Proteomic Profile of Extracellular Vesicles from Plasma and CFS of Multiple Sclerosis Patients Reveals Disease Activity- Associated EAAT2

Proteomic Profile of Extracellular Vesicles from Plasma and CFS of Multiple Sclerosis Patients Reveals Disease Activity- Associated EAAT2 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proteomic Profile of Extracellular Vesicles from Plasma and CFS of Multiple Sclerosis Patients Reveals Disease Activity- Associated EAAT2 Antonella D’Ambrosio, Silvia Zamboni, Serena Camerini, Marialuisa Casella, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909260/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Journal of Neuroinflammation → Version 1 posted 7 You are reading this latest preprint version Abstract Background and Objectives There is an urgent need to discover blood-based biomarkers of multiple sclerosis (MS) to better define the underlying biology of relapses and monitor disease progression. The main goal of this study is to search for candidate biomarkers of MS relapses associated with circulating extracellular vesicles (EVs), an emerging tool for biomarker discovery. Methods EVs, purified from unpaired plasma and CSF samples of RRMS patients by size-exclusion chromatography (SEC), underwent qualitative proteomic analysis to discover novel biomarkers associated with MS relapses. The candidate biomarkers of disease activity were detected by comparison approach between plasma- and CSF-EV proteomes associated with relapses. Among them, a selected potential biomarker was evaluated in a cohort of MS patients, using a novel and highly reproducible flow cytometry-based approach in order to detect low abundant EV subsets in a complex body fluid such as plasma. Results The proteomic profiles of both SEC-purified plasma EVs (from 6 patients in relapse and 5 patients in remission) and SEC-puirified CSF EVs (from 4 patients in relapse and 3 patients in remission) revealed a set of proteins associated with MS relapses significant enriched in the synaptic transmission pathway. Among common proteins, excitatory amino-acid transporter 2, EAAT2, responsible for the majority of the glutamate uptake in CNS, was worthy of further investigation. By screening plasma samples from 110 MS patients, we found a significant association of plasma EV-carried EAAT2 protein (EV-EAAT2) with MS relapses, regardless of disease-modifying therapies. This finding was confirmed by investigating the presence of EV-EAAT2 in plasma samples collected longitudinally from 10 RRMS patients, during relapse and remission. Moreover, plasma EV-EAAT2 levels correlated positively with Expanded Disability Status Scale (EDSS) score in remitting MS patients but showed a negative correlation in patients with secondary progressive (SPMS) and EDSS > 3. Conclusion Our results emphaticize the usefulness of plasma EVs as a source of accessible biomarkers to remotely analyse the CNS status. Plasma EV-EAAT2 showed to be a promising biomarker for MS relapses. Further studies are required to assess the clinical relevance of this biomarker also for disability progression independent of relapse activity and transition from RRMS towards SPMS. multiple sclerosis extracellular vesicles comparative proteomics synaptic transmission pathway disease activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Multiple sclerosis (MS) is a chronic demyelinating and neurodegenerative disease of the CNS affecting young adults, that typically manifests with episodes of transient exacerbations of neurological disability (relapses), followed by partial or total recovery 1 , 2 . MS relapses, a key feature of relapsing-remitting multiple sclerosis (RRMS), the most prevalent MS phenotype, are defined as occurrence of new symptoms or worsening of old symptoms, not always accompanied by the detection of contrast-enhancing lesions, by MRI. This may be due at least in part to the limited sensitivity of conventional MRI to detect small lesions, particularly in the spinal cord, cortical grey matter and optic nerve. 3 , 4 , 5 , 6 In the context of clinical sign and symptom worsening, in the absence of the gadolinium-enhancing lesions, and aiming to improve treatment decisions, it is important to distinguish MS relapses from pseudo-relapses, which may be triggered by infection or comorbidities. 7 On the other hand, MRI may reveal active lesions without symptoms, indicating a subclinical relapse. 8 Recent data have shown the impact of effective prevention of relapses on long-term disability. 9 However, there is also evidence of disability accumulation unrelated to relapses in RRMS; this condition, referred to as progression independent of relapse activity (PIRA), is associated with a predominant underlying neurodegenerative component. 10 Given the heterogeneity of MS course and the increasing number of disease-modifying therapies (DMTs) for RRMS, with different safety profiles and efficacy in reducing CNS inflammation and relapse rates, the discovery of peripheral biomarkers that facilitate disease activity assessment and personalized treatment, would greatly improve patient care. Currently, soluble neurofilament-light chain (sNfl) and glial fibrillary acidic protein (GFAP) have shown promise as biomarkers of acute disease activity and progression. 11 , 12 , 13 However, there are several limitations to the potential use of these molecules as peripheral biomarkers mainly due to confounding factors, such as age. 14 Therefore, there is an emerging interest in searching for novel MS biomarkers in order to develop a panel of molecules that might be used in the clinical practice. Growing evidence in neurological diseases indicates extracellular vesicles (EVs), an heterogeneous family of extracellular structures bounded by a phospholipid bilayer, released by all CNS cell types in cerebrospinal fluid (CSF), as vehicles of intercellular communication involved in many physiological and pathological processes. 15 The ability of CNS-derived EVs to cross the blood-brain barrier and entering the peripheral blood, makes them an easily accessible biomarker source of neurological disorders, including MS. 16 Moreover, EVs, sharing the same antigenic repertoire as their parental cells, may dynamically reflect the pathologic mechanisms underlying CNS damage. Therefore, EVs, with their molecular constituents more stable than soluble molecules in body fluids, are becoming object of multi-omics investigation not only to reveal novel biomarkers of the disease but also to improve the knowledge of the molecular mechanisms underpinning MS pathogenesis. In this study, we investigated the proteome composition of plasma and CSF EVs obtained from RRMS patients aiming at identifying potential peripheral biomarkers associated with disease activity. We have selected one plasma candidate biomarker associated with relapses and established a novel flow cytometry-based assay for its detection in a larger MS cohort, including RRMS and SPMS patients. Materials and methods Study Population In this multi-center longitudinal study, 110 patients with a diagnosis of MS based on the 2018 revised McDonald criteria, 17 were enrolled at the Department of Neuroscience, ‘La Sapienza’ University of Rome, and the Department of Neuroscience, University of Padua, Italy, between 2017 and 2019. Eighty-three patients had a diagnosis of RRMS (41 with no clinically or radiologically evident relapse for at least 12 months and 42 in acute relapse) and 27 SPMS patients (Table 1 ). Inclusion criteria for patient enrollment were: age from 18 to 65 years; no comorbidities or infectious diseases and no steroid therapy in the month before blood sampling; no women in pregnancy, lactation, or planning a pregnancy. Patient characterization included a clinical evaluation with EDSS score and MRI assessment. The control group included 23 gender- and age-matched healthy subjects. This study was approved by the ethics committees of the ‘La Sapienza’ University of Rome (725/16) and Istituto Superiore di Sanità (174/16). Signed informed consent was obtained from all the enrolled study subjects. Table 1 Demographic and clinical characteristics of MS patients and controls. RRMS Relapse RRMS Remission SPMS HC Total Demographic characteristics No. of Patients 42 41 27 23 133 Gender Female/Male 26 /16 30 /11 15 /12 12 /11 83 /50 Age Mean (SD) 40,9 ± 10,2 44,3 ± 11,2 53,6 ± 7,6 44,9 ± 14,7 45,2 ± 11.8 Range (years) 21–62 24–62 35–65 22–65 21–65 Clinical Disease Duration Mean (SD) 3,1 ± 3,5 6,9 ± 4,4 15 ± 7,3 - 7.4 ± 6.8 Range (years) 0–12 1–16 5–25 - 0–25 EDSS score Mean (SD) 1,2 ± 0,75 1,4 ± 1,02 4,6 ± 1,8 - 2.1 ± 1.8 Range 0–3 0–4 1.5 − 8 - 0–8 Age at Disease onset Mean (SD) 37,8 ± 10,1 37,4 ± 9,6 39 ± 10,3 - - (DMTs) yes/no 15 / 27 24 / 17 0 / 27 - Blood and CSF samples Blood samples were collected in sodium citrate tubes (Becton Dickinson, USA) and processed within 60 min from collection to obtain Platelet-Poor Plasma (PPP). CSF specimens were collected by non-traumatic lumbar puncture as previously reported. 18 All PPP and CSF samples were aliquoted and kept frozen at -80C until use. Among 83 RRMS patients enrolled in this study, plasma samples from 10 patients were collected at relapse and remission, over a one year period. For EV proteomic analysis, plasma samples from 11 untreated RRMS patients (6 in relapse and 5 in remission) and 5 healthy controls and CSF samples from 7 untreated RRMS patients (4 in relapse and 3 in remission) were used. For flow cytometry analysis, plasma samples from RRMS patients with (n = 39) or without (n = 44) first-line DMTs for at least 3 months from blood sampling, and 27 untreated SPMS patients, were used. DMTs included interferons, teriflunomide, glatiramer acetate or dimethyl fumarate. Size-exclusion chromatography (SEC) For purification of CSF- and blood-derived EVs by size, individual CSF (4 ml) and PPP (6ml) samples were loaded on to a Sephacryl S-500 gel filtration column (GE Healthcare). EV-containing fractions, evaluated by transmission electron microscopy (TEM), were concentrated by two consecutive centrifugations (Beckman) at 20.000g and 100.000g each for 2 hrs and suspended in PBS. Transmission electron microscopy (TEM) Fractions obtained by SEC EV purification, were deposited and dried onto thin substrates of amorphous carbon and negatively stained with 2% (w/v) phosphotungstic acid. Samples were observed using a Zeiss EM902 transmission electron microscope, operating at 80 kV and equipped with an “in column” electron energy filter. Images were acquired with a digital charge-coupled device camera, model PROSCAN HSC2 (1 K Å~ 1 K pixels), thermostated by a Peltier unit. Image analysis was performed using the digital analyzer SIS 3.0 and the overall resolution can be estimated in the order of 2 nm. 19 Proteomic analysis SEC-purified EVs were loaded on 1D-gel NuPAGE 4–12% and trypsin digested in 10 contiguous slices cut in each gel lane. 20 The resulting peptide mixtures were separated by an Ultimate 3000 HPLC (DIONEX, USA) connected with a linear ion trap mass spectrometer (LTQ-XL, ThermoElectron, USA): they were desalted on a trap column (Acclaim PepMap 100 C18, LC Packings, DIONEX) and separated on a 10 cm long column (Silica Tips FS 360-75-8, New Objective, USA) slurry-packed in-house with 5 µm, 200 Å pore size C18 resin (Michrom BioResources, USA). A 50 min gradient from 4 to 80% buffer B (95% acetonitrile and 0.1% formic acid) and buffer A(5% acetonitrile and 0.1% formic acid) was used at 300 nL/min flow rate. MS spectra were acquired from 400 to 2000 m/z in a Top 5 data-dependent mode, with 45s long dynamic exclusion and applying 35% CID for fragmentation. Tandem mass spectra were matched against the Homo Sapiens protein database ( http://www.uniprot.org/downloads ) and through Bioworks software (version 3.3, Thermo Electron). Fully tryptic cleavage constraints (one miss-cleavage allowed), static cysteine carbamidomethylation, and variable methionine oxidation were considered as match parameters and 1.5 and 1 Da were used as mass tolerance for precursor and fragment ions, respectively. For peptide identification cross correlation scores of 1.8, 2.5 and 3 for 1, 2 and 3 peptide charge state, respectively, and peptide probability cut-off of P < 0.001 were used. Proteins were identified with at least two peptides. Western blot analysis SEC-purified plasma EVs and EVs isolated by centrifugation (100.000g for 20 min) from the culture supernatant of U251 multiform glioblastoma and chronic myelogenous leukemia K562 cell lines were used. The protein concentration was determined using the Bradford protein assay (Bio-Rad, USA). Proteins were separated on 10% pre-casted acrylamide gels (Invitrogen, Carlsbad, CA) and transferred to PVDF membranes. The membranes were blocked (5% milk and 0.05% Tween-20) for 2 hrs and incubated overnight with PE-conjugated EAAT2-specific rabbit polyclonal antibody (1µg) (Bioss, USA), as primary antibody. After washing in PBS, the secondary HRP-conjugated anti-rabbit IgG (Sigma-Aldrich) was added. Chemiluminescent detection of proteins was performed using ECL Plus reagent (Amersham). Flow cytometry gating strategy EVs in PPP samples were analyzed using Gallios flow cytometer (Beckman Coulter, USA) after an accurate setting of the physical and fluorescence parameters. In particular, for the correct setting of the gate (based on the size of EVs) and the fluorescence parameters, fluorescent beads of variable size were used (Flow Cytometry Sub-micron Particle Size Reference Kit- Thermo Fischer scientific). The flow cytometer was adjusted to cover the EV size range between 0.5 and 1 µm. Moreover, FCS threshold value was determined to reduce the background noise of the instrument preserving the detection of the EV population of 0.5 µm in size. A routine verification of optical alignment of lasers and fluidic stability of flow cytometer were performed daily with Flow-Check Pro Fluorosphere (Beckman Coulter), according to the manufacturer's instructions. The correct setting of 0.5-1 µm range size for EVs gate was periodically checked. Data were analyzed using Kaluza software 1.2 (Beckman Coulter). Identification of EVs by MTG labelling SEC-purified plasma EVs or EVs in PPP samples were labeled with MITO Tracker Green FM (MTG) (Molecular Probes-Invitrogen) to identify EV populations using a newly developed flow cytometry assay described in Supplementary methods. Flow Count Fluorosphere (Beckman Coulter) with a known number of fluorescent beads were utilized for EV quantification, according to manufacturer’s instructions. A volume of PPP containing 1x10 6 EVs was diluted in PBS and the analysis of the EAAT2 protein on the EV surface in plasma samples of MS patients and healthy subjects was performed using 1 µg of PE-conjugated EAAT2-specific rabbit polyclonal antibody (Bioss, USA) for 45 min at RT. Then, MTG (100 nM) was added for 15 min at RT before FACS acquisition. To avoid immune complex formation and the unspecific background due to antibody aggregation, each antibody and reagent was centrifuged before use (20.000g for 20 min). 21 , 22 The amount of antibody used for EV staining was titrated in order to determine the optimal concentration and have a low signal-to-noise ratio. Unstained EVs and/or uncorrelated matching antibody isotype (Bioss Rabbit IgG isotype control, PE conjugated, USA), were used to determine the background fluorescence. EAAT2 measurement was made in triplicate for each sample and the mean values with a standard deviation (SD) less than 10% were used for data analysis. EAAT2 detection on EVs derived from U251 and K562 cell lines U251 cell line, provided by Dr. A. Calogero 23 , and K562 cell line culture conditions and EV isolation from supernatants are reported in Supplementary methods. Statistical and data analysis To compare data obtained in different patient subgroups, one way ANOVA followed by a post-hoc Bonferroni’s correction for multiple comparisons was conducted. The correlation analyses were perfomed by Pearson's index. When comparing two groups Student’s t-test and Mann-Withney test were used where appropriate to determine statistical significance. The P value ≤ 0,05 was considered statistically significant. The SPSS Version 28.0 and Graphpad Prism 5 software were used for statistical analyses. We annotated the identified proteins using the UniProt database ( http://www.uniprot.org/ ). Complete proteomic data are shown in Supplementary material (Tables 1 and 2 ). To identify the GO cellular components enriched by a set of proteins or genes, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID), updated on September 22, 2023 ( https://david.ncifcrf.gov/home.jsp ). In addition, we performed the Functional enrichment 3.1.3 (FunRich 3.1.3) and REACTOME pathway analysis ( https://reactome.org/ ) (Pathan M, et al., 2015). For KEGG pathway analysis, ShinyGO v0.741 ( http://bioinformatics.sdstate.edu/go74/ ) was used. Table 2 Lists of unique proteins of plasma- and CSF-derived EVs associated with MS relapse. PLASMA EV PROTEINS ASSOCIATED WITH RELAPSES Acc. Number Gene Names Protein Names 1 P09543 CNP 2',3'-cyclic-nucleotide 3'-phosphodiesterase 2 P80404 ABAT, 4-aminobutyrate aminotransferase, mitochondrial 3 P12235 SLC25A4 ADP/ATP translocase 1 4 P12236 SLC25A6 ADP/ATP translocase 3 5 P43652 AFM Afamin 6 A8K2U0 A2ML1 Α-2-macroglobulin-like protein 1 7 Q16352 INA Α-internexin 8 P15144 ANPEP, Aminopeptidase N 9 P07355 ANXA2 Annexin A2 10 O95782 AP2A1, AP-2 complex subunit α -1 11 P63010 AP2B1, AP-2 complex subunit β 12 P06727 APOA4 Apolipoprotein A-IV 13 P02656 APOC3 Apolipoprotein C-III 14 Q9UKV3 ACIN1 Apoptotic chromatin condensation inducer in the nucleus 15 Q562R1 ACTBL2 Β-actin-like protein 2 16 Q9UQM7 CAMK2A Calcium/calmodulin-dependent protein kinase type II subunit α 17 Q13554 CAMK2B Calcium/calmodulin-dependent protein kinase type II subunit β 18 P07858 CTSB Cathepsin B 19 P07339 CTSD Cathepsin D 20 P02747 C1QC Complement C1q subcomponent subunit C 21 Q03591 CFHR1 Complement factor H-related protein 1 22 P02741 CRP C-reactive protein 23 P12277 CKB Creatine kinase B-type 24 Q16555 DPYSL2 Dihydropyrimidinase-related protein 2 25 O95147 DUSP14 Dual specificity protein phosphatase 14 26 Q05193 DNM1 Dynamin-1 27 P43004 EAAT2 Excitatory amino acid transporter 2 28 P15311 EZR Ezrin 29 Q01469 FABP5 Fatty acid-binding protein 5 30 P14136 GFAP Glial fibrillary acidic protein 31 P15104 GLUL, GLNS Glutamine synthetase 32 P09471 GNAO1 Guanine nucleotide-binding protein G(o) subunit α 33 P19367 HK1 Hexokinase-1 34 P04908 H2AC4 Histone H2A type 1-B/E 35 P33778 H2BC3 Histone H2B type 1-B 36 P01762 IGHV3-11 Immunoglobulin heavy variable 3–11 37 P01614 IGKV2D-40 Immunoglobulin kappa variable 2D-40 38 Q14643 ITPR1 Inositol 1,4,5-trisphosphate receptor type 1 39 P29622 SERPINA4, Kallistatin 40 P60201 PLP1 Myelin proteolipid protein 41 P28331 NDUFS1 NADH-ubiquinone oxidoreductase 75 kDa subunit, mitochondrial 42 P07196 NFL Neurofilament light polypeptide 43 P07197 NFM Neurofilament medium polypeptide 44 P15309 ACP3 Prostatic acid phosphatase 45 P06702 S100A9 Protein S100-A9 46 P22735 TGM1 Protein-glutamine gamma-glutamyltransferase K 47 P13637 ATP1A3 Sodium/potassium-transporting ATPase subunit α-3 48 Q13813 SPTAN1 Spectrin α chain, non-erythrocytic 1 49 Q01082 SPTBN1 Spectrin β chain, non-erythrocytic 1 50 P60880 SNAP25 Synaptosomal-associated protein 25 51 P21579 SYT1 Synaptotagmin-1 52 P61764 STXBP1 Syntaxin-binding protein 1 53 Q5TAX3 TUT4 Terminal uridylyltransferase 4 54 P07437 TUBB Tubulin β chain 55 Q13509 TUBB3 Tubulin β-3 chain 56 Q9BUF5 TUBB6 Tubulin β-6 chain 57 P63027 VAMP2 Vesicle-associated membrane protein 2 58 P46459 NSF Vesicle-fusing ATPase 59 Q93050 ATP6V0A1 V-type proton ATPase 116 kDa subunit a 1 CSF EV PROTEINS ASSOCIATED WITH RELAPSES Acc. Number Gene Names Protein Names 1 P62258 YWHAE 14-3-3 protein epsilon 2 P68133 ACTA1 Actin, α skeletal muscle 3 P12235 SLC25A4 ADP/ATP translocase 1 4 P05141 SLC25A5, ADP/ATP translocase 2 5 P61204 ARF3 ADP-ribosylation factor 3 6 P02763 ORM1 Α-1-acid glycoprotein 1 7 P08697 SERPINF2 Α-2-antiplasmin 8 P02765 AHSG Α-2-HS-glycoprotein 9 P35609 ACTN2 Α-actinin-2 10 O94973 AP2A2 AP-2 complex subunit α-2 11 P63010 AP2B1 AP-2 complex subunit β 12 P25705 ATP5F1A ATP synthase subunit α, mitochondrial 13 P02730 SLC4A1 Band 3 anion transport protein 14 P01031 C5 Complement C5 15 P05156 CFI Complement factor I 16 P12277 CKB Creatine kinase B-type 17 O75746 SLC25A12 Electrogenic aspartate/glutamate antiporter, mitochondrial 18 P43004 EAAT2 Excitatory amino acid transporter 2 19 Q15485 FCN2 Ficolin-2 20 P14136 GFAP Glial fibrillary acidic protein 21 P09471 GNAO1 Guanine nucleotide-binding protein G(o) subunit α 22 P08238 HSP90AB1 Heat shock protein HSP 90-β 23 P68871 HBB Hemoglobin subunit β 24 P05546 SERPIND1 Heparin cofactor 2 25 P19367 HK1 Hexokinase-1 26 Q14764 MVP Major vault protein 27 Q02978 SLC25A11 Mitochondrial 2-oxoglutarate/malate carrier protein 28 P02686 MBP Myelin basic protein 29 P12882 MYH1 Myosin-1 30 Q9UKX2 MYH2 Myosin-2 31 P11055 MYH3 Myosin-3 32 P12883 MYH7 Myosin-7 33 P13535 MYH8 Myosin-8 34 Q92823 NRCAM Neuronal cell adhesion molecule 35 P08567 PLEK Pleckstrin 36 P12273 PIP Prolactin-inducible protein 37 P31151 S100A7 Protein S100-A7 38 P14618 PKM Pyruvate kinase PKM 39 P30153 PPP2R1A Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A α isoform 40 P05023 ATP1A1 Sodium/potassium-transporting ATPase subunit α-1 41 P50993 ATP1A2 Sodium/potassium-transporting ATPase subunit α-2 42 P13637 ATP1A3 Sodium/potassium-transporting ATPase subunit α-3 43 P38646 HSPA9 Stress-70 protein, mitochondrial 44 P61764 STXBP1 Syntaxin-binding protein 1 45 P68363 TUBA1B Tubulin α-1B chain 46 Q13509 TUBB3 Tubulin β-3 chain 47 P68371 TUBB4B Tubulin β-4B chain 48 P02774 GC Vitamin D-binding protein Results Aiming at identifying peripheral biomarkers associated with MS relapse, as a first step we investigated the proteomic profile of EVs purified from unpaired plasma and CSF samples of RRMS patients. The complete study design is depicted in Fig. 1 . Proteomic characterization of plasma EVs EVs were purified from plasma samples of 11 RRMS patients (6 patients in relapse and 5 patients in remission) and 5 healthy controls by SEC. SEC-purified plasma EVs visualized by TEM, appeared as lipid bilayer enclosed particles that ranged in size from 50 to 700 nm, confirming the validity of the EV isolation protocol (Fig. 2 A, 2 B and 2 C). Qualitative proteomic analysis of SEC-purified plasma EVs was carried out through pre-fractionation of samples by one dimensional SDS-PAGE followed by liquid chromatography-tandem mass spectrometry (LCMS/MS). A repertoire of 250 proteins were identified (Supplementary Table 1) and distributed as indicated in the Venn diagram (Fig. 2 D). The protein list derived from each group is reported in Supplementary Tables 2, 3 and 4. Furthermore, the matrix charts, showing pairwise comparison of shared EV proteins between subjects of the same group, are reported in Supplementary Fig. 1A, 1B and 1C. FunRich 3.1.3 analysis relative to cellular structures and comparison with Vesiclepedia database showed that the total proteins identified in plasma EVs were significantly enriched in extracellular vesicles ( P ≤ 0.001) (Fig. 2 E and 2 F). As shown in Venn diagram (Fig. 2 D), comparison of the EV proteomes among the three groups analysed (relapsing patients, remitting patients and healthy controls) revealed 59 unique proteins associated with the relapsing phase of MS (Table 2 ). By means of DAVID analysis, these 59 proteins, classified into the Cellular Component Gene Ontology (CC GO) term, were significantly enriched in proteins present in the synapsis, axon, mitochondrion, neuronal cell body and myelin sheath (Fig. 3 A). KEGG pathway analysis revealed that most of these proteins are associated with synaptic vesicle cycle, in line with the results of Reactome Pathway analysis, showing the involvement of this protein set in the synaptic transmission pathway (Fig. 3 B and 3 C). Table 2 Proteomic characterization of CSF EVs We next verified whether CSF EVs showed the same protein signature of plasma-derived EVs associated with MS relapses. Qualitative proteomic analysis of SEC-purified CSF EVs from samples of 7 RRMS patients (4 in relapse and 3 in remission), detected a total of 152 proteins (Supplementary Table 5), distributed as reported in Venn diagram (Fig. 4 A). The protein list derived from CSF samples of each group analysed is reported in Supplementary Tables 6 and 7. The matrix charts, showing pairwise comparison of shared EV proteins between relapsing and remitting patients, are reported in Supplementary Fig. 1D and 1E. Similar to the results obtained for the plasma EV proteome, FunRich tool for cellular component and Vesiclepedia database showed that the total identified proteins were significantly enriched in extracellular vesicles ( P ≤ 0.001) (Fig. 4 B). Furthermore, as shown in Venn diagram (Fig. 4 A), 48 unique proteins were associated with relapse (Table 2 ). These proteins, classified into the CC GO term, were significantly associated with neuronal cells, synapsis, axon and mitochondrion as well as with thick filaments of sarcomeres, suggesting a potential involvement of striated muscle in MS pathology during relapses (Fig. 4 C). Interestingly, as shown for plasma-derived EV proteins associated with MS relapses, also for this set of 48 proteins, synaptic transmission pathway and synapse vesicle cycle are among the most significantly enriched pathways obtained by Reactome and KEGG Pathways, respectively ( P < 0.001) (Fig. 4 D and 4 E). The comparison between proteins of CSF and plasma EVs associated with relapse (Fig. 5 A and 5 B), showed ten common proteins (Table 3 ), four of which are involved in synaptic transmission, namely tubulin β-3 chain, AP-2 complex /b unit β, syntaxin-binding protein 1 and excitatory amino-acid transporter 2 (EAAT2). Among these proteins, EAAT2, found in 66% and 75%, of plasma- and CSF-derived EVs, respectively (Table 3 ), is the dominant glutamatergic transporter in the CNS which is mainly expressed by astrocytes and involved in glutamate homeostasis dysfunction, a key feature in MS pathogenesis. 24 , 25 Taking into account that decreased expression of glutamate transporters on astrocyte surface during neuroinflammation may result in excessive extracellular glutamate and neurotoxicity, we have considered EAAT2 worthy of further investigation as potential MS biomarker. The presence of EAAT2 protein in two plasma samples of SEC-purified EVs from relapsing RRMS patients, used for proteomic experiments, was confirmed by western blot analysis (Fig. 5 C). Table 3 Common Plasma and CSF EV proteins associated with Relapses. Acc. Number Gene Names Protein name Plasma Relative frequency CSF Relative frequency 1 Q13509 TUBB3, TUBB4 Tubulin β-3 chain 4 / 6 3 / 4 2 P12235 SLC25A4, AAC1, ANT1 ADP/ATP translocase 1 5 / 6 4 / 4 3 P43004 SLC1A2, EAAT2, GLT1 Excitatory amino acid transporter 2 4 / 6 3 / 4 4 P13637 ATP1A3 Sodium/potassium-transporting ATPase /b unit α-3 5 / 6 3 / 4 5 P12277 CKB, CKBB Creatine kinase B-type 1 / 6 2 / 4 6 P61764 STXBP1, UNC18A Syntaxin-binding protein 1 1 / 6 1 / 4 7 P14136 GFAP Glial fibrillary acidic protein 1 / 6 1 / 4 8 P09471 GNAO1 Guanine nucleotide-binding protein G(o) /b unit α 1 / 6 1 / 4 9 P19367 HK1 Hexokinase-1 1 / 6 1 / 4 10 P63010 AP2B1, ADTB2, CLAPB1 AP-2 complex /b unit β 2 / 6 1 / 4 EAAT2 detection on plasma EV surface Aiming at detecting EAAT2 protein on plasma EV surface in a larger MS patient cohort, a flow cytometry-based approach, suitable to detect low abundant EV subsets, like CNS-derived EVs, in a complex body fluid such as plasma, was established. The sample processing protocols for EV labelling with currently used fluorescent dyes involve the employment of high-speed centrifugation causing the formation of EV aggregates or morphological changes that may lead to erroneous data interpretation. In order to ensure reproducibility of the results, we applied a “no washing” strategy, that does not require isolation or concentration of EVs from plasma samples prior to staining for flow cytometry analysis. After using a gating strategy for EV detection based on physical parameters (size and complexity) (Supplementary Fig. 2A and 2B), a thiol-based fluorescence labelling method (MTG probe) was used 26 , 27 to rapidly and accurately identify the EV population in the 0.5-1 µm gate. The evaluation of the efficiency and the specificity of the MTG binding to EV free-thiol groups are reported in Supplementary Results. The presence of EVs carrying EAAT2 protein (EV-EAAT2) on their surface was assessed in plasma samples of 110 MS patients (42 RRMS patients in relapse, 41 RRMS patients in remission, 27 SPMS patients) and 23 healthy subjects. The flow cytometry results, showing the percentages of EV-EAAT2 in the total plasma EVs, are presented in Fig. 6 A and 6 B, while the related descriptive statistics are reported in Table 4 . One-way ANOVA test revealed statistically significant differences ( P < 0.001) among the four groups, and post hoc Bonferroni analysis showed a statistically significant increase in the percentage of plasma EV-EAAT2 in relapsing RRMS patients compared to remitting RRMS patients, SPMS patients and healthy controls (Fig. 6 A). To explore whether changes in the percentage of plasma EV-EAAT2 are related to different MS phases over time, the presence of EV-EAAT2 was evaluated in paired relapse/remission plasma samples collected from 10 RRMS patients within 12 months of the first plasma sampling (Fig. 6 C). Student's t-test for paired samples showed a significant difference between EV-EAAT2 percentages in relapse and remission ( p = 0,003), confirming the association of plasma EV-EAAT2 with MS relapses. In order to investigate the effect of drug treatment on plasma EV- EAAT2 frequency, flow cytometry results were analysed taking into account whether RRMS patients received DMTs or were untreated, since at least three months. One-way ANOVA showed a statistically significant differences among all groups analysed ( P < 0,0001) (Tale 5). Post hoc Bonferroni revealed that plasma EV-EAAT2 were significantly more frequent in treated relapsing patients compared to treated remitting patients ( P < 0,05) and healthy controls ( P < 0,01), and in untreated relapsing patients compared to both untreated ( P < 0,001), and treated ( P < 0,001) remitting patients and healthy subjects ( P < 0,001) (Fig. 6 D). These findings suggest that relapsing RRMS patients have higher plasma EV-EAAT2 levels than remitting patients irrespective of exposure to DMT (Fig. 6 D and Table 5 ). For each patient group, there were no statistically significant correlations between plasma EV-EAAT2 levels and gender (Supplementary Table 8), age or disease duration (Table 4 ). Plasma EV-EAAT2 levels of RRMS patients in remission correlated positively with the EDSS score ( P = 0,0087; r = 0,4) (Table 4 and Fig. 7 A). Moreover, a trend for a negative correlation between plasma EV-EAAT2 levels of SPMS patients and the EDSS score was found. However, when SPMS patients with EDSS > 3 were considered, plasma EV-EAAT2 levels showed a significant negative correlation with disability ( P = 0,04; r =-0,45) (Fig. 7 B). To verify whether astrocyte-derived EVs expressed EAAT2 protein on their surface, we used the U251 multiform glioblastoma cell line, which expresses TLRs and TNF receptor 1, as an astrocyte-like model responding to inflammatory stimuli. 28 , 29 Flow cytometry analysis detected EAAT2 protein on the EV surface obtained from the culture supernatant of U251 cells; EAAT2 EV levels were similar in untreated cells and in cells treated with different inflammatory stimuli, like LPS, TNF-α and serum starvation (Supplementary Fig. 3). This finding was confirmed by Western blot analysis, showing the presence of EAAT2 protein in U251-derived EVs isolated from culture supernatant of untreated and serum starved cell line, but not in K562-derived EVs, used as negative control (data not shown). Table 4 Plasma EV- EEAT2 in different MS clinical phases RRMS Relapse RRMS Remission SPMS HC Total P - value a Plasma EV- EEAT2 (%) P < 0.001 No. of patients 42 41 27 23 133 Mean (%) 6,7 1,7 3,9 1 3,6 Std. Deviation 6,1 1,2 4,831 0,4 4,7 Std. Error 0,94 0,2 0,9 0,08 0,4 Range 0,2 / 24,6 0,1 / 4,8 0,3 / 17,5 0,57 / 2,4 0,1 / 24,6 EEAT2 correlation (r/P value) Age 0,08 / 0,6 0,2 / 0,15 − 0,3 / 0,07 − 0,1/0,5 - EDSS score -0,1 / 0,4 0,4 / 0,009* -0,16 / 0,4 - - Disease Duration 0,05 / 0,7 0,03 / 0,8 0,3 / 0,9 - - a : one way ANOVA test Table 5 Plasma EV-EAAT2 in RRMS. Relapsing RRMS patients Remission RRMS patients HC Total p-value a with DMT without DMT with DMT without DMT Plasma EV- EEAT2 (%) p < 0.001 N 15 27 24 17 23 106 Mean 5,6 7,3 1,5 2 1 3,5 Std. Deviation 4,4 6,9 1,2 1,1 0,4 4,6 Std. Error 1,1 1,3 0,2 0,3 0,08 0,4 Range 0,2–14 0,5–24,6 0,1–4,8 0,6 − 4,2 0,6-2.4 0,1–24,6 DISCUSSION This study is the first to investigate the proteome of plasma and CSF EVs from RRMS patients aiming at identifying candidate MS biomarkers using a proteomic profiling comparison approach. The main finding is that a set of proteins detected in both plasma and CSF EVs were associated with MS relapses and were significantly enriched in proteins involved in synaptic transmission, which is known to be dysregulated in MS. 30 Interestingly, plasma EVs associated with MS relapses carry several proteins derived from CNS cells, particularly proteins expressed in the synapse, axon and myelin sheet. These findings confirm the release of CNS-derived EVs into the peripheral blood and the power of the strategy adopted here to identify novel candidate disease biomarkers. Among neuronal proteins found in plasma EVs, particularly interesting are synaptotagmin1(SYT1), syntaxin binding protein 1(STXBP1), synaptosome associated protein 25(SNAP25) and vesicle associated membrane protein 2(VAMP2). These proteins are distributed along the axon and form the SNARE complex which is critical for synaptic vesicle fusion and neurotransmitter release. 31 Other proteins found in plasma EVs and expressed in neurons are neurofilament medium and light chains and ATPase Na+/K + transporting subunit α 3 (ATP1A3), an enzyme involved in the action potential propagation during neuronal depolarization. 32 Not only neuronal, but also glial proteins, like GFAP, 33 EAAT2, glutamine synthetase (GLNA), and the major CNS myelin protein, proteolipid protein 1 (PLP1) were detected in plasma EVs. Moreover, we identified mitochondrial proteins involved in the traffic of various solutes across the inner mitochondrial membrane (SLC25A4 and SLC25A6), glucose metabolism (HXK1) and oxidative phosphorylation (ATP5F1A and NDUFS1). Circulating EVs carrying mitochondrial components, classified as mitovesicles, may reflect mitochondrial dysfunction which is thought to have a key role in MS pathogenesis. 34 , 35 Proteomic analysis of CSF EVs associated with MS relapses revealed the presence of proteins derived from the same CNS cellular components found in plasma EVs, such as synapse, axon and mitochondria. Otherwise, proteomic analysis of CSF EVs also detected proteins expressed in peripheral tissues, such as components of the sarcomere (ARF3, ACTA1, MYH2, MYH3, MYH7 and MYH8), thereby confirming the bidirectional EV trafficking between the CNS and the periphery. The presence of circulating EVs carrying sarcomeric proteins might indicate skeletal muscle damage, probably triggered by circulating pro-inflammatory mediators during MS relapses. This suggestion is supported by the observation that histological and molecular changes in skeletal muscle linked to mitochondrial dysfunction occur at disease onset in EAE, a widely used animal model of MS. 36 , 37 In contrast, during disease progression, major changes in the muscle structure leading to motor deficits, could be attributed to impaired axonal conduction resulting from chronic demyelination. 38 Of major relevance for a better understanding of MS pathogenesis is the presence of circulating glia-derived EVs during relapses that may shed further light on the link between neuroinflammation and synaptic dysfunction in MS pathology. During disease exacerbation, studies performed in the EAE model and in MS patients using transcranial magneting stimulation techniques indicate that immune-mediated inflammation is associated not only with CNS demyelination but also with altered synaptic transmission. 39 , 40 , 41 Specifically, neuroinflammation induces an increase of excitatory glutamatergic transmission, a decrease in inhibitory GABAergic transmission, an altered glutamate uptake by astrocytes and a loss of synapses, all of which contribute to diffuse synaptopathy. 40 Glutamatergic synapse dysfunction, caused by an excessive activation of the ionotropic NMDA receptors of glutamate, which can be also produced by inflammatory cells, including activated microglia, as well as reduced glutamate uptake in the synaptic cleft, can lead to excitotoxicity and synaptic loss. Impaired or decreased expression of high-affinity sodium-dependent glutamate transporters, EAATs, particularly EAAT2/GLT1 responsible for the majority of the glutamate uptake in CNS, makes neurons and oligodendrocytes highly susceptible to excitotoxicity. 42 , 25 Studies in EAE models and MS brain lesions, have shown that EAATs, including EAAT2, 43 , 44 , 25 are reduced in CNS, predominantly in astrocyte, and that EAAT downregulation induced by inflammatory stimuli, such as interleukin 1β and TNFα, is associated with altered glutamate uptake. 45 , 46 Experiments in cultured rat astrocytes and the present results in the U251 multiform glioblastoma cell line show that EAAT2 is incorporated in EVs, under physiologic and inflammatory conditions. The evidence of EAATs carried by EVs released from spinal explants after nerve injury to have the ability to uptake extracellular glutamate, along with our proteomic detection of GLNA, responsible for conversion of glutamate to glutamine, suggest that glia-derived EVs, carrying proteins involved in synaptic glutamate clearance, may have a key role in maintaining glutamate homeostasis during neuroinflammation. 47 Owing to its central role in preventing excitotoxicity and its presence in both CSF and plasma EVs during MS relapses, EAAT2 was selected for validation as biomarker of disease activity in RRMS and evaluation in SPMS patients. To this end, we developed a strategy allowing for rapid flow cytometric detection of EAAT2 on plasma EV surface using an efficient and specific fluorescent probe (MTG) that allowed to identify EVs without the need for a purification step before antibody labelling. Screening of plasma samples for the presence of EV-EAAT2 showed a statistically significant increase in the frequency of EV-EAAT2 in relapsing RRMS patients compared to remitting RRMS patients, SPMS patients and healthy controls, regardless of DMT exposure. Further experiments in paired relapse/remission plasma samples collected from RRMS patients, highlighted changes of EV-EAAT2 level associated with different phases of the disease overtime and confirmed the association of plasma EV-EAAT2 with MS relapses. Despite of the small number of samples analysed, our study also provides preliminary evidence of a positive correlation of plasma EV-EAAT2 levels with EDSS score in RRMS during remission and of a negative correlation in SPMS patients with more severe disability (EDSS > 3). There are some limitations related to the current study. A downside of the proteomic approach is that highly abundant proteins can mask the detection of low abundance proteins, especially when EVs purified from plasma samples are analysed. Furthermore, in each subject, plasma EVs, originated from different body districts, with a large diversity of proteins, could differ in their number and protein content. In view of these considerations, the comparison between CSF and plasma EV proteomes associated with MS relapses showed only ten common proteins, although the most of the proteins identified in both proteomes were significantly associated with neuronal cells, synapsis, axon and mitochondrion. Furthermore, sarcomeric proteins were detected in CSF but not in plasma EVs otherwise myelin proteins were found in plasma but not in CSF EVs. Finally, the lack of commercial assays and the difficulty of developing specific assays for detecting proteins carried by EVs prevented us from analysing other EV proteins associated with relapses as potential MS biomarkers. Despite these limitations, the present study highlights EAAT2 as a candidate biomarker for MS relapses. Conclusion Our strategy based on comparison of proteomic signatures of CSF and plasma EVs, purified from samples of RRMS patients, turned out to be appropriate not only for supplying novel biomarkers of MS disease activity, but also to improve the knowledge about the pathological mechanisms underlying the disease. Indeed, the proteomic analysis of both CSF and plasma EV profiles associated with MS relapses revealed several proteins involved in synaptic transmission, which is known to be altered in MS during neuroinflammation. Chosen among ten shared proteins between CSF and plasma EV proteomes associated with MS relapses, EAAT2 on plasma EV surface detected by using a novel and highly reproducible flow cytometry-based approach, showed to be a promising biomarker of MS relapses. Additionally, the plasma EV-EAAT2 levels positively correlated with EDSS score in RRMS during remission and negatively correlated in SPMS patients with more severe disability (EDSS > 3), suggesting the need for more research to evaluate plasma EV-EAAT2 also as potential prognostic biomarker for PIRA and the transition from RRMS towards SPMS. 48 , 49 Moreover, the development of an easy-to use quantitative immunoassay to measure plasma EV-EAAT2 with high sensitivity and specificity would be helpful to evaluate its usefulness in the clinical practice. Abbreviations DMT Disease-modifying therapy EAAT2 Excitatory amino-acid transporter 2 EDSS Expanded Disability Status Scale EVs Extracellular vesicles GFAP Glial fibrillary acidic protein MTG MITO Tracker Green FM MS Multiple sclerosis sNfl soluble Neurofilament-light chain PPP Platelet-poor plasma PIRA Progression independent of relapse activity RRMS Relapsing remitting MS SEC Size-exclusion chromatography SPMS Secondary progressive MS TEM Transmission electron microscopy Declarations Acknowledgments The authors thank Dr Francesca Aloisi for scientific support throughout the course of this work and critical revision of the manuscript. Moreover, we thank Dr. Marco Crescenzi for supporting us in proteomic analysis. Ethical Approval This study was approved by the ethics committees of the ‘La Sapienza’ University of Rome (725/16) and Istituto Superiore di Sanità (174/16). Signed informed consent was obtained from all the enrolled study subjects. Funding This study was supported by co-financing grant: Italian Ministry of Health (Grant number: CO-2013-02359461) and Merck Serono S.p.A. (Grant number: MS 200136_0050). Availability of data The proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the following link: http://massive.ucsd.edu/ProteoSAFe/status.jsp?task=78fca3bcbfa54f8784ea45ec4282c670 Competing interests The authors report no competing interests. References Aloisi F, Giovannoni G, Salvetti M. Epstein-Barr virus as a cause of multiple sclerosis: opportunities for prevention and therapy. Lancet Neurol. 2023; 22:338-349. doi: 10.1016/S1474-4422(22)00471-9. Scalfari A, Neuhaus A, Degenhardt A, et al. The natural history of multiple sclerosis: a geographically based study 10: relapses and long-term disability. Brain. 2010; 133:1914-29. doi: 10.1093/brain/awq118. Chard D, Trip SA. Resolving the clinico-radiological paradox in multiple sclerosis. 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Supplementary Files SUPPLEMENTARYINFORMATION.docx Supplementarytable1.docx Supplementarytable2.docx Supplementarytable3.docx Supplementarytable4.docx Supplementarytable5.docx Supplementarytable6.docx Supplementarytable7.docx Supplementarytable8.docx supplfig1pdf.pdf supplfig2pdf.pdf supplfig3pdf.pdf supplementaryfig.gelandblot.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Journal of Neuroinflammation → Version 1 posted Editorial decision: Revision requested 25 Mar, 2024 Reviews received at journal 05 Mar, 2024 Reviewers agreed at journal 22 Feb, 2024 Reviewers invited by journal 21 Feb, 2024 Editor assigned by journal 31 Jan, 2024 Submission checks completed at journal 30 Jan, 2024 First submitted to journal 29 Jan, 2024 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. 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16:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909260/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12974-024-03148-x","type":"published","date":"2024-09-02T16:05:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50510496,"identity":"d9ae3590-f60f-4a6b-bdea-eb77efb4b64e","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of study design\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/bdd456a1c8d8b085135a91cb.png"},{"id":50510499,"identity":"7e379929-4b24-4130-8101-c2de1f0a6674","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1585724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of SEC-purified plasma EVs\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Representative images of SEC-purified plasma EVs obtained with TEM. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram showing the number of common and unique proteins in relapsing and remitting MS patients and healthy controls. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram showing the total identified proteins compared with total Vesiclepedia database. (\u003cstrong\u003eD\u003c/strong\u003e) Functional gene enrichment analysis of all identified proteins from the FunRich software for cellular component (\u003cem\u003eP\u003c/em\u003e≤ 0.001).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/259492319e437eb56722c28a.png"},{"id":50510503,"identity":"84948e66-2278-44d1-ad44-b981816ae056","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":478140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteome profiling of plasma-derived EVs associated with MS relapses\u003c/strong\u003e. (\u003cstrong\u003eA)\u003c/strong\u003e Cellular Component (CC) GO term enrichment analysis for unique 59 plasma EV proteins associated with the relapsing disease phase. In the boxes, proteins linked to various components obtained by DAVID bioinformatics tool, are listed. (\u003cstrong\u003eB\u003c/strong\u003e) and (\u003cstrong\u003eC\u003c/strong\u003e) Pathways identified by using Reactome and KEGG database, respectively. In the boxes of the (\u003cstrong\u003eB\u003c/strong\u003e) panel, the list of proteins associated with the most significant Reactome pathways, are listed.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/88709fac4611c5f7260ebfbd.png"},{"id":50511020,"identity":"47242c0f-49cf-40eb-96b4-338eec148dd7","added_by":"auto","created_at":"2024-02-01 16:12:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":524673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteome profiling of CSF-derived EVs associated with MS relapses.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Venn diagram showing common and unique proteins of CSF-derived EVs from relapsing and remitting MS patients. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram showing the comparison between total proteins identified in SEC-purified CSF EVs and Vesiclepedia database; FunRich functional analysis results of 152 total proteins related to cellular components. (\u003cstrong\u003eC\u003c/strong\u003e) Cellular Component (CC) GO term enrichment analysis for unique 48 CSF EV proteins associated with relapse. In the boxes, proteins linked to some significant structure components obtained by DAVID bioinformatics tool, are listed. (\u003cstrong\u003eD\u003c/strong\u003e) and (\u003cstrong\u003eE\u003c/strong\u003e) Pathways identified using Reactome and Kegg database, respectively. In the boxes of (\u003cstrong\u003eD\u003c/strong\u003e) panel, proteins associated with some of the more significant Reactome pathways, are listed.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/93043667c96371d2b9dfddea.png"},{"id":50513723,"identity":"93d2aa5e-8f47-4c1e-aec4-9a1bea405698","added_by":"auto","created_at":"2024-02-01 16:28:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":698879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between CSF and plasma EV proteomes associated with MS relapses. (A) \u003c/strong\u003eShared EV proteins belonging to different cellular components.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eB) \u003c/strong\u003eVenn diagram showing ten common proteins, four of which involved in synaptic transmission. (\u003cstrong\u003eC\u003c/strong\u003e) Representative Western blot and SDS-PAGE of SEC-purified EVs from plasma samples of relapsing RRMS patients and healthy controls for EAAT2 detection. Lane 1 and 2: SEC-purified plasma EV samples of 2 healthy controls; Lane: M marker; Lane 3 and 4: SEC-purified plasma EV samples of 2 relapsing RRMS patients.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/d2f4ec34193f917ed17b0c08.png"},{"id":50510507,"identity":"3ee635ac-2887-4b18-8cf3-d360673285c9","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":431044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFACS analysis of EAAT2 on EV surface in plasma of MS patients and healthy controls. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEVs in MTG-positive gate in plasma samples from 42 RRMS patients in relapse, 41 RRMS patients in remission, 27 SPMS patients and 23 healthy subjects. Statistically significant differences among groups were evaluated by one-way ANOVA test followed by Bonferroni post hoc test (* \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Cut-off value (mean+2SD) was calculated. Significance of differences among groups were evaluated by one-way ANOVA test followed by Bonferroni post hoc test. Mean ± standard error of the mean (SEM) values are shown as horizontal lines. (\u003cstrong\u003eB\u003c/strong\u003e) Representative dot plots showing the percentage of EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEVs in MTG-positive gate for each group considered. Isotype antibody was used as control. (\u003cstrong\u003eC\u003c/strong\u003e) EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEVs in MTG-positive gate in plasma samples longitudinally collected from 10 RRMS patients during relapse and remission. Two tailed paired Student’s t- test shows a statistically significant difference (\u003cem\u003eP\u003c/em\u003e=0.003) between the two groups (\u003cstrong\u003eD\u003c/strong\u003e) Comparison of EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEV frequency between RRMS patients with or without DMT treatment. Percentage of EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEVs in MTG-positive gate in plasma samples of RRMS patients under DMT (15 RRMS patients in relapse and 24 RRMS patients in remission), and RRMS patients without therapy (27 RRMS patients in relapse and 17 RRMS patients in remission). Each dot represents an individual subject. Mean ± standard error of the mean (SEM) values are shown as horizontal lines. (*\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/c28bcb6801eb500ca1c9eafb.png"},{"id":50512521,"identity":"fe1dc2fb-dddd-48cf-8dbb-fe0f99fd2762","added_by":"auto","created_at":"2024-02-01 16:20:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":65278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between plasma EAAT2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eEV\u003c/strong\u003e \u003cstrong\u003efrequency and EDSS score. (A\u003c/strong\u003e) Significant positive correlation between plasma EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEV percentage values and EDSS score in RRMS patients in remission. (\u003cstrong\u003eB\u003c/strong\u003e) Trend for a negative correlation between plasma EAAT2+EV percentage values and EDSS score in SPMS patients. (\u003cstrong\u003eC\u003c/strong\u003e) Significant inverse correlation between plasma EAAT2\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003eEV percentage values and EDSS in SPMS patients with EDSS \u0026gt;3 (20 out of 27 SPMS patients). Correlations were determined by Pearson's test using GraphPad Prism 5 software.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/6bcb08f9776c898d633db994.png"},{"id":64185828,"identity":"e81b2734-3a9b-4168-86f2-3e0e3254e7ef","added_by":"auto","created_at":"2024-09-09 16:22:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5967562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/760441a9-33de-4aec-aa6f-733bbd6b6791.pdf"},{"id":50511025,"identity":"9e1beb8c-fb47-42f7-b673-0ceef7fea3bd","added_by":"auto","created_at":"2024-02-01 16:12:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26364,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFORMATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/847b96f018a6d5102f66870b.docx"},{"id":50510497,"identity":"dc8f5259-bea4-42ca-9c60-c5f70a382bcb","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":35112,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/8d2a3037fd844a352a171db2.docx"},{"id":50510508,"identity":"ba122097-5d31-4363-b869-b6cfcaadb624","added_by":"auto","created_at":"2024-02-01 16:04:47","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30152,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/2292c086959c8e85bd1c1605.docx"},{"id":50511024,"identity":"620d7217-31bf-4d58-9035-7b4c4d7dc666","added_by":"auto","created_at":"2024-02-01 16:12:46","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26434,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/5ae5e04c737ff5788e8d6283.docx"},{"id":50511021,"identity":"d0d0cb18-efff-4529-bcea-b62bb967ed1e","added_by":"auto","created_at":"2024-02-01 16:12:46","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":24880,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/12f714e10f9302a624d76f84.docx"},{"id":50510505,"identity":"dc030050-96e0-4e4a-a814-65ea8348b347","added_by":"auto","created_at":"2024-02-01 16:04:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":25238,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/d202e6708e528b6c8de370b4.docx"},{"id":50512524,"identity":"f5731f67-1945-429d-8836-4d34a6df08d9","added_by":"auto","created_at":"2024-02-01 16:20:47","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":24344,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/3018e9e2789a67246e36ebdb.docx"},{"id":50511026,"identity":"4710f57f-a577-41c2-b5cb-7a0bed8ba870","added_by":"auto","created_at":"2024-02-01 16:12:47","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":21651,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable7.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/20400101a6da26c51bcd148b.docx"},{"id":50510509,"identity":"472d1a57-6f54-48e8-81bf-ec9b17565d3d","added_by":"auto","created_at":"2024-02-01 16:04:47","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":12798,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable8.docx","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/3029ac8ab571135525bcc474.docx"},{"id":50510514,"identity":"2048855e-73b9-4b3d-873d-ccca0daa62ff","added_by":"auto","created_at":"2024-02-01 16:04:47","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":216408,"visible":true,"origin":"","legend":"","description":"","filename":"supplfig1pdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/567794c36f02f9aa9abb062f.pdf"},{"id":50512525,"identity":"6607bb5d-b81b-4a2d-ad5c-63d2d20d0198","added_by":"auto","created_at":"2024-02-01 16:20:47","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":545666,"visible":true,"origin":"","legend":"","description":"","filename":"supplfig2pdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/1767295e17e161017feb93e6.pdf"},{"id":50510512,"identity":"6e3825e1-68c0-4a93-8bf8-3209fe3be94d","added_by":"auto","created_at":"2024-02-01 16:04:47","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":100389,"visible":true,"origin":"","legend":"","description":"","filename":"supplfig3pdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/5ecfe89a7ecc776daed3d409.pdf"},{"id":50510516,"identity":"1d0a47f0-98da-41ae-8086-52b5e2f7c11a","added_by":"auto","created_at":"2024-02-01 16:04:47","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":254702,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfig.gelandblot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909260/v1/82dc7941e917d0c5dd908dcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProteomic Profile of Extracellular Vesicles from Plasma and CFS of Multiple Sclerosis Patients Reveals Disease Activity- Associated EAAT2\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic demyelinating and neurodegenerative disease of the CNS affecting young adults, that typically manifests with episodes of transient exacerbations of neurological disability (relapses), followed by partial or total recovery\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. MS relapses, a key feature of relapsing-remitting multiple sclerosis (RRMS), the most prevalent MS phenotype, are defined as occurrence of new symptoms or worsening of old symptoms, not always accompanied by the detection of contrast-enhancing lesions, by MRI. This may be due at least in part to the limited sensitivity of conventional MRI to detect small lesions, particularly in the spinal cord, cortical grey matter and optic nerve.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In the context of clinical sign and symptom worsening, in the absence of the gadolinium-enhancing lesions, and aiming to improve treatment decisions, it is important to distinguish MS relapses from pseudo-relapses, which may be triggered by infection or comorbidities.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e On the other hand, MRI may reveal active lesions without symptoms, indicating a subclinical relapse.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Recent data have shown the impact of effective prevention of relapses on long-term disability.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, there is also evidence of disability accumulation unrelated to relapses in RRMS; this condition, referred to as progression independent of relapse activity (PIRA), is associated with a predominant underlying neurodegenerative component.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven the heterogeneity of MS course and the increasing number of disease-modifying therapies (DMTs) for RRMS, with different safety profiles and efficacy in reducing CNS inflammation and relapse rates, the discovery of peripheral biomarkers that facilitate disease activity assessment and personalized treatment, would greatly improve patient care.\u003c/p\u003e \u003cp\u003eCurrently, soluble neurofilament-light chain (sNfl) and glial fibrillary acidic protein (GFAP) have shown promise as biomarkers of acute disease activity and progression.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e However, there are several limitations to the potential use of these molecules as peripheral biomarkers mainly due to confounding factors, such as age.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Therefore, there is an emerging interest in searching for novel MS biomarkers in order to develop a panel of molecules that might be used in the clinical practice.\u003c/p\u003e \u003cp\u003eGrowing evidence in neurological diseases indicates extracellular vesicles (EVs), an heterogeneous family of extracellular structures bounded by a phospholipid bilayer, released by all CNS cell types in cerebrospinal fluid (CSF), as vehicles of intercellular communication involved in many physiological and pathological processes.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The ability of CNS-derived EVs to cross the blood-brain barrier and entering the peripheral blood, makes them an easily accessible biomarker source of neurological disorders, including MS.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Moreover, EVs, sharing the same antigenic repertoire as their parental cells, may dynamically reflect the pathologic mechanisms underlying CNS damage.\u003c/p\u003e \u003cp\u003eTherefore, EVs, with their molecular constituents more stable than soluble molecules in body fluids, are becoming object of multi-omics investigation not only to reveal novel biomarkers of the disease but also to improve the knowledge of the molecular mechanisms underpinning MS pathogenesis. In this study, we investigated the proteome composition of plasma and CSF EVs obtained from RRMS patients aiming at identifying potential peripheral biomarkers associated with disease activity. We have selected one plasma candidate biomarker associated with relapses and established a novel flow cytometry-based assay for its detection in a larger MS cohort, including RRMS and SPMS patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eIn this multi-center longitudinal study, 110 patients with a diagnosis of MS based on the 2018 revised McDonald criteria,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e were enrolled at the Department of Neuroscience, \u0026lsquo;La Sapienza\u0026rsquo; University of Rome, and the Department of Neuroscience, University of Padua, Italy, between 2017 and 2019. Eighty-three patients had a diagnosis of RRMS (41 with no clinically or radiologically evident relapse for at least 12 months and 42 in acute relapse) and 27 SPMS patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Inclusion criteria for patient enrollment were: age from 18 to 65 years; no comorbidities or infectious diseases and no steroid therapy in the month before blood sampling; no women in pregnancy, lactation, or planning a pregnancy. Patient characterization included a clinical evaluation with EDSS score and MRI assessment. The control group included 23 gender- and age-matched healthy subjects. This study was approved by the ethics committees of the \u0026lsquo;La Sapienza\u0026rsquo; University of Rome (725/16) and Istituto Superiore di Sanit\u0026agrave; (174/16). Signed informed consent was obtained from all the enrolled study subjects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of MS patients and controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRRMS Relapse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRRMS Remission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDemographic characteristics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo. of Patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 /16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 /11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 /12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 /11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83 /50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,9\u0026thinsp;\u0026plusmn;\u0026thinsp;10,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44,3\u0026thinsp;\u0026plusmn;\u0026thinsp;11,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53,6\u0026thinsp;\u0026plusmn;\u0026thinsp;7,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44,9\u0026thinsp;\u0026plusmn;\u0026thinsp;14,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45,2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u0026ndash;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u0026ndash;62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,1\u0026thinsp;\u0026plusmn;\u0026thinsp;3,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,9\u0026thinsp;\u0026plusmn;\u0026thinsp;4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;7,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEDSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,2\u0026thinsp;\u0026plusmn;\u0026thinsp;0,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,4\u0026thinsp;\u0026plusmn;\u0026thinsp;1,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,6\u0026thinsp;\u0026plusmn;\u0026thinsp;1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 \u0026minus;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at\u003c/p\u003e \u003cp\u003eDisease onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37,8\u0026thinsp;\u0026plusmn;\u0026thinsp;10,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37,4\u0026thinsp;\u0026plusmn;\u0026thinsp;9,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;10,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(DMTs) yes/no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 / 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 / 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 / 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood and CSF samples\u003c/h2\u003e \u003cp\u003eBlood samples were collected in sodium citrate tubes (Becton Dickinson, USA) and processed within 60 min from collection to obtain Platelet-Poor Plasma (PPP). CSF specimens were collected by non-traumatic lumbar puncture as previously reported.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e All PPP and CSF samples were aliquoted and kept frozen at -80C until use. Among 83 RRMS patients enrolled in this study, plasma samples from 10 patients were collected at relapse and remission, over a one year period. For EV proteomic analysis, plasma samples from 11 untreated RRMS patients (6 in relapse and 5 in remission) and 5 healthy controls and CSF samples from 7 untreated RRMS patients (4 in relapse and 3 in remission) were used. For flow cytometry analysis, plasma samples from RRMS patients with (n\u0026thinsp;=\u0026thinsp;39) or without (n\u0026thinsp;=\u0026thinsp;44) first-line DMTs for at least 3 months from blood sampling, and 27 untreated SPMS patients, were used. DMTs included interferons, teriflunomide, glatiramer acetate or dimethyl fumarate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSize-exclusion chromatography (SEC)\u003c/h2\u003e \u003cp\u003eFor purification of CSF- and blood-derived EVs by size, individual CSF (4 ml) and PPP (6ml) samples were loaded on to a Sephacryl S-500 gel filtration column (GE Healthcare). EV-containing fractions, evaluated by transmission electron microscopy (TEM), were concentrated by two consecutive centrifugations (Beckman) at 20.000g and 100.000g each for 2 hrs and suspended in PBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTransmission electron microscopy (TEM)\u003c/h2\u003e \u003cp\u003eFractions obtained by SEC EV purification, were deposited and dried onto thin substrates of amorphous carbon and negatively stained with 2% (w/v) phosphotungstic acid. Samples were observed using a Zeiss EM902 transmission electron microscope, operating at 80 kV and equipped with an \u0026ldquo;in column\u0026rdquo; electron energy filter. Images were acquired with a digital charge-coupled device camera, model PROSCAN HSC2 (1 K \u0026Aring;~ 1 K pixels), thermostated by a Peltier unit. Image analysis was performed using the digital analyzer SIS 3.0 and the overall resolution can be estimated in the order of 2 nm. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProteomic analysis\u003c/h2\u003e \u003cp\u003eSEC-purified EVs were loaded on 1D-gel NuPAGE 4\u0026ndash;12% and trypsin digested in 10 contiguous slices cut in each gel lane.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The resulting peptide mixtures were separated by an Ultimate 3000 HPLC (DIONEX, USA) connected with a linear ion trap mass spectrometer (LTQ-XL, ThermoElectron, USA): they were desalted on a trap column (Acclaim PepMap 100 C18, LC Packings, DIONEX) and separated on a 10 cm long column (Silica Tips FS 360-75-8, New Objective, USA) slurry-packed in-house with 5 \u0026micro;m, 200 \u0026Aring; pore size C18 resin (Michrom BioResources, USA). A 50 min gradient from 4 to 80% buffer B (95% acetonitrile and 0.1% formic acid) and buffer A(5% acetonitrile and 0.1% formic acid) was used at 300 nL/min flow rate. MS spectra were acquired from 400 to 2000 m/z in a Top 5 data-dependent mode, with 45s long dynamic exclusion and applying 35% CID for fragmentation. Tandem mass spectra were matched against the Homo Sapiens protein database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org/downloads\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org/downloads\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and through Bioworks software (version 3.3, Thermo Electron). Fully tryptic cleavage constraints (one miss-cleavage allowed), static cysteine carbamidomethylation, and variable methionine oxidation were considered as match parameters and 1.5 and 1 Da were used as mass tolerance for precursor and fragment ions, respectively. For peptide identification cross correlation scores of 1.8, 2.5 and 3 for 1, 2 and 3 peptide charge state, respectively, and peptide probability cut-off of P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 were used. Proteins were identified with at least two peptides.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eSEC-purified plasma EVs and EVs isolated by centrifugation (100.000g for 20 min) from the culture supernatant of U251 multiform glioblastoma and chronic myelogenous leukemia K562 cell lines were used. The protein concentration was determined using the Bradford protein assay (Bio-Rad, USA). Proteins were separated on 10% pre-casted acrylamide gels (Invitrogen, Carlsbad, CA) and transferred to PVDF membranes. The membranes were blocked (5% milk and 0.05% Tween-20) for 2 hrs and incubated overnight with PE-conjugated EAAT2-specific rabbit polyclonal antibody (1\u0026micro;g) (Bioss, USA), as primary antibody. After washing in PBS, the secondary HRP-conjugated anti-rabbit IgG (Sigma-Aldrich) was added. Chemiluminescent detection of proteins was performed using ECL Plus reagent (Amersham).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry gating strategy\u003c/h2\u003e \u003cp\u003eEVs in PPP samples were analyzed using Gallios flow cytometer (Beckman Coulter, USA) after an accurate setting of the physical and fluorescence parameters. In particular, for the correct setting of the gate (based on the size of EVs) and the fluorescence parameters, fluorescent beads of variable size were used (Flow Cytometry Sub-micron Particle Size Reference Kit- Thermo Fischer scientific). The flow cytometer was adjusted to cover the EV size range between 0.5 and 1 \u0026micro;m. Moreover, FCS threshold value was determined to reduce the background noise of the instrument preserving the detection of the EV population of 0.5 \u0026micro;m in size. A routine verification of optical alignment of lasers and fluidic stability of flow cytometer were performed daily with Flow-Check Pro Fluorosphere (Beckman Coulter), according to the manufacturer's instructions. The correct setting of 0.5-1 \u0026micro;m range size for EVs gate was periodically checked. Data were analyzed using Kaluza software 1.2 (Beckman Coulter).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of EVs by MTG labelling\u003c/h2\u003e \u003cp\u003eSEC-purified plasma EVs or EVs in PPP samples were labeled with MITO Tracker Green FM (MTG) (Molecular Probes-Invitrogen) to identify EV populations using a newly developed flow cytometry assay described in Supplementary methods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlow Count Fluorosphere (Beckman Coulter) with a known number of fluorescent beads were utilized for EV quantification, according to manufacturer\u0026rsquo;s instructions. A volume of PPP containing 1x10\u003csup\u003e6\u003c/sup\u003e EVs was diluted in PBS and the analysis of the EAAT2 protein on the EV surface in plasma samples of MS patients and healthy subjects was performed using 1 \u0026micro;g of PE-conjugated EAAT2-specific rabbit polyclonal antibody (Bioss, USA) for 45 min at RT. Then, MTG (100 nM) was added for 15 min at RT before FACS acquisition. To avoid immune complex formation and the unspecific background due to antibody aggregation, each antibody and reagent was centrifuged before use (20.000g for 20 min).\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The amount of antibody used for EV staining was titrated in order to determine the optimal concentration and have a low signal-to-noise ratio. Unstained EVs and/or uncorrelated matching antibody isotype (Bioss Rabbit IgG isotype control, PE conjugated, USA), were used to determine the background fluorescence. EAAT2 measurement was made in triplicate for each sample and the mean values with a standard deviation (SD) less than 10% were used for data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEAAT2 detection on EVs derived from U251 and K562 cell lines\u003c/h2\u003e \u003cp\u003eU251 cell line, provided by Dr. A. Calogero\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and K562 cell line culture conditions and EV isolation from supernatants are reported in Supplementary methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and data analysis\u003c/h2\u003e \u003cp\u003eTo compare data obtained in different patient subgroups, one way ANOVA followed by a post-hoc Bonferroni\u0026rsquo;s correction for multiple comparisons was conducted. The correlation analyses were perfomed by Pearson's index. When comparing two groups Student\u0026rsquo;s t-test and Mann-Withney test were used where appropriate to determine statistical significance. The \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026le;\u0026thinsp;0,05 was considered statistically significant. The SPSS Version 28.0 and Graphpad Prism 5 software were used for statistical analyses. We annotated the identified proteins using the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Complete proteomic data are shown in Supplementary material (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To identify the GO cellular components enriched by a set of proteins or genes, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID), updated on September 22, 2023 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, we performed the Functional enrichment 3.1.3 (FunRich 3.1.3) and REACTOME pathway analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Pathan M, et al., 2015). For KEGG pathway analysis, ShinyGO v0.741 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go74/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go74/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLists of unique proteins of plasma- and CSF-derived EVs associated with MS relapse.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePLASMA EV PROTEINS ASSOCIATED WITH RELAPSES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcc. Number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGene Names\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProtein Names\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2',3'-cyclic-nucleotide 3'-phosphodiesterase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP80404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eABAT,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4-aminobutyrate aminotransferase, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP translocase 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP translocase 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP43652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfamin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA8K2U0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA2ML1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-2-macroglobulin-like protein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ16352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-internexin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP15144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANPEP,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAminopeptidase N\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANXA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnexin A2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO95782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAP2A1,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP-2 complex subunit α -1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP63010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAP2B1,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP-2 complex subunit β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP06727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPOA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApolipoprotein A-IV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPOC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApolipoprotein C-III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9UKV3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACIN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApoptotic chromatin condensation inducer in the nucleus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ562R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTBL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΒ-actin-like protein 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9UQM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAMK2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalcium/calmodulin-dependent protein kinase type II subunit α\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAMK2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalcium/calmodulin-dependent protein kinase type II subunit β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCathepsin B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCathepsin D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC1QC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplement C1q subcomponent subunit C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ03591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFHR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplement factor H-related protein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCreatine kinase B-type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ16555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPYSL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDihydropyrimidinase-related protein 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO95147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDUSP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDual specificity protein phosphatase 14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ05193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynamin-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP43004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcitatory amino acid transporter 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP15311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEZR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEzrin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ01469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFABP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatty acid-binding protein 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP15104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLUL, GLNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlutamine synthetase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNAO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuanine nucleotide-binding protein G(o) subunit α\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP19367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHexokinase-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH2AC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHistone H2A type 1-B/E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP33778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH2BC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHistone H2B type 1-B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP01762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIGHV3-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmunoglobulin heavy variable 3\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP01614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIGKV2D-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmunoglobulin kappa variable 2D-40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ14643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eITPR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInositol 1,4,5-trisphosphate receptor type 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP29622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSERPINA4,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKallistatin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP60201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyelin proteolipid protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP28331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDUFS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNADH-ubiquinone oxidoreductase 75 kDa subunit, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeurofilament light polypeptide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeurofilament medium polypeptide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP15309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProstatic acid phosphatase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP06702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein S100-A9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP22735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein-glutamine gamma-glutamyltransferase K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP1A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium/potassium-transporting ATPase subunit α-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPTAN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectrin α chain, non-erythrocytic 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ01082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPTBN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectrin β chain, non-erythrocytic 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP60880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNAP25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSynaptosomal-associated protein 25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP21579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSYT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSynaptotagmin-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP61764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTXBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSyntaxin-binding protein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ5TAX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerminal uridylyltransferase 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β-3 chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9BUF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β-6 chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP63027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVAMP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVesicle-associated membrane protein 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP46459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVesicle-fusing ATPase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ93050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP6V0A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV-type proton ATPase 116 kDa subunit a 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCSF EV PROTEINS ASSOCIATED WITH RELAPSES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAcc. Number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGene Names\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProtein Names\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP62258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYWHAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14-3-3 protein epsilon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP68133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActin, α skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP translocase 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A5,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP translocase 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP61204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP-ribosylation factor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eORM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-1-acid glycoprotein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP08697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSERPINF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-2-antiplasmin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAHSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-2-HS-glycoprotein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP35609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΑ-actinin-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO94973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAP2A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP-2 complex subunit α-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP63010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAP2B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP-2 complex subunit β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP25705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP5F1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eATP synthase subunit α, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC4A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBand 3 anion transport protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP01031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplement C5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplement factor I\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCreatine kinase B-type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO75746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElectrogenic aspartate/glutamate antiporter, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP43004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAAT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcitatory amino acid transporter 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ15485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFCN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFicolin-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNAO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuanine nucleotide-binding protein G(o) subunit α\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP08238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSP90AB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeat shock protein HSP 90-β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP68871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHemoglobin subunit β\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSERPIND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeparin cofactor 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP19367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHexokinase-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ14764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMajor vault protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ02978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMitochondrial 2-oxoglutarate/malate carrier protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyelin basic protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyosin-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9UKX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyosin-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP11055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyosin-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyosin-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyosin-8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ92823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeuronal cell adhesion molecule\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP08567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePleckstrin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProlactin-inducible protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP31151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein S100-A7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePyruvate kinase PKM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP30153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPP2R1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A α isoform\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium/potassium-transporting ATPase subunit α-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP50993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP1A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium/potassium-transporting ATPase subunit α-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP1A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium/potassium-transporting ATPase subunit α-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP38646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSPA9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStress-70 protein, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP61764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTXBP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSyntaxin-binding protein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP68363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBA1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin α-1B chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β-3 chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP68371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β-4B chain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVitamin D-binding protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAiming at identifying peripheral biomarkers associated with MS relapse, as a first step we investigated the proteomic profile of EVs purified from unpaired plasma and CSF samples of RRMS patients. The complete study design is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProteomic characterization of plasma EVs\u003c/h2\u003e \u003cp\u003eEVs were purified from plasma samples of 11 RRMS patients (6 patients in relapse and 5 patients in remission) and 5 healthy controls by SEC. SEC-purified plasma EVs visualized by TEM, appeared as lipid bilayer enclosed particles that ranged in size from 50 to 700 nm, confirming the validity of the EV isolation protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Qualitative proteomic analysis of SEC-purified plasma EVs was carried out through pre-fractionation of samples by one dimensional SDS-PAGE followed by liquid chromatography-tandem mass spectrometry (LCMS/MS). A repertoire of 250 proteins were identified (Supplementary Table\u0026nbsp;1) and distributed as indicated in the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The protein list derived from each group is reported in Supplementary Tables\u0026nbsp;2, 3 and 4. Furthermore, the matrix charts, showing pairwise comparison of shared EV proteins between subjects of the same group, are reported in Supplementary Fig.\u0026nbsp;1A, 1B and 1C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunRich 3.1.3 analysis relative to cellular structures and comparison with Vesiclepedia database showed that the total proteins identified in plasma EVs were significantly enriched in extracellular vesicles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eAs shown in Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), comparison of the EV proteomes among the three groups analysed (relapsing patients, remitting patients and healthy controls) revealed 59 unique proteins associated with the relapsing phase of MS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By means of DAVID analysis, these 59 proteins, classified into the Cellular Component Gene Ontology (CC GO) term, were significantly enriched in proteins present in the synapsis, axon, mitochondrion, neuronal cell body and myelin sheath (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). KEGG pathway analysis revealed that most of these proteins are associated with synaptic vesicle cycle, in line with the results of Reactome Pathway analysis, showing the involvement of this protein set in the synaptic transmission pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProteomic characterization of CSF EVs\u003c/h2\u003e \u003cp\u003eWe next verified whether CSF EVs showed the same protein signature of plasma-derived EVs associated with MS relapses. Qualitative proteomic analysis of SEC-purified CSF EVs from samples of 7 RRMS patients (4 in relapse and 3 in remission), detected a total of 152 proteins (Supplementary Table\u0026nbsp;5), distributed as reported in Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The protein list derived from CSF samples of each group analysed is reported in Supplementary Tables\u0026nbsp;6 and 7. The matrix charts, showing pairwise comparison of shared EV proteins between relapsing and remitting patients, are reported in Supplementary Fig.\u0026nbsp;1D and 1E. Similar to the results obtained for the plasma EV proteome, FunRich tool for cellular component and Vesiclepedia database showed that the total identified proteins were significantly enriched in extracellular vesicles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, as shown in Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), 48 unique proteins were associated with relapse (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These proteins, classified into the CC GO term, were significantly associated with neuronal cells, synapsis, axon and mitochondrion as well as with thick filaments of sarcomeres, suggesting a potential involvement of striated muscle in MS pathology during relapses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Interestingly, as shown for plasma-derived EV proteins associated with MS relapses, also for this set of 48 proteins, synaptic transmission pathway and synapse vesicle cycle are among the most significantly enriched pathways obtained by Reactome and KEGG Pathways, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The comparison between proteins of CSF and plasma EVs associated with relapse (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), showed ten common proteins (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), four of which are involved in synaptic transmission, namely tubulin β-3 chain, AP-2 complex /b unit β, syntaxin-binding protein 1 and excitatory amino-acid transporter 2 (EAAT2). Among these proteins, EAAT2, found in 66% and 75%, of plasma- and CSF-derived EVs, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), is the dominant glutamatergic transporter in the CNS which is mainly expressed by astrocytes and involved in glutamate homeostasis dysfunction, a key feature in MS pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Taking into account that decreased expression of glutamate transporters on astrocyte surface during neuroinflammation may result in excessive extracellular glutamate and neurotoxicity, we have considered EAAT2 worthy of further investigation as potential MS biomarker.\u003c/p\u003e \u003cp\u003eThe presence of EAAT2 protein in two plasma samples of SEC-purified EVs from relapsing RRMS patients, used for proteomic experiments, was confirmed by western blot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommon Plasma and CSF EV proteins associated with Relapses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcc. Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProtein name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003cp\u003eRelative\u003c/p\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003cp\u003eRelative\u003c/p\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTUBB3, TUBB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTubulin β-3 chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A4, AAC1, ANT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP translocase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP43004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC1A2, EAAT2, GLT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcitatory amino acid transporter 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATP1A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSodium/potassium-transporting ATPase /b unit α-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCKB, CKBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCreatine kinase B-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP61764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTXBP1, UNC18A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSyntaxin-binding protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNAO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuanine nucleotide-binding protein G(o) /b unit α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP19367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHexokinase-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP63010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAP2B1, ADTB2, CLAPB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP-2 complex /b unit β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 / 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 / 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEAAT2 detection on plasma EV surface\u003c/h2\u003e \u003cp\u003eAiming at detecting EAAT2 protein on plasma EV surface in a larger MS patient cohort, a flow cytometry-based approach, suitable to detect low abundant EV subsets, like CNS-derived EVs, in a complex body fluid such as plasma, was established. The sample processing protocols for EV labelling with currently used fluorescent dyes involve the employment of high-speed centrifugation causing the formation of EV aggregates or morphological changes that may lead to erroneous data interpretation.\u003c/p\u003e \u003cp\u003eIn order to ensure reproducibility of the results, we applied a \u0026ldquo;no washing\u0026rdquo; strategy, that does not require isolation or concentration of EVs from plasma samples prior to staining for flow cytometry analysis. After using a gating strategy for EV detection based on physical parameters (size and complexity) (Supplementary Fig.\u0026nbsp;2A and 2B), a thiol-based fluorescence labelling method (MTG probe) was used \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e to rapidly and accurately identify the EV population in the 0.5-1 \u0026micro;m gate. The evaluation of the efficiency and the specificity of the MTG binding to EV free-thiol groups are reported in Supplementary Results.\u003c/p\u003e \u003cp\u003eThe presence of EVs carrying EAAT2 protein (EV-EAAT2) on their surface was assessed in plasma samples of 110 MS patients (42 RRMS patients in relapse, 41 RRMS patients in remission, 27 SPMS patients) and 23 healthy subjects. The flow cytometry results, showing the percentages of EV-EAAT2 in the total plasma EVs, are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, while the related descriptive statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. One-way ANOVA test revealed statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the four groups, and post hoc Bonferroni analysis showed a statistically significant increase in the percentage of plasma EV-EAAT2 in relapsing RRMS patients compared to remitting RRMS patients, SPMS patients and healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore whether changes in the percentage of plasma EV-EAAT2 are related to different MS phases over time, the presence of EV-EAAT2 was evaluated in paired relapse/remission plasma samples collected from 10 RRMS patients within 12 months of the first plasma sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Student's t-test for paired samples showed a significant difference between EV-EAAT2 percentages in relapse and remission (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,003), confirming the association of plasma EV-EAAT2 with MS relapses. In order to investigate the effect of drug treatment on plasma EV- EAAT2 frequency, flow cytometry results were analysed taking into account whether RRMS patients received DMTs or were untreated, since at least three months. One-way ANOVA showed a statistically significant differences among all groups analysed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,0001) (Tale 5). Post hoc Bonferroni revealed that plasma EV-EAAT2 were significantly more frequent in treated relapsing patients compared to treated remitting patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,05) and healthy controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,01), and in untreated relapsing patients compared to both untreated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001), and treated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001) remitting patients and healthy subjects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0,001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These findings suggest that relapsing RRMS patients have higher plasma EV-EAAT2 levels than remitting patients irrespective of exposure to DMT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor each patient group, there were no statistically significant correlations between plasma EV-EAAT2 levels and gender (Supplementary Table\u0026nbsp;8), age or disease duration (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Plasma EV-EAAT2 levels of RRMS patients in remission correlated positively with the EDSS score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,0087; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,4) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, a trend for a negative correlation between plasma EV-EAAT2 levels of SPMS patients and the EDSS score was found. However, when SPMS patients with EDSS\u0026thinsp;\u0026gt;\u0026thinsp;3 were considered, plasma EV-EAAT2 levels showed a significant negative correlation with disability (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,04; \u003cem\u003er\u003c/em\u003e=-0,45) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo verify whether astrocyte-derived EVs expressed EAAT2 protein on their surface, we used the U251 multiform glioblastoma cell line, which expresses TLRs and TNF receptor 1, as an astrocyte-like model responding to inflammatory stimuli.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Flow cytometry analysis detected EAAT2 protein on the EV surface obtained from the culture supernatant of U251 cells; EAAT2 EV levels were similar in untreated cells and in cells treated with different inflammatory stimuli, like LPS, TNF-α and serum starvation (Supplementary Fig.\u0026nbsp;3). This finding was confirmed by Western blot analysis, showing the presence of EAAT2 protein in U251-derived EVs isolated from culture supernatant of untreated and serum starved cell line, but not in K562-derived EVs, used as negative control (data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlasma EV- EEAT2 in different MS clinical phases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRRMS Relapse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRRMS Remission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e- value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlasma EV- EEAT2 (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,2 / 24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,1 / 4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,3 / 17,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,57 / 2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,1 / 24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEEAT2\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ecorrelation (r/P value)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,08 / 0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2 / 0,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0,3 / 0,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0,1/0,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0,1 / 0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,4 / \u003cb\u003e0,009*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-0,16 / 0,4 -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,05 / 0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,03 / 0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,3 / 0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003ea\u003c/b\u003e: one way ANOVA test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlasma EV-EAAT2 in RRMS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRelapsing RRMS patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRemission RRMS patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith\u003c/p\u003e \u003cp\u003eDMT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewithout\u003c/p\u003e \u003cp\u003eDMT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewith\u003c/p\u003e \u003cp\u003eDMT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewithout\u003c/p\u003e \u003cp\u003eDMT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlasma\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eEV- EEAT2 (%)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStd. Deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStd. Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,2\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,5\u0026ndash;24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,1\u0026ndash;4,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,6\u0026thinsp;\u0026minus;\u0026thinsp;4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,6-2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,1\u0026ndash;24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study is the first to investigate the proteome of plasma and CSF EVs from RRMS patients aiming at identifying candidate MS biomarkers using a proteomic profiling comparison approach. The main finding is that a set of proteins detected in both plasma and CSF EVs were associated with MS relapses and were significantly enriched in proteins involved in synaptic transmission, which is known to be dysregulated in MS.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Interestingly, plasma EVs associated with MS relapses carry several proteins derived from CNS cells, particularly proteins expressed in the synapse, axon and myelin sheet. These findings confirm the release of CNS-derived EVs into the peripheral blood and the power of the strategy adopted here to identify novel candidate disease biomarkers. Among neuronal proteins found in plasma EVs, particularly interesting are synaptotagmin1(SYT1), syntaxin binding protein 1(STXBP1), synaptosome associated protein 25(SNAP25) and vesicle associated membrane protein 2(VAMP2). These proteins are distributed along the axon and form the SNARE complex which is critical for synaptic vesicle fusion and neurotransmitter release.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Other proteins found in plasma EVs and expressed in neurons are neurofilament medium and light chains and ATPase Na+/K\u0026thinsp;+\u0026thinsp;transporting subunit α 3 (ATP1A3), an enzyme involved in the action potential propagation during neuronal depolarization.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Not only neuronal, but also glial proteins, like GFAP, \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e EAAT2, glutamine synthetase (GLNA), and the major CNS myelin protein, proteolipid protein 1 (PLP1) were detected in plasma EVs. Moreover, we identified mitochondrial proteins involved in the traffic of various solutes across the inner mitochondrial membrane (SLC25A4 and SLC25A6), glucose metabolism (HXK1) and oxidative phosphorylation (ATP5F1A and NDUFS1). Circulating EVs carrying mitochondrial components, classified as mitovesicles, may reflect mitochondrial dysfunction which is thought to have a key role in MS pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProteomic analysis of CSF EVs associated with MS relapses revealed the presence of proteins derived from the same CNS cellular components found in plasma EVs, such as synapse, axon and mitochondria. Otherwise, proteomic analysis of CSF EVs also detected proteins expressed in peripheral tissues, such as components of the sarcomere (ARF3, ACTA1, MYH2, MYH3, MYH7 and MYH8), thereby confirming the bidirectional EV trafficking between the CNS and the periphery. The presence of circulating EVs carrying sarcomeric proteins might indicate skeletal muscle damage, probably triggered by circulating pro-inflammatory mediators during MS relapses. This suggestion is supported by the observation that histological and molecular changes in skeletal muscle linked to mitochondrial dysfunction occur at disease onset in EAE, a widely used animal model of MS.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e In contrast, during disease progression, major changes in the muscle structure leading to motor deficits, could be attributed to impaired axonal conduction resulting from chronic demyelination.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOf major relevance for a better understanding of MS pathogenesis is the presence of circulating glia-derived EVs during relapses that may shed further light on the link between neuroinflammation and synaptic dysfunction in MS pathology.\u003c/p\u003e \u003cp\u003eDuring disease exacerbation, studies performed in the EAE model and in MS patients using transcranial magneting stimulation techniques indicate that immune-mediated inflammation is associated not only with CNS demyelination but also with altered synaptic transmission. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Specifically, neuroinflammation induces an increase of excitatory glutamatergic transmission, a decrease in inhibitory GABAergic transmission, an altered glutamate uptake by astrocytes and a loss of synapses, all of which contribute to diffuse synaptopathy.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGlutamatergic synapse dysfunction, caused by an excessive activation of the ionotropic NMDA receptors of glutamate, which can be also produced by inflammatory cells, including activated microglia, as well as reduced glutamate uptake in the synaptic cleft, can lead to excitotoxicity and synaptic loss. Impaired or decreased expression of high-affinity sodium-dependent glutamate transporters, EAATs, particularly EAAT2/GLT1 responsible for the majority of the glutamate uptake in CNS, makes neurons and oligodendrocytes highly susceptible to excitotoxicity.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudies in EAE models and MS brain lesions, have shown that EAATs, including EAAT2, \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e are reduced in CNS, predominantly in astrocyte, and that EAAT downregulation induced by inflammatory stimuli, such as interleukin 1β and TNFα, is associated with altered glutamate uptake.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eExperiments in cultured rat astrocytes and the present results in the U251 multiform glioblastoma cell line show that EAAT2 is incorporated in EVs, under physiologic and inflammatory conditions.\u003c/p\u003e \u003cp\u003eThe evidence of EAATs carried by EVs released from spinal explants after nerve injury to have the ability to uptake extracellular glutamate, along with our proteomic detection of GLNA, responsible for conversion of glutamate to glutamine, suggest that glia-derived EVs, carrying proteins involved in synaptic glutamate clearance, may have a key role in maintaining glutamate homeostasis during neuroinflammation.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOwing to its central role in preventing excitotoxicity and its presence in both CSF and plasma EVs during MS relapses, EAAT2 was selected for validation as biomarker of disease activity in RRMS and evaluation in SPMS patients.\u003c/p\u003e \u003cp\u003eTo this end, we developed a strategy allowing for rapid flow cytometric detection of EAAT2 on plasma EV surface using an efficient and specific fluorescent probe (MTG) that allowed to identify EVs without the need for a purification step before antibody labelling.\u003c/p\u003e \u003cp\u003eScreening of plasma samples for the presence of EV-EAAT2 showed a statistically significant increase in the frequency of EV-EAAT2 in relapsing RRMS patients compared to remitting RRMS patients, SPMS patients and healthy controls, regardless of DMT exposure. Further experiments in paired relapse/remission plasma samples collected from RRMS patients, highlighted changes of EV-EAAT2 level associated with different phases of the disease overtime and confirmed the association of plasma EV-EAAT2 with MS relapses.\u003c/p\u003e \u003cp\u003eDespite of the small number of samples analysed, our study also provides preliminary evidence of a positive correlation of plasma EV-EAAT2 levels with EDSS score in RRMS during remission and of a negative correlation in SPMS patients with more severe disability (EDSS\u0026thinsp;\u0026gt;\u0026thinsp;3).\u003c/p\u003e \u003cp\u003eThere are some limitations related to the current study. A downside of the proteomic approach is that highly abundant proteins can mask the detection of low abundance proteins, especially when EVs purified from plasma samples are analysed. Furthermore, in each subject, plasma EVs, originated from different body districts, with a large diversity of proteins, could differ in their number and protein content.\u003c/p\u003e \u003cp\u003eIn view of these considerations, the comparison between CSF and plasma EV proteomes associated with MS relapses showed only ten common proteins, although the most of the proteins identified in both proteomes were significantly associated with neuronal cells, synapsis, axon and mitochondrion. Furthermore, sarcomeric proteins were detected in CSF but not in plasma EVs otherwise myelin proteins were found in plasma but not in CSF EVs. Finally, the lack of commercial assays and the difficulty of developing specific assays for detecting proteins carried by EVs prevented us from analysing other EV proteins associated with relapses as potential MS biomarkers. Despite these limitations, the present study highlights EAAT2 as a candidate biomarker for MS relapses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur strategy based on comparison of proteomic signatures of CSF and plasma EVs, purified from samples of RRMS patients, turned out to be appropriate not only for supplying novel biomarkers of MS disease activity, but also to improve the knowledge about the pathological mechanisms underlying the disease. Indeed, the proteomic analysis of both CSF and plasma EV profiles associated with MS relapses revealed several proteins involved in synaptic transmission, which is known to be altered in MS during neuroinflammation.\u003c/p\u003e \u003cp\u003eChosen among ten shared proteins between CSF and plasma EV proteomes associated with MS relapses, EAAT2 on plasma EV surface detected by using a novel and highly reproducible flow cytometry-based approach, showed to be a promising biomarker of MS relapses. Additionally, the plasma EV-EAAT2 levels positively correlated with EDSS score in RRMS during remission and negatively correlated in SPMS patients with more severe disability (EDSS\u0026thinsp;\u0026gt;\u0026thinsp;3), suggesting the need for more research to evaluate plasma EV-EAAT2 also as potential prognostic biomarker for PIRA and the transition from RRMS towards SPMS.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMoreover, the development of an easy-to use quantitative immunoassay to measure plasma EV-EAAT2 with high sensitivity and specificity would be helpful to evaluate its usefulness in the clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisease-modifying therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAAT2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExcitatory amino-acid transporter 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExpanded Disability Status Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlial fibrillary acidic protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMITO Tracker Green FM\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esNfl\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esoluble Neurofilament-light chain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-poor plasma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression independent of relapse activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelapsing remitting MS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSize-exclusion chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSecondary progressive MS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransmission electron microscopy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr Francesca Aloisi for scientific support throughout the course of this work and critical revision of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, we thank Dr. Marco Crescenzi for supporting us in proteomic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committees of the \u0026lsquo;La Sapienza\u0026rsquo; University of Rome (725/16) and Istituto Superiore di Sanit\u0026agrave; (174/16). Signed informed consent was obtained from all the enrolled study subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by co-financing grant: Italian Ministry of Health (Grant number:\u0026nbsp;CO-2013-02359461) and Merck Serono S.p.A. (Grant number:\u0026nbsp;MS 200136_0050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the following link:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttp://massive.ucsd.edu/ProteoSAFe/status.jsp?task=78fca3bcbfa54f8784ea45ec4282c670\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAloisi F, Giovannoni G, Salvetti M. Epstein-Barr virus as a cause of multiple sclerosis: opportunities for prevention and therapy. Lancet Neurol. 2023; 22:338-349. doi: 10.1016/S1474-4422(22)00471-9. \u003c/li\u003e\n\u003cli\u003eScalfari A, Neuhaus A, Degenhardt A, et al. The natural history of multiple sclerosis: a geographically based study 10: relapses and long-term disability. Brain. 2010; 133:1914-29. doi: 10.1093/brain/awq118.\u003c/li\u003e\n\u003cli\u003eChard D, Trip SA. Resolving the clinico-radiological paradox in multiple sclerosis. F1000Res. 2017; 6:1828. doi: 10.12688/f1000research.11932.1\u003c/li\u003e\n\u003cli\u003eHickman SJ. Optic nerve imaging in multiple sclerosis. J Neuroimaging. 2007; 17:42S\u0026ndash;45S. doi: 10.1111/j.1552-6569.2007.00136.x.\u003c/li\u003e\n\u003cli\u003eKearney H, Miller DH, Ciccarelli O. Spinal cord MRI in multiple sclerosis\u0026ndash;diagnostic, prognostic and clinical value. Nat Rev Neurol. 2015; 11: 327\u0026ndash;338. doi: 10.1038/nrneurol.2015.80.\u003c/li\u003e\n\u003cli\u003eAvasarala J. Redefining acute relapses in multiple sclerosis: implications for phase 3 clinical trials and treatment algorithms. Innov. Clin. 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Mult Scler Relat Disord. 2020; 44:102242. doi: 10.1016/j.msard.2020.102242.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multiple sclerosis, extracellular vesicles, comparative proteomics, synaptic transmission pathway, disease activity","lastPublishedDoi":"10.21203/rs.3.rs-3909260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Objectives\u003c/h2\u003e \u003cp\u003eThere is an urgent need to discover blood-based biomarkers of multiple sclerosis (MS) to better define the underlying biology of relapses and monitor disease progression. The main goal of this study is to search for candidate biomarkers of MS relapses associated with circulating extracellular vesicles (EVs), an emerging tool for biomarker discovery.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEVs, purified from unpaired plasma and CSF samples of RRMS patients by size-exclusion chromatography (SEC), underwent qualitative proteomic analysis to discover novel biomarkers associated with MS relapses. The candidate biomarkers of disease activity were detected by comparison approach between plasma- and CSF-EV proteomes associated with relapses. Among them, a selected potential biomarker was evaluated in a cohort of MS patients, using a novel and highly reproducible flow cytometry-based approach in order to detect low abundant EV subsets in a complex body fluid such as plasma.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe proteomic profiles of both SEC-purified plasma EVs (from 6 patients in relapse and 5 patients in remission) and SEC-puirified CSF EVs (from 4 patients in relapse and 3 patients in remission) revealed a set of proteins associated with MS relapses significant enriched in the synaptic transmission pathway. Among common proteins, excitatory amino-acid transporter 2, EAAT2, responsible for the majority of the glutamate uptake in CNS, was worthy of further investigation. By screening plasma samples from 110 MS patients, we found a significant association of plasma EV-carried EAAT2 protein (EV-EAAT2) with MS relapses, regardless of disease-modifying therapies. This finding was confirmed by investigating the presence of EV-EAAT2 in plasma samples collected longitudinally from 10 RRMS patients, during relapse and remission. Moreover, plasma EV-EAAT2 levels correlated positively with Expanded Disability Status Scale (EDSS) score in remitting MS patients but showed a negative correlation in patients with secondary progressive (SPMS) and EDSS\u0026thinsp;\u0026gt;\u0026thinsp;3.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results emphaticize the usefulness of plasma EVs as a source of accessible biomarkers to remotely analyse the CNS status. Plasma EV-EAAT2 showed to be a promising biomarker for MS relapses. Further studies are required to assess the clinical relevance of this biomarker also for disability progression independent of relapse activity and transition from RRMS towards SPMS.\u003c/p\u003e","manuscriptTitle":"Proteomic Profile of Extracellular Vesicles from Plasma and CFS of Multiple Sclerosis Patients Reveals Disease Activity- Associated EAAT2","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-01 16:04:41","doi":"10.21203/rs.3.rs-3909260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-26T02:15:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-05T15:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"05b0a477-965a-472e-9128-dc8a2c2194e7","date":"2024-02-22T17:25:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-21T15:01:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-31T06:44:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-31T02:09:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2024-01-29T16:47:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d979b45-f70e-4331-835a-1f47a9824a42","owner":[],"postedDate":"February 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:12:49+00:00","versionOfRecord":{"articleIdentity":"rs-3909260","link":"https://doi.org/10.1186/s12974-024-03148-x","journal":{"identity":"journal-of-neuroinflammation","isVorOnly":false,"title":"Journal of Neuroinflammation"},"publishedOn":"2024-09-02 16:05:24","publishedOnDateReadable":"September 2nd, 2024"},"versionCreatedAt":"2024-02-01 16:04:41","video":"","vorDoi":"10.1186/s12974-024-03148-x","vorDoiUrl":"https://doi.org/10.1186/s12974-024-03148-x","workflowStages":[]},"version":"v1","identity":"rs-3909260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909260","identity":"rs-3909260","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}