monoclonal antibody, BD transduction, 610457) and Alexa-488-conjugated goat anti-mouse (Goat monoclonal; Thermo fisher, A11001). For the characterization of iPSC-derived neuronal cells the following primary antibodies were used: Ki67 (rabbit polyclonal, Abcam ab15580), SOX2 (mouse monoclonal, R\u0026amp;D systems MAB2018), PAX6 (rabbit polyclonal, BioLegend 901301), Nestin (mouse monoclonal, Sigma-Aldrich, MAB5326), GFAP (mouse monoclonal, Sigma Aldrich, MAB360), MAP2 (chicken polyclonal, Abcam ab5392). Following incubation with the respective secondary antibodies, slides were mounted in Mowiol (Sigma Aldrich).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImaging\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor SH-SY5Y differentiation brightfield and fluorescence images were then taken of 7 different fields in each well on a Biotek Cytation 5 system (Agilent) at 20 X magnification. Neurite length was measured in a\u0026nbsp;b-tubulin-stained field using the simple neurite tracer plugin in ImageJ (FIJI). n=3, with 3 wells analysed each time per condition. \u0026nbsp;100\u0026nbsp;cells were analysed per well.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV detection by flow cytometry\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrowth medium was collected, centrifuged at 300 x g at 4\u0026nbsp;\u0026deg;C for 5min to remove dead cells, and the supernatant was then centrifuged again at 2000 x g for 20 at 4\u0026nbsp;\u0026deg;C\u0026nbsp;to remove cellular debris. EV in collected supernatant were stained overnight by 5 \u0026micro;M Bodipy FL-SE (Invitrogen, UK) or 1h at room temperature with 40nM Memglow (Universal Biologicals, UK). The following day EV concentration and sizes were analysed by flow cytometry using a Beckman Coulter Cytoflex S. The detectors employed were FITC and violet light side scatter (SSC_1). Megamix-Plus SSC and Megamix-Plus FSC standardisation beads (1:1 mix) (BioCytex, UK) ranging from 100 nm to 900 nm diameter were employed to generate EV gates. The acquisition settings were as follows: SSC_1 threshold of 18,000 and gain of 400; FITC gain of 250. The flow rate was set to 10 \u0026micro;l/min and sample analysis stopped at 30,000 total fluorescent positive EV events detected.\u0026nbsp;EV count was normalised to cell protein amount or cell number in each well, depending on the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV Cryo-EM and confocal microscopy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree T-75 laminin coated flasks per genotype were seeded with 800,000 WT or DJ-1 KO SH-SY5Y cells in 16 ml DMEM F-12 glutamax \u003csup\u003e\u0026nbsp;\u003c/sup\u003emedium supplemented with FBS and P/S. Cells were then differentiated and EV were collected as described above. EV supernatant was harvested and concentrated to 500\u0026nbsp;\u0026micro;l via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 \u0026deg;C. EV were then purified via IZON qEV size exclusion chromatography columns according to the manufacturer\u0026rsquo;s instructions. Pure EV samples were then concentrated again via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4 \u0026deg;C from 3 ml to 200\u0026nbsp;\u0026micro;l and placed on ice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5 \u0026micro;L of each sample was applied to a freshly glow-discharged lacey carbon grid and plunge-frozen using a Leica GP2 plunge-freezer. Grids were imaged using a JEOL 2200 FS with a Gatan K2 camera.\u003c/p\u003e\n\u003cp\u003eFor confocal microscopy, concentrated pure EV samples were stained with 2.5 mM Memglow\u003csup\u003eTM\u003c/sup\u003e Green for 1 hr at RT. 10 \u0026micro;l of EV sample was then imaged on a Leica SP8 Falcon confocal microscope using the Alexa 488 dye assistant settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWestern blot\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were washed twice with sterile PBS and then lysed on ice for 10 min in lysis buffer [17]. Lysates were centrifuged at 13,000 rpm for 10 min at 4 \u0026deg;C. Supernatants were collected and protein concentration was determined by the Bradford method. Samples were stored at \u0026minus;\u0026thinsp;80 \u0026deg;C until used. Proteins were separated on a Biorad 4\u0026ndash;20% Mini-PROTEAN\u0026reg; TGX Stain-Free\u0026trade; gel, (10 \u0026mu;g of total proteins per well) and transferred to a PVDF membrane by using a Trans-blot Turbo Transfer System (Biorad). Membranes were then blocked with Biorad EveryBlot blocking buffer\u003csup\u003eTM\u003c/sup\u003e for 5 min and incubated overnight at 4 \u0026deg;C with primary antibodies.\u003c/p\u003e\n\u003cp\u003ePrimary antibodies and dilutions were as follows: DJ-1 (rabbit poly; Novus Biologicals; 1:2000) and \u0026nbsp;GAPDH (mouse mono; Santa Cruz sc-265062; 1:500 dilution). Blots were developed using horseradish peroxidase (HRP)-conjugated secondary antibodies (1:10000; Vector Laboratories) and the ECL chemiluminescence system (SuperSignal West Dura Extended Duration Substrate, Thermo Scientific).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEndosome analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were plated on laminin coated IBIDI dishes (10\u003csup\u003e5\u003c/sup\u003e cells per dish), differentiated, and stained with 300 nM Nile red for 15 min at 37 \u0026deg;C at 5 % CO\u003csub\u003e2\u003c/sub\u003e before being imaged on a Leica SP8 Falcon Confocal microscope with environmental control box set to 37 \u0026deg;C and 5 % CO\u003csub\u003e2\u003c/sub\u003e. Nile red was imaged in living cells using the Alexa 555 dye assistant settings (n = 6 spread evenly across 3 weeks of cultures, and for each n at least 200 cells were imaged).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the evaluation of endosomes an automatic pipeline was built in FIJI (ImageJ) to analyse endosome count and size. Briefly, a top hat filter of radius 1.5 was applied to images of Nile red staining, followed by binarisation via the Find Maxima command (parameters = exclude, strict, maxima within tolerance output) with prominence set as the minimum pixel intensity determined by the default auto-threshold command. Particles of area 0-10\u0026nbsp;mm\u003csup\u003e2\u003c/sup\u003e and circularity \u0026gt; 0.5 were selected as endosomes and passed to the Analyze Particles command. Endosome count was normalized to the total stain area determined via Huang auto threshold of the auto-scaled (equivalent to brightness and contrast \u0026gt; auto in FIJI) raw Nile red image.\u0026nbsp;For endosome count and size the model \u0026ldquo;Count ~ Genotype\u0026nbsp;\u0026lowast;\u0026nbsp;Toxin + (1|Genotype + Toxin|Batch)\u0026rdquo; was used, least squares means were calculated for count and area data by the emmeans R package. Pairwise contrasts were calculated and their significance determined using emmeans \u0026ldquo;contrast\u0026rdquo; function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMitochondria morphology analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentiated SH-SY5Y cells were stained with 250 nM MitoSpy\u0026trade; Orange CMTMRos (BioLegend 424804) for 30 min at 37 \u0026deg;C then washed once with PBS. Cells were then fixed with 4 % w/v paraformaldehyde and immunostaining was performed as described before using anti-ATP5-alpha primary antibody (Mouse monoclonal; Abcam, 14748) and Alexa-488-conjugated goat anti-mouse (Goat mono; Thermo fisher, A11001). Nuclei, ATP5-alpha and Mitospy orange were visualised on a Leica SP8 confocal microscope system at 20X and 40X magnification using the DAPI, Alexa 488 and Alexa 532 dye assistant settings with laser and detector settings kept consistent for all images of each marker. 10 images were taken per culture, with n = 4 spread evenly across 2 weeks of cultures, and for each condition at least 600 cells were imaged. Mitochondrial morphology analysis was performed in FIJI (ImageJ) via an automatic analysis pipeline. Nuclei morphology was analysed using the StarDist (https://github.com/stardist/stardist-imagej) deep learning plugin for nuclei ROI creation with the following settings: modelChoice, Versatile (fluorescent nuclei); normalizeInput, true; percentileBottom, 1.0; percentileTop, 99.0; probThresh, 0.8; nmsThresh, 0.3; excludeBoundary, 2. Preprocessing was performed on all mitochondria stain images as follows: max intensity projection (Z project command); rolling ball background subtraction of radius 5; unsharp mask of radius 0.5 and mask 0.3; enhance local contrast with block size 199, histogram of 256, and maximum of 1.5; and finally median filter of radius 0.5. Fluorescence intensity was measured on a thresholded image of mitochondria with min and max pixel values of 40 and 255 and was normalised against nuclei counts.\u003c/p\u003e\n\u003cp\u003eMitochondria branch morphology was analysed in ImageJ as follows: mitochondria selective staining was selected via Otsu auto thresholding, followed by skeletonisation using Skeletonize command, and skeleton analysis via the Analyze skeleton (2D/3D) command with prune set to none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV isolation for mass spectrometry analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentiated WT and DJ-1 KO SH-SY5Y cells were grown in T-75 flasks seeded at a density of 8x 10\u003csup\u003e5\u003c/sup\u003e cells / flask. 3 EV samples per condition were prepared by pooling the growth media of 5 T-75 flasks per sample, spread equally across 3 weeks. EV were collected from the medium as described above, then the supernatant was concentrated to 500\u0026nbsp;ml via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4\u0026nbsp;\u0026deg;C. EV were then purified via size exclusion chromatography columns (qEV original, Izon) according to the manufacturer\u0026rsquo;s instructions. Purified EV fractions were then concentrated via centrifugation in Amicon 30 K centrifugal filter units at 3260 x g at 4\u0026nbsp;\u0026deg;C from 3 ml to ~ 100\u0026nbsp;ml and stored at -20\u0026deg;C prior use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMass spectrometry\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtracellular vesicle (EV) samples purified by size exclusion chromatography (SEC) were analyzed for protein concentration using the Bradford reagent. When feasible, protein equivalents of 10-30 \u0026micro;g were lysed in reducing Laemmli buffer (Alfa Aesar, J61337) at 65 \u0026deg;C for 15 minutes. The reduced samples were then loaded and separated on a 10% SDS-PAGE gel. Resolved proteins were stained overnight at 4 \u0026deg;C with Coomassie Brilliant Blue G-250 (0.5% w/v in 40% aqueous methanol and 10% glacial acetic acid). After destaining, each gel was divided into five uniform molecular weight bands, providing five gel sections per sample. The sections were diced and transferred into PCR-clean, low protein binding polypropylene tubes (Eppendorf, Hamburg, Germany). The gel pieces in separate tubes were fully destained with 50% acetonitrile in 50 mM ammonium bicarbonate (50% MeCN v/v in 50 mM ABC), dehydrated with pure acetonitrile, and vacuum dried for 30 minutes using a vacuum concentrator (Eppendorf, Hamburg, Germany). For in-gel proteolytic digestion, the dried gel pieces were rehydrated with 40 \u0026micro;L of trypsin solution (Sequencing grade, Promega, UK) in 6 mM ABC (at a 25:1 protein to trypsin ratio) at room temperature for 5 minutes and overlaid with 200 \u0026micro;L of 6 mM ammonium bicarbonate to prevent dehydration during overnight digestion at 37 \u0026deg;C on an orbital thermoshaker (700\u0026times; g). Tryptic peptides were sequentially extracted from the gel using 30% and 50% acetonitrile in 50 mM ABC for 15 minutes in an ultrasonic bath, followed by full dehydration in pure acetonitrile. Peptide extracts from each sample section were pooled into a single polypropylene tube, vacuum dried, and stored at \u0026minus;20 \u0026deg;C until mass spectrometry analysis.\u003c/p\u003e\n\u003cp\u003eFor LC-MS analysis, the dried samples were reconstituted in 30 \u0026micro;L of 1% aqueous acetonitrile with 0.1% formic acid for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Peptide samples (5 \u0026micro;L) were injected onto a trap column (nanoEase M/Z Symmetry C18 Trap Column, 100\u0026Aring;, 5 \u0026micro;m, 180 \u0026micro;m \u0026times; 20 mm, Waters, UK) on an nUPLC system (Acquity M Class, Waters, UK) operating in single-pump trapping mode at a flow rate of 5 \u0026micro;L/min using eluent B (acetonitrile in aqueous 0.1% formic acid). Peptides were separated at a flow rate of 0.5 \u0026micro;L/min on an analytical column (nanoEase M/Z ACQUITY UPLC BEH C18, 1.7 \u0026micro;m, 100 \u0026Aring;, 75 \u0026micro;m \u0026times; 150 mm, Waters, UK) with the following gradient of eluent B: 0\u0026ndash;45 minutes, 1\u0026ndash;45% B; 45\u0026ndash;49 minutes, 45\u0026ndash;90% B; 49\u0026ndash;52 minutes, 90% B; 52\u0026ndash;67 minutes, 1% B. Peptide electrospray was formed at 2200 V using a PicoTip\u0026trade; emitter (New Objective, Germany), and charged peptides were analyzed on a 5600 TripleTOF mass spectrometer (AB Sciex, Framingham, MA, USA) in information-dependent acquisition mode. The 10 most intense ions from each high-resolution MS survey scan were selected for high-sensitivity MS/MS, with a 30-second exclusion window for previously acquired peptide ions. The mass spectrometer was calibrated before acquisition to ensure high mass accuracy at both MS and MS/MS levels.\u003c/p\u003e\n\u003cp\u003eRelative protein quantification was performed using Progenesis QI for proteomics software (version 4.1, Nonlinear Dynamics, Newcastle, UK). Each sample sub-group (within the same molecular weight range) was aligned on a retention time vs \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e plot, ensuring the alignment of identical peptides across different samples. Data was further in-silico normalized (based on the peptide distribution) to allow for the high-accuracy relative quantification. All sub-groups were combined using a multi-fraction setup to build up representative samples. Relative quantification included only protein-unique peptides. Merged data were exported as an .mgf file to Mascot search engine (Mascot Daemon platform, ver 2.5) which was searched against the curated SwissProt database with the following parameters: mass tolerance of 0.1 Da for MS and 0.5 Da for MS/MS spectra, a maximum of two trypsin missed-cleavages, \u003cem\u003eHomo Sapiens\u003c/em\u003e taxonomy, and variable modifications of methionine oxidation and cysteine carbamidomethylation. Mascot searches were filtered to include peptides identified with minimum of 95% confidence including only scores with confirmed peptide identity (usually 31 or higher). Identified peptide list was exported back to Progenesis for the protein relative quantification between the samples. All keratins were treated as contamination and were excluded from the analysis. Exported protein relative quantification data tables were used for the quantitative gene ontology analysis using FunRich software tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBioinformatic analysis of the EV proteome\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis was carried out on EV samples using their protein quantity signatures to form the principal components via the R programming language. Kmeans clustering was carried out via the package ClusterR in the R programming language. The mini batch kmeans algorithm was employed with a kmeans ++ initialiser. Optimal centroids were decided by running kmeans algorithms with increasing cluster numbers until substantial diminishing returns in the decreasing \u0026ldquo;sum of squares\u0026rdquo; was observed. GO term enrichment and fold change compared to the Uniprot total \u003cem\u003eHomo sapiens\u003c/em\u003e dataset and between analysed conditions was analysed via the FunRich software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV collection for migration assay\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH-SY5Y cells were seeded at a density of 8x 10\u003csup\u003e5\u003c/sup\u003e cells in T75 flasks, differentiated and treated with 10nM rotenone as described above. Cells were removed by centrifugation at 300 x g for 5 min at 4 \u0026deg;C, and the supernatant harvested. EV-containing supernatant was centrifuged at 2000 x g for 20 min at 4\u0026nbsp;\u0026deg;C\u0026nbsp;to remove cellular debris, the supernatant harvested and next concentrated to a volume of 500\u0026nbsp;\u0026micro;L using 30 kDa Amicon vertical centrifugal filter column at 4\u0026deg;C. EV were purified from the resulting supernatant via size exclusion chromatography (qEV 70 nm columns, IZON science) according to the manufacturer\u0026rsquo;s instructions. Pure EV samples were subsequently concentrated to ~ 150\u0026nbsp;\u0026micro;l via a 30 kDa Amicon vertical centrifugal filter column at 4\u0026deg;C4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTHP-1 derived macrophage migration assay\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTHP-1 monocytes were differentiated into macrophages via 48 hour treatment with 100 nM dihydroxy vitamin D3 at a cell density of 1x10\u003csup\u003e6\u003c/sup\u003e cells/ml. 8x 10\u003csup\u003e4\u003c/sup\u003e macrophages were subsequently seeded into each porous transwell insert in 300\u0026nbsp;\u0026micro;l serum-free RPMI medium, with each transwell situated in a well of 24 well Corning tissue culture companion plate containing 700\u0026nbsp;\u0026micro;l of EV (derived from WT or DJ-1 KO differentiated SH-SY5Y in control or 10 nM rotenone treated conditions), or negative control (Serum-free RPMI). 8x10\u003csup\u003e4\u003c/sup\u003e THP-1 derived macrophages were also seeded directly into wells in 700\u0026nbsp;\u0026micro;l serum-free RPMI without transwell insert as positive controls. Vertical migration of THP-1 derived macrophages was monitored on a Cytation 5 automatic microscope system. \u0026nbsp;Positive controls were used to define the focal height employed for all imaging by focussing on the macrophages directly seeded into the wells and thus residing at the migration finish focal height. Bright-field images were captured at 4 X magnification for 12 h with 4 images being captured per well every 30 min. \u0026nbsp;Each set of 4 images was subsequentially stitched together. A minimum pixel intensity threshold of 3000 was applied to generate a mask, holes in masks were filled and touching objects split. Cells were defined as objects with a diameter ranging from 10 to 50\u0026nbsp;\u0026micro;m, and cell counts generated. \u0026nbsp; For this experiment n = 3 individual cultures. Differences in the trend line intercepts and overall rate of migration were assessed via a mixed effect model of the following form: Migrated cells ~ polynomial(Time, 2nd degree)\u0026nbsp;\u0026lowast; condition +(1 + polynomial(Time, 2nd degree)|culture).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eiPSC-derived neurons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eiPSC with a 1bp deletion in the \u003cem\u003ePARK7\u003c/em\u003e gene and its isogenic control (A18945, DJ1 WT and A18945, DJ1 KO, 2B10 clone)\u0026nbsp;were kindly provided by Dr Mark Cookson, NIH. iPSCs were maintained under a feeder-free condition with Essential 8 medium (Gibco) on 10 \u0026micro;g/mL vitronectin (Gibco)-coated plates. Cells were fed daily with full media changes and passaged using EDTA 0.5 mM (Lonza) at 80% confluence.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNeuronal Induction:\u0026nbsp;\u003c/em\u003eAfter 24 hours from the plating, the medium was changed into neural induction media (NIM) composed of E6 (ThermoFisher), 2\u0026micro;M XAV-939 (HelloBio), 10\u0026micro;M SB431542 (HelloBio), 0.1\u0026micro;M LDN193189 (HelloBio). For cell seeding medium was supplemented with 10 \u0026micro;M RHO/ROCK pathway inhibitor Y-27632, and after 24 hours, this was removed. Cells were fed daily for 12 days. At around day 5, neural rosettes began to form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNeural Precursor Cells (NPCs) Differentiation\u003c/em\u003e: Cells were detached using Accutase (Sigma Aldrich) at 37\u0026deg;C for 4 minutes. The cell suspension was then centrifuged at 200g for 5 minutes and the pellet resuspended in Neural Maintenance Media (NMM) composed of Advanced DMEM: F12 (ThermoFisher), Neurobasal Media (ThermoFisher), 1% Glutamax (ThermoFisher), 0.5X N2 (ThermoFisher), 0.5X B27 (ThermoFisher) and\u0026nbsp;bMercaptoethanol (1:1000, ThermoFisher). Cells were seeded at a density of\u0026nbsp;100,000 cells/cm\u003csup\u003e2\u003c/sup\u003e on 6-well plates or coverslips to perform immunostaining, coated with 20 \u0026micro;g/mL of Poly-L-Ornithine (ThermoFisher) and 10 \u0026micro;g/mL of laminin (Biolamina, LN511-0502). For cell seeding medium was supplemented with 10 \u0026micro;M Rock-Inhibitor, and after 24 hours, this was removed. Cells were then fed every 3-4 days and passaged at around 90% confluence. On day 7, FGF2 (1:10000, Qkine) was added to the medium.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNeural Differentiation:\u003c/em\u003e At passage 2 or 3, NPCs were transferred to the final plating format. They were seeded at 285,000 cells/well on coverslips in 24-well plates coated with 20 \u0026micro;g/mL of Poly-L-Ornithine (ThermoFisher) and 10 \u0026micro;g/mL of laminin (Biolamina, LN511-0502). Cells were seeded into NMM with 10\u0026micro;M Rock inhibitor. The day after, the media was changed into BrainPhys, SM1 (Stem Cell Technologies), 20ng/mL BDNF (Qkine), 20ng/mL GDNF (Qkine), 2\u0026micro;M Compound E (Stem Cell Technologies). Every 3 days, half media change was performed. After 6 days in the presence of Compound E, this was removed, and cells were fed every 3 days with BrainPhys, SM1, 20ng/mL BDNF and 20ng/mL GDNF. At DIV12, cells were used for the experimental procedures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRotenone treatment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt DIV12 iPSC-derived neuronal cells were treated with 1mM of Rotenone in DMSO for 24 hours and then fixed as previously described for immunofluorescence analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV from iPSC-derived neurons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore fixation, the media was collected to isolate EV. EV concentration and size were analysed by nano flow cytometry using a NanoFCM Nanoanalyser system. The detectors employed were green fluorescence and violet light side scatter, and the system was set up according to manufacturer\u0026rsquo;s instructions. EV count was normalised to true nuclei count in each condition assessed by total culture growth area Tilescan, capturing images using the DAPI dye assistant settings of a Leica SP8 confocal microscope of the entire culture area and quantification of nuclei in FIJI (ImageJ).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDJ-1 KO affects EV concentration in differentiated SH-SY5Y cells\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate DJ-1 role in intercellular communication, we first characterized the EV population produced by wild-type and DJ-1 KO differentiated SH-SY5Y cells (Supplementary Figure. 1) in control conditions. To date, studies on SH-SY5Y EV have been narrowly focussed on exosomes [14-16], identified by the standard exosome markers described in MISEV 2014 [17]. We here took a wider approach and interrogated EV based on size, considering EV below 200 nm diameter as small EV and those above 200 nm as large, and making no assumptions of biosynthetic origin as recommended in MISEV 2024 [18]. EV present in the cell medium were stained with BODIPY FL-SE and detected by flow cytometry (Fig. 1A, B). To account for variations in the number of donor cells in each condition, we normalised EV per \u0026micro;g of protein lysates. Our results show an increase in the number of total EV upon differentiation both in wild-type cells and DJ-1KO cells, primarily driven by changes in small EV (Fig.1D). Interestingly, DJ-1 KO cells showed higher small EV counts than wild-type cells (1.18-fold change) irrespective of differentiation state. Cryo-EM studies confirmed structural details and morphological featured of EV obtained from SH-SY5Y cells (Fig.1 C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV response to rotenone is different in WT and DJ-1 KO differentiated SH-SY5Y\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the critical role of oxidative stress in PD pathogenesis [19] and the well-established function of DJ-1 in the cellular response to oxidative stress [20, 21] we next examined whether the EV differences observed under control conditions were maintained during oxidative stress. To this aim, we first identified the rotenone concentration that minimally affects differentiated SH-SY5Y cells by analysing parameters related to different aspects of cell death: total nuclei, necrotic nuclei percentage, nuclei circularity. An increase in circularity indicates enhanced apoptosis, as nuclear shrinkage and condensation, hallmark features of apoptosis, lead to a more uniform, circular shape [22]. No differences were found in the percentage of necrotic nuclei upon rotenone treatment for both genotypes (Suppl. Fig.2A), suggesting that all tested treatments of rotenone are not inducing primary necrosis, and that apoptotic events are primarily non-necrotic and largely early stage. Nuclear circularity slightly increased upon rotenone treatment in both WT and DJ-1 KO genotypes at 5, 10, and 50 nM rotenone compared to their respective control (Supplementary Figure 2B), indicating that the rotenone concentrations used were only causing minimal apoptosis with no further increase in DJ-1 KO cells compared to wild type cells. \u003c/p\u003e\n\u003cp\u003eInterestingly, the EV response to this range of rotenone concentrations was different in WT and DJ-1 KO cells (Figure 2). As previously observed in control conditions (Figure 1), the proportion of the EV population designated as large (\u0026gt; 200 nm diameter) was substantially lower than the small population, so we focussed our attention on small EV (\u0026lt;200 nm). Our data show an increase of 2.25-fold at 5nM rotenone (p = 0.006) and 2.36-fold at 10 nM rotenone (p = 0.003) compared to control conditions in small EV from WT cells. However, in DJ-1 KO, small EV significantly increased at 10nM rotenone (3.48-fold, p \u0026lt; 0.0001) and 25nM rotenone (2.19-fold, p = 0.006) (Figure 2) compared to their respective control. At 5 nM rotenone, WT cells showed a significantly higher number of small EV than DJ-1 KO cells (1.52-fold). However, this trend reversed at 10 nM rotenone, where DJ-1 KO cells produced 1.56-fold more small EV compared to WT cells (Figure 2). Since treatment with 10 nM rotenone was the only concentration to produce significant differences when compared to the control in each genotype and between the genotypes, this concentration was used for further investigations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e10nM rotenone treatment results in genotype dependent changes in mitochondria morphology\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results obtained on EV response to 10nM rotenone prompted us to further investigate the effect of this rotenone concentration on the morphology of mitochondria in differentiated SH-SY5Y cells. Mitochondrial stress induced by the toxin was studied by using a double readout of mitochondria healthy state: the polarised (healthy) mitochondria selective dye Mitospy orange and the immunofluorescent staining of the mitochondrial protein ATP5-alpha, known to undergo changes upon oxidative stress [23]. Confocal microscopy of both ATP5-alpha and Mitospy orange revealed strong and clear staining of mitochondrial structures in control conditions for WT and DJ-1 KO differentiated SH-SY5Y with a decrease in fluorescence observed upon rotenone treatment in both genotypes (Figure 3A). To confirm these qualitative data, we quantified mitochondria staining in both ATP5-alpha and Mitospy orange labelled cells (Figure 3B, C) and we analysed the average mitochondrial branch length upon Mitospy orange staining, as this staining was stronger than ATP5 (Figure 3D). ATP5-alpha immunofluorescence integrated density per cell decreased 2.88-fold in WT differentiated SH-SY5Y when treated with rotenone (p = 0 .038), while Mitospy integrated density per cell decreased 2.8-fold in WT (p = 0.05) and 10.6-fold (p = 0.037) in DJ-1 KO cells upon rotenone treatment (Figure 3B), thus confirming a higher effect of 10nM rotenone on mitochondria in cells lacking DJ-1. The quantification of mitochondria area/cell produced similar but not identical results, with a 2.9-fold decrease in area for ATP5-alpha in WT cells (p = 0.028), and a 9.2-fold decrease for Mitospy orange in DJ-1 KO cells (p = 0.032) (Figure 3C). Lastly, network branch analysis of Mitospy labelled mitochondria showed that rotenone treatment reduced by 1.2-fold the maximum mitochondria branch length of DJ-1 KO cells, but not wild-type cells (p = 0.0248) (Figure 3D). These results clearly showed that 10nM rotenone affects mitochondria in both genotypes, with a stronger effect on DJ-1 KO cells. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e10nM rotenone treatment results in genotype dependent changes in endosome size/count per cell\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EV response observed at 10nM rotenone in DJ-1 KO cells (increased number of EV) could be the results of an increased EV production or / and decreased uptake. To answer this question, we investigated the differences in the size and number of endosomes upon 10 nM rotenone treatment by live cell imaging using Nile Red (Figure 4A and B). The only significant difference in endosome count per cell was detected between DJ-1 KO cells treated with 10 nM rotenone and respective control (13% decrease, p = 0.038, Figure 4B), thus suggesting a reduced EV uptake may contribute to the increased number of EV in the medium of DJ-1 KO cells under rotenone conditions. On the other hand, the only difference in endosome area was detected between WT cells treated with rotenone and its control (9% decrease, (p = 0.006), Figure 4.B). To further confirm the endosomal nature of Nile Red positive structures immunofluorescence staining was carried out for Early Endosome Antigen 1 (EEA1) and late Ras-related protein Rab-7a (Rab7) endosome markers (Figure 4C). Our results show that Nile red colocalises with EEA1, i.e early endosomes, and to a lesser extent with the late endosomes marked by Rab7. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProteomics analysis of EV from WT and DJ-1 KO differentiated SH-SY5Y in oxidative stress reveals a specific DJ-1 dependent signature\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo truly understand the consequences of a change in the number of EV upon 10nM rotenone treatment, we next explored EV cargo by mass spectrometry analysis. Oxidative stress can affect EV cargo in three ways: addition or removal of specific cargo such as anti-oxidants; change in the concentration of cargo, or chemical modification of cargo (protein oxidations, post-translational modifications [24]). Analysis of the EV proteome from rotenone treated WT and DJ-1 KO cells successfully identified 574 distinct proteins. Principal components analysis (PCA) showed a clear separation of WT and DJ-1 KO EV samples, primarily along PC1 and to a lesser extent along PC2 (Supplementary Figure 3). Furthermore, out of the total 574 identified proteins, 116 or 20.2 % possessed significantly different quantities (P \u0026lt; 0.05), and within this group 50 were overexpressed in EV from DJ-1 KO and 66 in EV from WT (Fig 5A). Notably, cellular compartment GO enrichment analysis against the Uniprot human database supported isolation and analysis of pure EV. Indeed, the \u0026ldquo;Exosomes\u0026rdquo; cellular compartment GO term was the most common term in the total dataset along with \u0026ldquo;Cytoplasm\u0026rdquo; (55 % of proteins, Figure 5C). However, the \u0026ldquo;Exosomes\u0026rdquo; term showed a substantially higher enrichment compared to the background than cytoplasm, with a fold enrichment of 3.9 vs 1.4 (Figure 5C). Furthermore, other significantly enriched terms linked to EV were present, including \u0026ldquo;Extracellular\u0026rdquo;, \u0026ldquo;Extracellular region\u0026rdquo;, \u0026ldquo;Extracellular space\u0026rdquo;, \u0026ldquo;Plasma membrane\u0026rdquo;, and \u0026ldquo;Lysosome\u0026rdquo; with fold enrichments of 2.5, 4.8, 4.1, 1.4, and 3.5 respectively (Figure 5C).\u003c/p\u003e\n\u003cp\u003eK-means clustering of the significantly different proteins (increased in either EV from WT or DJ-1KO) resulted in 5 clusters (Figure 5D): cluster 1, 1 protein absent in EV from DJ-1 KO; cluster 2, 7 proteins highly enriched in EV from DJ-1 KO; cluster 3, 43 proteins lowly enriched in EV from DJ-1 KO; cluster 4, 59 proteins lowly enriched in EV from WT; and cluster 5, 6 proteins highly enriched in EV from WT. The vast majority of EV proteins were either in cluster 3 or 4, which together represent 87.9 % of significantly different EV proteins. \u003c/p\u003e\n\u003cp\u003eGO analysis of biological process for the fold change derived protein groupings showed that each cluster retained a relevant theme of interest. Cluster 1 was represented by a single protein, RUVBL2, involved in regulation of DNA transcription and repair, and histone and chromatin modification [25]. Cluster 2 highlighted the theme of blood coagulation, and a secondary theme of synaptic regulation. Cluster 3 was enriched in proteins involved in immunoregulation and protein localisation/transport; cluster 4 presented proteins playing a role in immunoregulation and synaptic vesicle regulation, and cluster 5 showed the themes of cell adhesion and differentiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEV effects on macrophage migration are dependent on DJ-1 and rotenone-induced oxidative stress in donor SH-SY5Y cells\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data shown so far indicate a clear DJ-1 effect in regulating the amount and cargo of EV present in the medium upon oxidative stress. This is only biologically relevant if linked to a different response from recipient cells to such EV. As DJ-1 has been reported to modulate the activation of several immune cells including macrophages, mast cells, and T cells [26], we next investigated the ability of EV obtained from wild-type cells or DJ-1 KO cells to promote THP-1-derived macrophage migration (Figure 6).\u003c/p\u003e\n\u003cp\u003eInterestingly, when comparing the effect of EV from wild-type and DJ-1 KO cells in control condition (Figure 6A and B), we observed a stronger effect of EV from wild-type cells compared to DJ-1 KO on THP-1 derived macrophages migration (p= 0.016), consistent with a decreased efficiency of EV mediated signal in the absence of DJ-1 in donor cells despite no change in EV number between the two genotypes (Fig.2). However, when we compared the effect of EV from wild-type and DJ-1 KO cells upon rotenone treatment, a much stronger effect on THP1 migration was observed for DJ-1 KO cells than wild-type (p=0.008). These results clearly indicate DJ-1 involvement in the modulation of macrophage migration via EV. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDJ-1 KO alters EV response to rotenone in iPSC-derived neuronal cells\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results obtained with the SH-SY5Y cell line show a clear role of DJ-1 in intercellular communication upon oxidative stress. However, to verify their relevance in a cellular model not derived from a cancer cell line, the effect of rotenone on EV populations was also studied in iPSC-derived neuronal cells. Neuronal cells were differentiated from iPSC with a 1bp deletion in the \u003cem\u003ePARK7\u003c/em\u003e gene and their isogenic control, both kindly provided by Dr Mark Cookson, NIH (Figure 7A). We first identified a suitable rotenone concentration, to account for differences in cell sensitivity to oxidative stress compared to SH-SY5Y cells. To this aim, iPSC-derived neurons were treated with varying concentrations of rotenone for 24 hr and the number of necrotic cells was analysed as described for SH-SY5Y cells by using a membrane-impermeable fluorescent dye. Of the treatments tested, 1 \u0026micro;M rotenone appeared the most suitable both by visual observation of cultures and assessment of necrotic percentage as it had minimal effect on the death of B-10 iPSC-derived neurons (data not shown). The number of EV per iPSC-derived neuron in untreated/treated B-10 and isogenic control cultures (MAP2 positive, Sox2 negative) was assessed via flow cytometry on a NanoFCM nanoanalyser system. Our results showed that rotenone treatment increased the number of EV in the isogenic control cultures by a factor of 2.25 from 177 to 399 (p = 0.013, t test, Bonferroni corrected) (Figure 7B). This effect on the isogenic control coupled with the lack of effect in B-10 cultures resulted in 185 more EV per cell in rotenone treated control compared to B10 (p = 0.035 t test, Bonferroni corrected), thus confirming DJ-1 key role in the EV response to oxidative stress in this iPSC-derived neuronal cells. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the CNS, EV released by neurons and glial cells contribute to different processes essential to brain health including neuronal maintenance and repair, myelination, synaptic activity, and stress response [27\u0026ndash;29].\u003c/p\u003e \u003cp\u003eThis study highlights a role for DJ-1 in EV-mediated intercellular communication by demonstrating that neuronal EV number and relative protein cargo are different between WT and DJ-1 KO cells upon oxidative stress. While substantia nigra dopaminergic neurons vulnerability to oxidative stress [30] and DJ-1 involvement in the protection from oxidative stress are well established, the role played by EV in this context is still unclear. EV increased production may represent a protective cell mechanism against oxidative stress: via their cargo EV can stimulate pro-survival responses in recipient cells [31]. Alternatively, EV released in oxidative stress may exert a detrimental effect on recipient cells via their oxidized lipids and proteins cargo [32] though this may represent a mechanism by which a donor cell may seek \u0026ldquo;self-protection\u0026rdquo; through discard of oxidised components.\u003c/p\u003e \u003cp\u003eIntrigued by this key EV role in the ability of cells to deal with oxidative stress, we analyzed the EV response to rotenone treatment in differentiated SH-SY5Y cells lacking DJ-1. We observed an increase in EV upon rotenone treatment in both wild type and DJ-1 KO cells, confirming the involvement of EV in the oxidative stress response. Strikingly, DJ-1 knockout cells required a higher concentration of rotenone to elicit an enhanced small EV response compared to wild-type cells (10 nM rotenone for DJ-1KO, 5 nM for WT). This aligns with the ability of cellular DJ-1 to act as an oxidative stress sensor [20] and respond to a lower level of oxidative stress in wild type cells compared to DJ-1 KO, and could explain why in the absence of DJ-1 the EV response to such low level of oxidative stress was absent.\u003c/p\u003e \u003cp\u003eAt 10nm rotenone the EV response was significantly different for each genotype compared to the respective control and between genotypes, with DJ-1 KO cells exhibiting a marked increase in detectable EV. Interestingly, a much smaller but still significant difference in EV between the two genotypes was also observed in control condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, EV may represent a cellular mechanism for managing oxidative stress, which is already higher in DJ-1 KO cells compared to controls, even in the absence of rotenone. This is supported by our data on mitochondrial morphology and polarization state (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince EV cargo determines the biological response, we next analysed the EV protein content using mass spectrometry. Our data show clearly that DJ-1 not only regulates the quantity of EV, but also influences the protein composition of EV under oxidative stress, indicating a DJ-1 dependent proteomic signature of EV upon oxidative stress. Significant proteins identified as different in DJ-1 KO EV compared to wild type EV were distributed among 5 clusters based on their fold change (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Notably, GO analysis of each of each cluster highlighted biological processes linked to PD. In cluster 2 (Blood coagulation/ cell adhesion) APOH (Beta-2-glycoprotein 1) was the protein with the highest difference between the two genotypes, with an increase of 26-fold in DJ-1 KO EV compared to wild type EV. APOH is a multifunctional protein that can both upregulate and downregulate the coagulation and complement systems in response to external stimuli [33], expressed by different cell types including neurons [34]. It is known that PD may also be accompanied by changes in the normal clotting of blood [35]: a blood transcriptome analysis in idiopathic and LRRK2 G2019S PD patients [36] showed complement and coagulation cascade as one of the only four common dysregulated pathway in both idiopathic and LRRK2 patients compared to controls, thus highlighting such dysregulation as a common mechanism in sporadic and familial PD cases. Sharma et al [37] also showed that a subset of genes that play an active role in blood coagulation-fibrinolysis are altered and contribute significantly to PD-associated key biological pathways.\u003c/p\u003e \u003cp\u003eIn cluster 3, lysosomal-trafficking regulator (LYST) was the protein with the highest fold change, with a 4.33-fold increase in EV from DJ-1 KO compared to wild type. LYST plays a key role in the regulation of membrane dynamics and intracellular trafficking of lysosomes [38], a process essential to maintaining proteostasis, which is disrupted in Parkinson\u0026rsquo;s disease. The theme of intracellular trafficking returns in cluster 4 as its most interesting candidate, synaptotagmin 11, reduced (0.13 fold change) in DJ-1 KO EV, is involved in regulating vesicle dynamics, including endo and exocytosis [39]. Differently from other synaptotagmin isoforms, Syt11 is not localized on synaptic vesicles but on trafficking endosomes and its function is crucial for development and synaptic plasticity [40].\u003c/p\u003e \u003cp\u003eGWAS studies have identified the synaptotagmin-11 (SYT11) locus as being linked to an increased risk of Parkinson\u0026rsquo;s disease (PD) [41, 42]. Lastly, TSG101 in cluster 5 (Tumour susceptibility gene 101 protein), depleted in DJ1 KO EV (fold change\u0026thinsp;=\u0026thinsp;0.051) is involved in the regulation of exosome biogenesis [43]. These data indicate that dysregulated vesicle trafficking is a key mechanism disrupted in the absence of DJ-1.\u003c/p\u003e \u003cp\u003eThe narrative around RUVBL2, the only significant protein undetectable in DJ-1 KO EV and sole component of cluster 1, takes a different direction. Unlike other EV protein candidates implicated in intracellular trafficking, RUVBL2 is a highly conserved AAA\u0026thinsp;+\u0026thinsp;ATPase that forms a hetero\u0026ndash;hexameric complex with the closely related protein RUVBL1 [25]. This hetero-hexameric ring main function is to provide a scaffold on which the other subunits of the INO80 nucleosome remodeller complex are assembled [44]. The INO80 complex alters transcription via regulating the chromatin structure by repositioning, sliding, ejecting, dis/assembling the nucleosome as well as exchanging histone variants [45]. Notably, a recent study by Yuan et al.[46] has shown that in yeast INO80 contributes to stress adaptation by binding to DNA and creating open chromatin regions at specific sites, facilitating efficient transcription initiation.\u003c/p\u003e \u003cp\u003eTo link the proteomic signature to a functional read out, we proved that EV from cells lacking DJ-1 in oxidative stress condition have a dramatically different effect in stimulating macrophage migration. We identified two distinct effects of EV obtained from wild type or DJ-1 KO cells under control conditions compared to rotenone treatment. Under control conditions, EV from wild-type cells showed a stronger ability to induce macrophage migration than those from DJ-1 KO cells. However, following rotenone treatment, EV from DJ-1 KO cells exhibited a significantly stronger effect. These results suggest that a low level of oxidative stress such as the one present in wild type cells treated with 10nM rotenone or in DJ-1 KO cells in control conditions (increased oxidative stress level exclusively due to DJ-1 absence) consistently reduces migration, as both EV from DJ-1 KO control and rotenone treated WT cells are less capable of inducing migration compared to EV from wild type cells. This could be easily explained by the loss of function of EV cargo molecules that are modified upon mild oxidative stress conditions. Interestingly, this is no longer observed when both DJ-1 knockout and rotenone treatment (10nM) are applied together as a much stronger effect in inducing cell migration was observed. The observed differences in migration in this case indicate that the effect cannot be attributed solely to the number of EV produced. Although treatment with 10 nM rotenone increases the EV count in both genotypes, an increase in migration is only seen when the EV originate from DJ-1 KO cells. This suggests that the cargo of the EV\u0026mdash;rather than their quantity\u0026mdash;is influencing the migration. Furthermore, it implies that a certain threshold of oxidative damage may be necessary to activate an alternative signalling pathway, potentially related to the absence of DJ-1, which influences the observed cellular behaviour. Macrophage migration in our experiments modelled the responsive ability of the CNS resident macrophage population, microglial cells. However, research has also shown that infiltration of monocyte-derived macrophages into the CNS can occur under certain conditions [47]. To further understand the effect of EV released from neuronal cells upon oxidative stress on the macrophage population future work is needed to clarify the phagocytic ability and polarization state of the recipient cells (pro-inflammatory versus pro-resolving).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our results identify a new role for DJ-1 in EV mediated intercellular communication: DJ-1 regulates the number, cargo and functional effects of EV released from neuronal cells upon oxidative stress. Our data underscore altered intercellular communication via EV as a core aspect of Parkinson's disease biology, with consequences not only in understanding molecular mechanisms in DJ-1 associated forms of PD but also sporadic PD forms, offering new insights into disease progression and novel therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePD: Parkinson\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eEV: extracellular vesicles\u003c/p\u003e\n\u003cp\u003eCNS: central nervous system\u003c/p\u003e\n\u003cp\u003eiPSC: induced pluripotent stem cells\u003c/p\u003e\n\u003cp\u003eCryo-EM: cryogenic electron microscopy\u003c/p\u003e\n\u003cp\u003eMS: mass spectrometry\u003c/p\u003e\n\u003cp\u003eBodipy FL-SE: Bodipy FL Succinimidyl Ester\u003c/p\u003e\n\u003cp\u003eWT: wild type\u003c/p\u003e\n\u003cp\u003eKO: knock-out\u003c/p\u003e\n\u003cp\u003eROS: Reactive oxygen species\u003c/p\u003e\n\u003cp\u003eGO: Gene ontology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) and Aston University funded Midlands Integrative Biosciences Training Partnership (MIBTP) (BB/T00746X/1). MR acknowledge ARUK Midlands Network for funding the iPSC work. AD and IM acknowledge support from the BBSRC (BB/S00324X/1 and BB/S01943X/1). The authors also acknowledge funding support from the Midlands Regional Cryo-EM Facility, hosted at the Warwick Advanced Bioimaging Research Technology Platform, for use of the JEOL 2100Plus, supported by MRC award reference MC_PC_17136. Access was funded by the Warwick Analytical Science Centre EPSRC grant code (EP/V007688/1). The Aston Institute for Membrane Excellence (AIME) is funded by UKRI\u0026rsquo;s Research England as part of their Expanding Excellence in England (E3) fund.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e The authors declare that they have no conflict of interest. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Dr. Mark Cookson for kindly providing the iPSC cells (A18945, DJ1 WT and A18945, DJ1 KO, 2B10 clone). We thank Dr Ann Vernallis for critical reading of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization was done by MR and AD. Experiments were performed by TP, CAM, SEB, DRJ, IM and MR. Data analysis was done by TP and MR. Resources were provided by MR, AD, EJH and TB. Writing of the original draft was done by MR and TP. Review and editing were done by AD, EJH, DRJ, IM and TB. \u0026nbsp;Funding acquisition was done by MR and AD. Supervision was done by MR and AD.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MS datasets used and analysed during the current study are available from the corresponding author on request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBonifati V, Rizzu P, van Baren MJ, Schaap O, Breedveld GJ, Krieger E, Dekker MC, Squitieri F, Ibanez P, Joosse M, et al: \u003cstrong\u003eMutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism.\u003c/strong\u003e \u003cem\u003eScience \u003c/em\u003e2003, \u003cstrong\u003e299:\u003c/strong\u003e256-259.\u003c/li\u003e\n\u003cli\u003eSkou LD, Johansen SK, Okarmus J, Meyer M: \u003cstrong\u003ePathogenesis of DJ-1/PARK7-Mediated Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eCells \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eRepici M, Giorgini F: \u003cstrong\u003eDJ-1 in Parkinson\u0026apos;s Disease: Clinical Insights and Therapeutic Perspectives.\u003c/strong\u003e \u003cem\u003eJ Clin Med \u003c/em\u003e2019, \u003cstrong\u003e8\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHo DH, Yi S, Seo H, Son I, Seol W: \u003cstrong\u003eIncreased DJ-1 in Urine Exosome of Korean Males with Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eBiomed Res Int \u003c/em\u003e2014, \u003cstrong\u003e2014\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eJang J, Jeong S, Lee SI, Seol W, Seo H, Son I, Ho DH: \u003cstrong\u003eOxidized DJ-1 Levels in Urine Samples as a Putative Biomarker for Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eParkinsons Dis-Us \u003c/em\u003e2018, \u003cstrong\u003e2018\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eZhao ZH, Chen ZT, Zhou RL, Zhang X, Ye QY, Wang YZ: \u003cstrong\u003eIncreased DJ-1 and \u0026alpha;-Synuclein in Plasma Neural-Derived Exosomes as Potential Markers for Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eFront Aging Neurosci \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eXia X, Wang Y, Zheng JC: \u003cstrong\u003eExtracellular vesicles, from the pathogenesis to the therapy of neurodegenerative diseases.\u003c/strong\u003e \u003cem\u003eTransl Neurodegener \u003c/em\u003e2022, \u003cstrong\u003e11:\u003c/strong\u003e53.\u003c/li\u003e\n\u003cli\u003eBudnik V, Ruiz-Canada C, Wendler F: \u003cstrong\u003eExtracellular vesicles round off communication in the nervous system.\u003c/strong\u003e \u003cem\u003eNat Rev Neurosci \u003c/em\u003e2016, \u003cstrong\u003e17:\u003c/strong\u003e160-172.\u003c/li\u003e\n\u003cli\u003eGassama Y, Favereaux A: \u003cstrong\u003eEmerging Roles of Extracellular Vesicles in the Central Nervous System: Physiology, Pathology, and Therapeutic Perspectives.\u003c/strong\u003e \u003cem\u003eFront Cell Neurosci \u003c/em\u003e2021, \u003cstrong\u003e15:\u003c/strong\u003e626043.\u003c/li\u003e\n\u003cli\u003eLizarraga-Valderrama LR, Sheridan GK: \u003cstrong\u003eExtracellular vesicles and intercellular communication in the central nervous system.\u003c/strong\u003e \u003cem\u003eFEBS Lett \u003c/em\u003e2021, \u003cstrong\u003e595:\u003c/strong\u003e1391-1410.\u003c/li\u003e\n\u003cli\u003eShi M, Liu CQ, Cook TJ, Bullock KM, Zhao YC, Ginghina C, Li YF, Aro P, Dator R, He CM, et al: \u003cstrong\u003ePlasma exosomal \u0026alpha;-synuclein is likely CNS-derived and increased in Parkinson\u0026apos;s disease.\u003c/strong\u003e \u003cem\u003eActa Neuropathol \u003c/em\u003e2014, \u003cstrong\u003e128:\u003c/strong\u003e639-650.\u003c/li\u003e\n\u003cli\u003eDanzer KM, Kranich LR, Ruf WP, Cagsal-Getkin O, Winslow AR, Zhu LY, Vanderburg CR, McLean PJ: \u003cstrong\u003eExosomal cell-to-cell transmission of alpha synuclein oligomers.\u003c/strong\u003e \u003cem\u003eMol Neurodegener \u003c/em\u003e2012, \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eNila IS, Sumsuzzman DM, Khan ZA, Jung JH, Kazema AS, Kim SJ, Hong Y: \u003cstrong\u003eIdentification of exosomal biomarkers and its optimal isolation and detection method for the diagnosis of Parkinson\u0026apos;s disease: A systematic review and meta-analysis.\u003c/strong\u003e \u003cem\u003eAgeing Res Rev \u003c/em\u003e2022, \u003cstrong\u003e82:\u003c/strong\u003e101764.\u003c/li\u003e\n\u003cli\u003eZhou T, Zhang M, Xie Y, Chen Y, Peng S, Jing X, Lei M, Tao E, Liang Y: \u003cstrong\u003eEffects of miRNAs in exosomes derived from \u0026alpha;-synuclein overexpressing SH-SY5Y cells on autophagy and inflammation of microglia.\u003c/strong\u003e \u003cem\u003eCellular Signalling \u003c/em\u003e2022, \u003cstrong\u003e89\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eYuyama K, Sun H, Mikami D, Mioka T, Mukai K, Igarashi Y: \u003cstrong\u003eLysosomal‐associated transmembrane protein 4B regulates ceramide‐induced exosome release.\u003c/strong\u003e \u003cem\u003eThe FASEB Journal \u003c/em\u003e2020, \u003cstrong\u003e34:\u003c/strong\u003e16022-16033.\u003c/li\u003e\n\u003cli\u003eTortolici F, Vumbaca S, Incocciati B, Dayal R, Aquilano K, Giovanetti A, Rufini S: \u003cstrong\u003eIonizing Radiation-Induced Extracellular Vesicle Release Promotes AKT-Associated Survival Response in SH-SY5Y Neuroblastoma Cells.\u003c/strong\u003e \u003cem\u003eCells \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eTh\u0026eacute;ry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin‐Smith GK, et al: \u003cstrong\u003eMinimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines.\u003c/strong\u003e \u003cem\u003eJournal of Extracellular Vesicles \u003c/em\u003e2018, \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eWelsh JA, Goberdhan DCI, O\u0026apos;Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai HJ, Di Vizio D, Driedonks TAP, Erdbr\u0026uuml;gger U, et al: \u003cstrong\u003eMinimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches.\u003c/strong\u003e \u003cem\u003eJournal of Extracellular Vesicles \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eDias V, Junn E, Mouradian MM: \u003cstrong\u003eThe role of oxidative stress in Parkinson\u0026apos;s disease.\u003c/strong\u003e \u003cem\u003eJ Parkinsons Dis \u003c/em\u003e2013, \u003cstrong\u003e3:\u003c/strong\u003e461-491.\u003c/li\u003e\n\u003cli\u003eWilson MA: \u003cstrong\u003eThe role of cysteine oxidation in DJ-1 function and dysfunction.\u003c/strong\u003e \u003cem\u003eAntioxid Redox Signal \u003c/em\u003e2011, \u003cstrong\u003e15:\u003c/strong\u003e111-122.\u003c/li\u003e\n\u003cli\u003eAriga H, Takahashi-Niki K, Kato I, Maita H, Niki T, Iguchi-Ariga SM: \u003cstrong\u003eNeuroprotective function of DJ-1 in Parkinson\u0026apos;s disease.\u003c/strong\u003e \u003cem\u003eOxid Med Cell Longev \u003c/em\u003e2013, \u003cstrong\u003e2013:\u003c/strong\u003e683920.\u003c/li\u003e\n\u003cli\u003eDoonan F, Cotter TG: \u003cstrong\u003eMorphological assessment of apoptosis.\u003c/strong\u003e \u003cem\u003eMethods \u003c/em\u003e2008, \u003cstrong\u003e44:\u003c/strong\u003e200-204.\u003c/li\u003e\n\u003cli\u003eEbanks B, Chakrabarti L: \u003cstrong\u003eMitochondrial ATP Synthase is a Target of Oxidative Stress in Neurodegenerative Diseases.\u003c/strong\u003e \u003cem\u003eFront Mol Biosci \u003c/em\u003e2022, \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eChiaradia E, Tancini B, Emiliani C, Delo F, Pellegrino RM, Tognoloni A, Urbanelli L, Buratta S: \u003cstrong\u003eExtracellular Vesicles under Oxidative Stress Conditions: Biological Properties and Physiological Roles.\u003c/strong\u003e \u003cem\u003eCells \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eDauden MI, L\u0026oacute;pez-Perrote A, Llorca O: \u003cstrong\u003eRUVBL1-RUVBL2 AAA-ATPase: a versatile scaffold for multiple complexes and functions.\u003c/strong\u003e \u003cem\u003eCurr Opin Struc Biol \u003c/em\u003e2021, \u003cstrong\u003e67:\u003c/strong\u003e78-85.\u003c/li\u003e\n\u003cli\u003eZhang L, Wang J, Wang J, Yang B, He Q, Weng Q: \u003cstrong\u003eRole of DJ-1 in Immune and Inflammatory Diseases.\u003c/strong\u003e \u003cem\u003eFrontiers in Immunology \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAntonucci F, Turola E, Riganti L, Caleo M, Gabrielli M, Perrotta C, Novellino L, Clementi E, Giussani P, Viani P, et al: \u003cstrong\u003eMicrovesicles released from microglia stimulate synaptic activity via enhanced sphingolipid metabolism.\u003c/strong\u003e \u003cem\u003eThe EMBO Journal \u003c/em\u003e2012, \u003cstrong\u003e31:\u003c/strong\u003e1231-1240.\u003c/li\u003e\n\u003cli\u003eFr\u0026uuml;hbeis C, Kuo-Elsner WP, M\u0026uuml;ller C, Barth K, Peris L, Tenzer S, M\u0026ouml;bius W, Werner HB, Nave KA, Fr\u0026ouml;hlich D, Kr\u0026auml;mer-Albers EM: \u003cstrong\u003eOligodendrocytes support axonal transport and maintenance via exosome secretion.\u003c/strong\u003e \u003cem\u003ePlos Biol \u003c/em\u003e2020, \u003cstrong\u003e18\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLino MM, Simoes S, Tomatis F, Albino I, Barrera A, Vivien D, Sobrino T, Ferreira L: \u003cstrong\u003eEngineered extracellular vesicles as brain therapeutics.\u003c/strong\u003e \u003cem\u003eJ Control Release \u003c/em\u003e2021, \u003cstrong\u003e338:\u003c/strong\u003e472-485.\u003c/li\u003e\n\u003cli\u003eSurmeier DJ, Guzman JN, Sanchez-Padilla J, Schumacker PT: \u003cstrong\u003eThe role of calcium and mitochondrial oxidant stress in the loss of substantia nigra pars compacta dopaminergic neurons in Parkinson\u0026apos;s disease.\u003c/strong\u003e \u003cem\u003eNeuroscience \u003c/em\u003e2011, \u003cstrong\u003e198:\u003c/strong\u003e221-231.\u003c/li\u003e\n\u003cli\u003eBorras C, Mas-Bargues C, Sanz-Ros J, Roman-Dominguez A, Gimeno-Mallench L, Ingles M, Gambini J, Vina J: \u003cstrong\u003eExtracellular vesicles and redox modulation in aging.\u003c/strong\u003e \u003cem\u003eFree Radic Biol Med \u003c/em\u003e2020, \u003cstrong\u003e149:\u003c/strong\u003e44-50.\u003c/li\u003e\n\u003cli\u003eBenedikter BJ, Weseler AR, Wouters EFM, Savelkoul PHM, Rohde GGU, Stassen FRM: \u003cstrong\u003eRedox-dependent thiol modifications: implications for the release of extracellular vesicles.\u003c/strong\u003e \u003cem\u003eCell Mol Life Sci \u003c/em\u003e2018, \u003cstrong\u003e75:\u003c/strong\u003e2321-2337.\u003c/li\u003e\n\u003cli\u003eMcDonnell T, Wincup C, Buchholz I, Pericleous C, Giles I, Ripoll V, Cohen H, Delcea M, Rahman A: \u003cstrong\u003eThe role of beta-2-glycoprotein I in health and disease associating structure with function: More than just APS.\u003c/strong\u003e \u003cem\u003eBlood Rev \u003c/em\u003e2020, \u003cstrong\u003e39\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eCaronti B, Calderaro C, Alessandri C, Conti F, Tinghino R, Palladini G, Valesini G: \u003cstrong\u003eBeta2-glycoprotein I (beta2-GPI) mRNA is expressed by several cell types involved in anti-phospholipid syndrome-related tissue damage.\u003c/strong\u003e \u003cem\u003eClin Exp Immunol \u003c/em\u003e1999, \u003cstrong\u003e115:\u003c/strong\u003e214-219.\u003c/li\u003e\n\u003cli\u003eRosenbaum H, Aharon-Peretz J, Brenner B: \u003cstrong\u003eHypercoagulability, parkinsonism, and Gaucher disease.\u003c/strong\u003e \u003cem\u003eSemin Thromb Hemost \u003c/em\u003e2013, \u003cstrong\u003e39:\u003c/strong\u003e928-934.\u003c/li\u003e\n\u003cli\u003eInfante J, Prieto C, Sierra M, S\u0026aacute;nchez-Juan P, Gonz\u0026aacute;lez-Aramburu I, S\u0026aacute;nchez-Quintana C, Berciano J, Combarros O, Sainz J: \u003cstrong\u003eComparative blood transcriptome analysis in idiopathic and G2019S-associated Parkinson\u0026apos;s disease.\u003c/strong\u003e \u003cem\u003eNeurobiol Aging \u003c/em\u003e2016, \u003cstrong\u003e38\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eSharma A, Muller J, Schuetze K, Rolfes V, Bissinger R, Rosero N, Ahmad A, Franklin BS, Zur B, Frohlich H, et al: \u003cstrong\u003eComprehensive Profiling of Blood Coagulation and Fibrinolysis Marker Reveals Elevated Plasmin-Antiplasmin Complexes in Parkinson\u0026apos;s Disease.\u003c/strong\u003e \u003cem\u003eBiology (Basel) \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eTurner ME, Che J, Mirhaidari GJM, Kennedy CC, Blum KM, Rajesh S, Zbinden JC, Breuer CK, Best CA, Barker JC: \u003cstrong\u003eThe lysosomal trafficking regulator \u0026quot;LYST\u0026quot;: an 80-year traffic jam.\u003c/strong\u003e \u003cem\u003eFront Immunol \u003c/em\u003e2024, \u003cstrong\u003e15:\u003c/strong\u003e1404846.\u003c/li\u003e\n\u003cli\u003eWolfes AC, Dean C: \u003cstrong\u003eThe diversity of synaptotagmin isoforms.\u003c/strong\u003e \u003cem\u003eCurr Opin Neurobiol \u003c/em\u003e2020, \u003cstrong\u003e63:\u003c/strong\u003e198-209.\u003c/li\u003e\n\u003cli\u003eShimojo M, Madara J, Pankow S, Liu XR, Yates J, S\u0026uuml;dhof TC, Maximov A: \u003cstrong\u003eSynaptotagmin-11 mediates a vesicle trafficking pathway that is essential for development and synaptic plasticity.\u003c/strong\u003e \u003cem\u003eGene Dev \u003c/em\u003e2019, \u003cstrong\u003e33:\u003c/strong\u003e365-376.\u003c/li\u003e\n\u003cli\u003eInternational Parkinson Disease Genomics C, Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simon-Sanchez J, Schulte C, Lesage S, et al: \u003cstrong\u003eImputation of sequence variants for identification of genetic risks for Parkinson\u0026apos;s disease: a meta-analysis of genome-wide association studies.\u003c/strong\u003e \u003cem\u003eLancet \u003c/em\u003e2011, \u003cstrong\u003e377:\u003c/strong\u003e641-649.\u003c/li\u003e\n\u003cli\u003eSesar A, Cacheiro P, L\u0026oacute;pez-L\u0026oacute;pez M, Cami\u0026ntilde;a-Tato M, Quint\u0026aacute;ns B, Monroy-Jaramillo N, Alonso-Vilatela ME, Cebri\u0026aacute;n E, Yescas-G\u0026oacute;mez P, Ares B, et al: \u003cstrong\u003eSynaptotagmin XI in Parkinson\u0026apos;s disease: New evidence from an association study in Spain and Mexico.\u003c/strong\u003e \u003cem\u003eJ Neurol Sci \u003c/em\u003e2016, \u003cstrong\u003e362:\u003c/strong\u003e321-325.\u003c/li\u003e\n\u003cli\u003eGurung S, Perocheau D, Touramanidou L, Baruteau J: \u003cstrong\u003eThe exosome journey: from biogenesis to uptake and intracellular signalling.\u003c/strong\u003e \u003cem\u003eCell Commun Signal \u003c/em\u003e2021, \u003cstrong\u003e19:\u003c/strong\u003e47.\u003c/li\u003e\n\u003cli\u003ePoli J, Gasser SM, Papamichos-Chronakis M: \u003cstrong\u003eThe INO80 remodeller in transcription, replication and repair.\u003c/strong\u003e \u003cem\u003ePhilos Trans R Soc Lond B Biol Sci \u003c/em\u003e2017, \u003cstrong\u003e372\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eJiang D, Li T, Guo C, Tang TS, Liu H: \u003cstrong\u003eSmall molecule modulators of chromatin remodeling: from neurodevelopment to neurodegeneration.\u003c/strong\u003e \u003cem\u003eCell Biosci \u003c/em\u003e2023, \u003cstrong\u003e13:\u003c/strong\u003e10.\u003c/li\u003e\n\u003cli\u003eYuan B, Wang WB, Wang XQ, Liu CG, Hasunuma T, Kondo A, Zhao XQ: \u003cstrong\u003eThe chromatin remodeler Ino80 regulates yeast stress tolerance and cell metabolism through modulating nitrogen catabolite repression.\u003c/strong\u003e \u003cem\u003eInt J Biol Macromol \u003c/em\u003e2024, \u003cstrong\u003e258\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ePerry VH, Teeling J: \u003cstrong\u003eMicroglia and macrophages of the central nervous system: the contribution of microglia priming and systemic inflammation to chronic neurodegeneration.\u003c/strong\u003e \u003cem\u003eSemin Immunopathol \u003c/em\u003e2013, \u003cstrong\u003e35:\u003c/strong\u003e601-612.\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":true,"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":"neurodegeneration, DJ-1, extracellular vesicles, proteomic signature","lastPublishedDoi":"10.21203/rs.3.rs-5669239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5669239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMutations in DJ-1 cause autosomal recessive Parkinson\u0026rsquo;s disease. Several functions have been attributed to DJ-1, including a key role in the protection from oxidative stress, however how this protein contributes to PD pathogenesis is still unclear. Recently, DJ-1 has been identified at higher concentration in extracellular vesicles (EV) from biological fluids of PD patients, providing a link between EV and a protein associated with PD. EV were isolated from the medium of control and rotenone-treated wild-type and DJ-1 KO differentiated SH-SY5Y cells, their number was evaluated by flow cytometry and the proteomic signature of their cargo was investigated by mass spectrometry analysis. Migration of THP-1 derived macrophages was used a read out for functional EV. The results obtained were validated in iPSC-derived neuronal cells. We identified an altered EV response to rotenone in DJ-1 KO cells compared to wild-type. Mass spectrometry analysis identified 116 proteins with significantly different concentrations between the two genotypes, suggesting a link between DJ-1 and EV cargo in response to oxidative stress. Additionally, we showed that DJ-1 KO alters the ability of EV to stimulate macrophage migration, thus implying functional consequences for DJ-1 absence in the EV mediated response to oxidative stress. The altered EV response to rotenone was confirmed in iPSC-derived neurons lacking DJ-1 compared to isogenic controls. Our results indicate a clear DJ-1 role in intercellular communication in oxidative stress, underlying a new EV mediated DJ-1 function that may be relevant to PD pathogenesis.\u003c/p\u003e","manuscriptTitle":"Parkinson’s associated protein DJ-1 regulates intercellular communication via extracellular vesicles in oxidative stress.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:50:56","doi":"10.21203/rs.3.rs-5669239/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":"b8e5d6f0-b278-4339-843f-175308460c70","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T23:21:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:50:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5669239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5669239","identity":"rs-5669239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}