Next-Generation Proteomics of Brain Extracellular Vesicles in Schizophrenia Provide New Clues on the Altered Molecular Connectome

Citation: Lorca, C.; Fernández-Rhodes, M.; Sánchez Milán, J.A.; Mulet, M.; Elortza, F.; Ramos-Miguel, A.; Callado, L.F.; Meana, J.J.; Mur, M.; Batalla, I.; et al. Next-Generation Proteomics of Brain Extracellular Vesicles in Schizophrenia Provide New Clues on the Altered Molecular Connectome. Biomedicines 2024,12, 129. https://doi.org/ 10.3390/biomedicines12010129 Academic Editors: Meenakshi Ahluwalia, Kumar Vaibhav, Pankaj Gaur and Pankaj Ahluwalia Received: 20 December 2023 Revised: 30 December 2023 Accepted: 5 January 2024 Published: 8 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biomedicines Article Next-Generation Proteomics of Brain Extracellular Vesicles in Schizophrenia Provide New Clues on the Altered Molecular Connectome Cristina Lorca 1,2,† , María Fernández-Rhodes 1,2,†, Jose Antonio Sánchez Milán1,2, María Mulet 1,2, Félix Elortza 3, Alfredo Ramos-Miguel 4,5,6 , Luis F. Callado 4,5,6 , J. Javier Meana 4,5,6 , Maria Mur 7, Iolanda Batalla 7, Elisabet Vilella 6,8,* , Aida Serra 2,*,‡ and Xavier Gallart-Palau 1,9,*,‡ 1Biomedical Research Institute of Lleida Dr. PifarréFoundation (IRBLLEIDA), Neuroscience Area, +Pec Proteomics Research Group (+PPRG), University Hospital Arnau de Vilanova (HUAV), 80 Av. Rovira Roure, 25198 Lleida, Spain; cristinalor[email protected] (C.L.); [email protected] (M.F.-R.); [email protected] (J.A.S.M.); [email protected] (M.M.) 2Department of Medical Basic Sciences, Biomedical Research Institute of Lleida Dr. Pifarré Foundation (IRBLLEIDA), +Pec Proteomics Research Group (+PPRG), University of Lleida (UdL), 25198 Lleida, Spain 3Proteomics Platform, CIC bioGUNE, Basque Research and Technology Alliance (BRTA), CIBERehd, Science and Technology Park of Bizkaia, 48160 Derio, Spain; [email protected] 4Department of Pharmacology, University of the Basque Country UPV/EHU, 48940 Leioa, Spain; [email protected] (A.R.-M.); [email protected] (L.F.C.); javier[email protected] (J.J.M.) 5Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Spain 6Centro de Investigación Biomédica en Red en Salud Mental CIBERSAM, Instituto de Salud Carlos III, 43206 Reus, Spain 7Psychiatry Department, Hospital Universitari Santa Maria, Medicine Department, Universitat de Lleida (UdL), 25198 Lleida, Spain; [email protected] (M.M.); [email protected] (I.B.) 8Hospital Universitari Institut Pere Mata, Institut InvestigacióSanitària Pere Virgili (IISPV)-CERCA, Universitat Rovira i Virgili, 43206 Reus, Spain 9Department of Psychology, University of Lleida (UdL), 25001 Lleida, Spain *Correspondence: [email protected] (E.V.); [email protected] (A.S.); [email protected] (X.G.-P.) †These authors contributed equally to this work. ‡These authors contributed equally to this work. Abstract: Extracellular vesicles (EVs) are tiny membranous structures that mediate intercellular communication. The role(s) of these vesicles have been widely investigated in the context of neurological diseases; however, their potential implications in the neuropathology subjacent to human psychiatric disorders remain mostly unknown. Here, by using next-generation discovery-driven proteomics, we investigate the potential role(s) of brain EVs (bEVs) in schizophrenia (SZ) by analyzing these vesicles from the three post-mortem anatomical brain regions: the prefrontal cortex (PFC), hippocampus (HC), and caudate (CAU). The results obtained indicate that bEVs from SZ-affected brains contain region-specific proteins that are associated with abnormal GABAergic and glutamatergic transmission. Similarly, these vesicles from the analyzed regions were implicated in synaptic decay, abnormal brain immunity, neuron structural imbalances, and impaired cell homeostasis. Our findings also provide evidence, for the first time, that networks of molecular exchange (involving the PFC, HC, and CAU) are potentially active and mediated by EVs in non-diseased brains. Additionally, these bEV-mediated networks seem to have become partially reversed and largely disrupted in the brains of subjects affected by SZ. Taken as a whole, these results open the door to the uncovering of new biological markers and therapeutic targets, based on the compositions of bEVs, for the benefit of patients affected by SZ and related psychotic disorders. Keywords: extracellular vesicles; molecular exchange; neuroinflammation; systems biology; immunoglobulins; brain antibodies; psychiatry; psychotic spectrum Biomedicines 2024,12, 129. https://doi.org/10.3390/biomedicines12010129 https://www.mdpi.com/journal/biomedicines Biomedicines 2024,12, 129 2 of 20 1. Introduction Schizophrenia (SZ) is a severe mental disorder that involves acute psychoticism and is estimated to affect 21 million people worldwide [ 1 ]. Individuals diagnosed with SZ commonly show impairment of the superior abilities of thinking, speech, emotional regulation, and social cognition [ 2 ]. The prevalence of the disorder shows a global rising tendency and involves a high disability rate for the affected individuals [ 1 ]. Thus, the personal and social burden of SZ is devastating in multiple facets, and nearly one-half of the subjects present refractory forms of the disease [ 3 ], which hardly respond to any available pharmacological treatments. Multiple epidemiological culprits seem to be implicated in the origin of the disorder, including genetic, biologic, and environmental factors [ 3 ]. Early childhood trauma, the accumulation of traumatic events, and drug abuse are considered highly relevant factors in the erosion of the human capacity of resilience and in triggering first-episode psychosis (FEP), a stage of illness often preceding SZ [ 4 ]. Indeed, several large-scale genetic studies have already been performed to decipher the highly heritable rate of the disorder [ 5 , 6 ]. Multiple genetic variants, including common variants and rare mutations, have been associated with SZ. Common genetic variants, including those with weak or no statistically significant association, can explain more than 20% of the liability to the disorder [ 5 ]. Rare mutations in genes such as SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1 have also been identified to substantially increase the risk of SZ, as previously reviewed [ 7 ], and large copy number variants (CNVs) with large effect sizes have also been implicated in the disorder [ 8 ]. Lastly, new findings about genetic loci linked to SZ susceptibility have shed light on the pathophysiology of the condition [9]. Similarly, neurochemical attempts to characterize the molecular basis of the disorder have also been conducted, and the alteration of the dopaminergic brain networks have been encountered [ 10 , 11 ]. SZ seems to also be associated with states of increased inflammation and oxidative stress, as well as with alterations of the innate immunity [ 12 ]. At a wider scope, patients with SZ present neuroanatomical changes, including a progressive loss of brain gray matter, which appears to be consistent with increased aberrant pruning and a consequent decreased number of cortical glutamatergic synapses [ 13 ]. Similarly, patients with SZ display white matter microstructural abnormalities across multiple brain regions, with a significant reduction in fractional anisotropy and widespread connectivity deficits, indicating that the disorder may result from an impaired brain connectome [ 14 ]. Although connectome alterations involving key neuroanatomical structures, such as the prefrontal cortex (PFC), hippocampus (HC), and caudate (CAU), have been largely identified in the brains of subjects with SZ [ 15 ], by using in vivo imaging approaches, any feasible molecular substrate for these connectome alterations remains elusive. Extracellular vesicles (EVs) are tiny vesicles that act as intercellular carriers and actively participate in molecular exchange and communication within the central nervous system [ 16 ]. We, and other colleagues, have largely studied these vesicles using systems biology and have described their implications in multiple neurophysiological functions associated with aging-degenerative dementias and its therapeutic potential [ 17 – 21 ]. Proteome-wide analyses have shown that EV biogenesis is altered during preclinical AD, with the presence of specific proteins such as MHC class-type markers, prion protein (PrP), and amyloid protein (APP) [ 17 ]. Changes in the levels of AD-related proteins involved in vesicle endocytosis and the secretory pathways have been observed in both preclinical and symptomatic AD cases [ 17 , 18 ]. Furthermore, EVs from AD patients have been found to contain altered levels of cytokines, pS396 tau, A β 1-42, and microRNA, which may reflect disease severity [ 22 , 23 ]. EVs have also been proposed as excellent platforms of nanovectors in neurologic diseases given their capacity to cross the blood–brain barrier [ 19 , 24 , 25 ]. It follows, hence, that these vesicles could play crucial functions in the altered brain connectome that is extensively found in SZ, while their role(s) in that disorder are still mostly unknown [26]. Biomedicines 2024,12, 129 3 of 20 In order to shed light on the potential functions of EVs in the brain connectome of SZ, we perform here an in-depth characterization of the proteome compositions of post-mortem brain EVs (bEVs) from three different neuroanatomical regions that are broadly implicated in illness-related alterations in brain connectivity. The obtained data reflect the potential roles of these vesicles within the complexity of the psychiatric disorder and point towards the participation of bEVs in active brain networks of molecular exchange that become specifically altered in SZ. 2. Materials and Methods 2.1. Chemicals and Reagents All chemicals and solvents were purchased from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise specified. Water and acetonitrile (ACN) of liquid chromatography (HPLC) grade were purchased from Thermo Fisher Scientific (Thermo Fisher Chemical, Waltham, MA, USA). Sequencing-grade modified trypsin was purchased from Promega (Madison, WI, USA). 2.2. Human Brain Samples All post-mortem brain specimens were collected during autopsies performed between 2010 and 2018 at the Basque Institute of Legal Medicine (Bilbao, Spain). Only samples with post-mortem delay < 24 h were included. Grey matter specimens from the dorsolateral PFC (DLPFC, approximating Brodmann area (BA) 9), hippocampus (HC), and caudate nucleus were carefully dissected, avoiding the white matter, and immediately stored at −80 ◦C until assay. Retrospective search into subjects’ medical records was conducted for antemortem diagnoses of SZ meeting DSM-IV or ICD-10 criteria. Clinical diagnoses of SZ were all performed by a board-certified psychiatrist of the Basque Healthcare System (Osakidetza). Cases with additional psychiatric or neurologic diagnoses, including a history of substance abuse, were excluded. Samples from 15 SZ cases meeting the above criteria were assayed in the present study. Each SZ case was paired to a matched (control, C) subject, matching case’s sex, age, and post-mortem delay, and with no evidence of psychiatric or neurological conditions, according to available antemortem medical records. Demographic characteristics of all C and SZ subjects are summarized in Table 1. Table 1. Details of the human post-mortem brains analyzed in this study. a PMD refers to post-mortem delay expressed in hours. Subject Gender Age at Death (y.o.) PMD a(h) Clinical Group 1 M 58 6 SZ 2 M 57 3 C 3 M 51 18 SZ 4 M 50 2 C 5 M 58 24 SZ 6 M 58 20 C 7 M 58 16 SZ 8 M 56 15 C 9 M 60 17 SZ 10 M 60 14 C 11 F 46 12 SZ 12 F 48 21 C 13 M 43 6 SZ 14 M 44 21 C 15 F 50 14 SZ 16 F 49 18 C 17 M 63 20 SZ 18 M 63 20 C 19 M 48 13 SZ 20 M 47 20 C Biomedicines 2024,12, 129 4 of 20 Table 1. Cont. Subject Gender Age at Death (y.o.) PMD a(h) Clinical Group 21 M 60 7 SZ 22 M 60 19 C 23 M 52 24 SZ 24 M 52 23 C 25 M 52 11 SZ 26 M 51 16 C 27 F 52 10 SZ 28 F 51 10 C 29 M 41 17 SZ 30 M 41 15 C 2.3. Preparation of Brain Tissues Prior to EVs Obtention Brain tissues from each respective and previously detailed neuroanatomical region and subject were dissected, and any remaining meninges and large blood vessels were carefully inspected and removed. Dissected tissues were subsequently washed three times with 1 × PBS for 30 min. Brain tissues from each neuroanatomical region and subject were randomly pooled to form three independent biological group replicates (n= 5; ~150 mg) per condition and neuroanatomical region, as we previously indicated [ 18 , 27 ]. Homogenization of dissected brain tissues was performed as previously described by our group [ 16 ] by using a Bullet Blender tissue homogenizer (Next Advance, Raymertown, NY, USA). Briefly, each sample was suspended in safe-lock tubes in a detergent-free homogenization buffer consisting of 100 mM ammonium acetate (AA) at pH 6.5 (500 µ L), supplemented with protease inhibitor, and mixed with homogenization beads (150 mg, 0.9–2.00 mm magnetic particles) that were previously washed three times with 1 × PBS for 30 min. Homogenization was then conducted in four cycles of 5 min each. At the end of each cycle, the homogenate was centrifuged at 15,000 × gfor 10 min, and the supernatants were collected. Afterwards, 300 µ L of homogenization buffer was added, and the process was repeated. The intensity of the first two cycles was medium, and maximum intensity was used in the last two cycles. All of the procedures were performed at 4 ◦C. 2.4. Enrichment of Brain EVs by PROSPR bEVs were enriched from the detergent-free brain homogenates by using PROSPR as we previously detailed [ 16 ]. Briefly, EV-containing homogenates were mixed with a fourfold volume of chilled acetone ( − 20 ◦ C), vortexed and centrifuged at 5000 × gfor <1 min. Supernatants containing the hydrophobic EV fraction were then concentrated to near-dryness using a vacuum concentrator (Eppendorf AG, Hamburg, Germany) and stored at −80 ◦C until further use. 2.5. Characterization of Brain EVs using Nanoparticle Tracking Analysis bEV fractions obtained from subjects with SZ and C subjects were subjected to indepth characterization using a nanoparticle tracking analysis (NTA) as we previously described [ 18 ]. Briefly, bEV preparations were visualized and analyzed using a Nanosight NS300 with a sCMOS camera (Malvern Panalytical, Malvern, UK). Analysis parameters were set as follows: 60 s acquisition time, camera level 4, slider shutter of 50, slider gain of 100, FPS 32.5, syringe pump speed of 100, total volume per sample of 1 mL, viscosity of 0.906–0.910 cP, and temperature of ~24 ◦ C. NTA was performed without establishing any restricted areas to the image fields by allowing for imaging and analysis of the full sample content. 2.6. Ultrastructural Characterization of Brain EVs Representative bEVs fractions from SZ and C subjects were mounted on Cu-Formvarcarbon grids and kept for 20 min at room temperature (RT). Grids were then washed with Biomedicines 2024,12, 129 5 of 20 HPLC water, and bEV preparations were fixed by using 1% glutaraldehyde in PBS for 5 min. The bEVs were subsequently stained with uranyl oxalate for 5 min, embedded in methyl-cellulose-uranyl-oxalate, and subsequently dried for permanent preservation. Electron micrographs were then imaged by using a Jeol Jem 1010 electron microscope at 80 kV . The obtained ultrastructural micrographs were then scale-calibrated, bi-leveled, and further analyzed by using the open software ImageJ (National Institutes of Health (NIH), Bethesda, MD, USA). 2.7. Processing of Brain EVs for Next-Generation Label-Free Proteomics bEV samples were suspended in a lysis buffer composed of 16 M urea in 100 mM ammonium bicarbonate (ABB), subsequently incubated for 20 min at RT, and finally diluted 1:1 with HPLC water, as we previously indicated [ 17 ]. The bEV proteomes were then subjected to tryptic digestion, as previously described [ 19 ]. Briefly, bEV proteins were reduced using 10 mM dithiothreitol at 30 ◦ C for 3 h, followed by alkylation using 20 mM iodoacetamide (IAA) for 1 h at room temperature in darkness. Next, urea was diluted to <1 M with 25 mM ABB, and the proteins were digested with trypsin overnight at 30 ◦ C, with a 1:20 protein-to-enzyme ratio (w/w). Acidification with 0.5% final concentration of formic acid (FA) was used to quench the reaction. Waters Sep-Pak 50 mg C18 cartridges (Waters, Milford, MA, USA) were then used for to desalt the peptides, and peptide recovery was achieved by using a 70% ACN concentrated buffer. Finally, eluted peptides were concentrated by using a vacuum concentrator. 2.8. Four-Dimensional Proteomics of Brain EVs Desalted bEVs and digested proteome samples were resuspended in 0.1% FA prior to LC-MS/MS analysis by using an EVOSEP liquid chromatographic instrument (EVOSEP, Odense, Denmark) at 300 nL · min −1 . Samples were run by performing an 88 min gradient (15 samples per day). The EVOSEP liquid chromatographer was coupled online to the state-of-the-art timsTOF Pro mass spectrometer (Bruker Daltonics, Billerica, MA, USA), and the samples were analyzed by using four-dimensional (4D) parallel accumulation–serial fragmentation (PASEF) data acquisition, as previously detailed [19,28]. 2.9. Bioinformatics and Data Analysis Bioinformatic analysis of the obtained bEV 4D proteomics raw data was carried out, as previously indicated [ 29 ], by using the specialized proteomics suite software PEAKS Studio X (Bioinformatics Solutions INC., Waterloo, Canada). Precursor ion tolerance was set to 10 ppm, and fragment ion tolerance was set to 0.05 Da. Trypsin was set as proteolytic enzyme for database searching, and carbamidomethylation of Cys residues was set as fixed modification. The human Uniprot database (downloaded on 3 February 2023, containing 140,065 protein sequences) was used for the identification of proteins. Decoy fusion, FDR < 1% , was established for protein identification in all samples, and trypsin with cleavage on at least one end was set as a proteolytic enzyme. The bEV proteome data were exported to Microsoft Excel CSV files, and in-house-generated macros were created and used for further analysis. Label-free relative quantification of bEV proteins between conditions and brain regions was performed based on spectral count, as previously reported [ 30 – 33 ], and the equality of variances was assessed using Levene’s test. The referred data were analyzed using parametric two-way ANOVA and by performing multiple comparisons and Fisher’s Least Significant Difference tests, with statistical significance set at p< 0.05 (95% confidence interval). Afterwards, additional Bonferroni correction for multiple comparisons was applied (p< 0.05) . Proteins present in all three regions analyzed were subjected to correlation analysis, and only Pearson’s correlation coefficients ≥ ±0.8 were considered as indicators of strong interaction between the analyzed variables. Additionally, Bioconductor “org.Hs.eg.db” package (version 3.16.0) and “clusterProfiler” package (version 4.6.2) were installed in R software (version 4.2.3) for Gene Ontology Biomedicines 2024,12, 129 6 of 20 functional analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. 2.10. Data Availability All proteomics data generated for this study have been made publicly available through the specialized repository, PRIDE, with the following identifier: PXD042732. The free availability and re-usage of human brain generated systems biology data, such as these in this study, have been encouraged by the scientific community to openly contribute to the progress of the neuroscientific understanding of the human brain and its diseases [34]. 3. Results 3.1. Morphometric Characteristics of bEVs in Schizophrenia To initially define the specific properties of bEVs, we performed an in-depth morphometric characterization of these vesicles in the brains of the controls (C) and of the subjects with SZ. The bEVs showed a mean concentration of 1.14 × 10 12 particles/mL and an average diameter size of 255.73 nm (Figure 1A). Regarding the diameter size, there were no discernible variations between the groups under analysis; nevertheless, the mean concentration of bEVs showed a significant difference, with the C group’s bEVs exhibiting a greater concentration of particles (Figure 1A). Subsequently, we performed an ultrastructural analysis of bEVs using transmission electron microscopy (TEM) to define the predominant morphology and purity of the analyzed bEV preparations. As shown in the representative micrographs of Figure 1B, the EVs obtained showed predominant spherical morphology without any apparent differences observed between the C and subjects with SZ. Of note, the ultrastructural study also confirmed the absence of any appreciable particle contamination in the obtained bEV preparations (Figure 1B). Biomedicines 2024, 12, x FOR PEER REVIEW 7 of 21 Figure 1. Morphological and ultrastructural characterization of brain extracellular vesicles (bEVs) obtained from post-mortem brain tissues. (A) Average size distribution profiles of bEVs obtained using nanoparticle tracking analysis (NTA) of bEVs from the prefrontal cortex (PFC) region of controls (C) and subjects with schizophrenia (SZ). Captures refer to the average distribution obtained from ten independent size/concentration distribution runs. Standard deviation of the mean is shaded in blue. (B) Representative micrographs of bEVs, obtained by transmission electron microscopy (TEM) from the PFC, hippocampus (HC), and caudate (CAU) regions of C subjects (upper micrographs) and of subjects with SZ (lower micrographs). Scale bar in Figure 1B represents 100 nm. ** indicates significant statistical differences (p < 0.0001). 3.2. Molecular Compositions of bEVs in Schizophrenia We then performed a four-dimensional unbiased discovery-driven characterization of the obtained bEV proteomes to define the molecular composition(s) of these vesicles in the three analyzed brain regions (PFC, HC, and CAU) from the C group and subjects with Figure 1. Cont. Biomedicines 2024,12, 129 7 of 20 Biomedicines 2024, 12, x FOR PEER REVIEW 7 of 21 Figure 1. Morphological and ultrastructural characterization of brain extracellular vesicles (bEVs) obtained from post-mortem brain tissues. (A) Average size distribution profiles of bEVs obtained using nanoparticle tracking analysis (NTA) of bEVs from the prefrontal cortex (PFC) region of controls (C) and subjects with schizophrenia (SZ). Captures refer to the average distribution obtained from ten independent size/concentration distribution runs. Standard deviation of the mean is shaded in blue. (B) Representative micrographs of bEVs, obtained by transmission electron microscopy (TEM) from the PFC, hippocampus (HC), and caudate (CAU) regions of C subjects (upper micrographs) and of subjects with SZ (lower micrographs). Scale bar in Figure 1B represents 100 nm. ** indicates significant statistical differences (p < 0.0001). 3.2. Molecular Compositions of bEVs in Schizophrenia We then performed a four-dimensional unbiased discovery-driven characterization of the obtained bEV proteomes to define the molecular composition(s) of these vesicles in the three analyzed brain regions (PFC, HC, and CAU) from the C group and subjects with Figure 1. Morphological and ultrastructural characterization of brain extracellular vesicles (bEVs) obtained from post-mortem brain tissues. (A) Average size distribution profiles of bEVs obtained using nanoparticle tracking analysis (NTA) of bEVs from the prefrontal cortex (PFC) region of controls (C) and subjects with schizophrenia (SZ). Captures refer to the average distribution obtained from ten independent size/concentration distribution runs. Standard deviation of the mean is shaded in blue. (B) Representative micrographs of bEVs, obtained by transmission electron microscopy (TEM) from the PFC, hippocampus (HC), and caudate (CAU) regions of C subjects (upper micrographs) and of subjects with SZ (lower micrographs). Scale bar in Figure 1B represents 100 nm. ** indicates significant statistical differences (p< 0.0001). 3.2. Molecular Compositions of bEVs in Schizophrenia We then performed a four-dimensional unbiased discovery-driven characterization of the obtained bEV proteomes to define the molecular composition(s) of these vesicles in the three analyzed brain regions (PFC, HC, and CAU) from the C group and subjects with SZ (Supplementary Datasets S1 and S2). The obtained bEV proteomes data were subsequently matched to the data curated in the specialized EV database repositories, Exocarta and Vesiclepedia, as we previously indicated [ 16 , 17 ], to define the quality of the bEV preparations and identify any potential differences regarding the portion of exosomes and microvesicles present. Consistent identification between 70 and 90 percent of the top 100 exosomal and microvesicle markers, respectively curated in these databases, was achieved throughout the analyzed groups and brain regions (Figure 2A,B). Additionally, no significant differences were observed regarding the presence of EV markers between the analyzed groups and brain regions (Figure 2A,B). Biomedicines 2024,12, 129 8 of 20 Biomedicines 2024, 12, x FOR PEER REVIEW 9 of 21 Figure 2. Molecular characterization performed by four-dimensional liquid chromatography proteomics of brain extracellular vesicles (bEVs) of the three analyzed brain regions, prefrontal cortex (PFC), hippocampus (HC), and caudate (CAU). (A,B) Presence of microvesicle markers in the proteomes of bEVs obtained from subjects with schizophrenia (SZ) and control (C) subjects. Parallel analysis of the obtained proteome data was performed in (A) with the top 100 protein markers curated in the specialized microvesicle data repository, Vesiclepedia, and in (B), it was performed with the specialized exosomal data repository, Exocarta. (C) Diagram table indicating the number of common and unique proteins present in the bEV proteomes of the analyzed brain regions, PFC, HC, and CAU, from C subjects and subjects with SZ. Salmon tones represent common proteins to the PFC region. Purple tones represent common proteins to the HC region, and blue tones represent common proteins to the CAU region. ALL indicates the number of proteins commonly present in the three evaluated regions, and ANY indicates the total number of proteins considering all identified proteins between the three evaluated regions. (D) Mean average cumulative proteome levels in bEVs Figure 2. Molecular characterization performed by four-dimensional liquid chromatography proteomics of brain extracellular vesicles (bEVs) of the three analyzed brain regions, prefrontal cortex (PFC), hippocampus (HC), and caudate (CAU). (A,B) Presence of microvesicle markers in the proteomes of bEVs obtained from subjects with schizophrenia (SZ) and control (C) subjects. Parallel analysis of the obtained proteome data was performed in (A) with the top 100 protein markers curated in the specialized microvesicle data repository, Vesiclepedia, and in (B), it was performed with the specialized exosomal data repository, Exocarta. (C) Diagram table indicating the number of common and unique proteins present in the bEV proteomes of the analyzed brain regions, PFC, HC, and CAU, from C subjects and subjects with SZ. Salmon tones represent common proteins to the PFC region. Purple tones represent common proteins to the HC region, and blue tones represent common proteins to the CAU region. ALL indicates the number of proteins commonly present in the three evaluated regions, and ANY indicates the total number of proteins considering all identified proteins between the three evaluated regions. (D) Mean average cumulative proteome levels in bEVs of the three analyzed brain regions, PFC, HC, and CAU, from C subjects and subjects with SZ. *** indicates statistical significance at p< 0.001. Error bars represent standard deviation of the mean. Biomedicines 2024,12, 129 9 of 20 The presence of common and unique specific proteins across groups and brain regions was also analyzed in the profiled bEV preparations (Figure 2C). No differences regarding the number of proteins present in bEV proteomes throughout the analyzed groups were detected, with a total of 1258 proteins being identified in the bEV proteomes of the C subjects and 1252 proteins being identified in the bEV proteomes of subjects with SZ (Figure 2C; Supplementary Datasets S1 and S2). However, whereas 672 proteins were identified as common to all brain regions in the bEV proteomes of the C subjects, only 537 were encountered in the bEV proteomes of the subjects with SZ (Figure 2C). Of note, 201 proteins of these that are common to all brain regions were unique to the bEVs of the C subjects, whilst 66 proteins were unique to the bEVs from the subjects with SZ (Figure 2C). Similarly, 57 proteins were uniquely identified in the PFC-bEVs, 206 proteins were uniquely identified in the HC-bEVs, and 86 proteins were uniquely identified in the CAU-bEVs of subjects with SZ (Figure 2C). Complete lists of these common and unique proteins identified in bEVs are, respectively, included in Supplementary Dataset S3 and Supplementary Tables S1–S5. The total proteome bEVs levels were also analyzed in the three brain regions of the C group and subjects with SZ. A significant upregulation affecting the total proteome of bEVs in the PFC of subjects with SZ was observed (Figure 2D), whilst the bEV proteomes from the rest of brain regions scrutinized did not show significant alterations. 3.3. Schizophrenia-Linked Alteration of bEV Proteomes We then analyzed whether some of the bEV proteins in these subjects alter their levels throughout the three analyzed brain regions. The proteins that were identified with significantly altered regulation in these analyses are detailed in Figure 3A. These proteins, hereafter referred to as SZ-altered bEV proteins, were subsequently subjected to functional categorization considering their role(s) in specific molecular functions and biological processes, as shown in Figure 3B,C. We found that larger portions of these SZaltered bEV proteins contribute to cell growth and homeostatic maintenance as biological processes and to the maintenance of cell structure as molecular function (Figure 3B). A large subset of these proteins was also associated with the malfunction of cell metabolism, brain immunity, and calcium homeostasis (Figure 3C). Specifically, as shown in Figure 3A, the significant downregulation of structural proteins in bEVs from subjects with SZ, including actin (ACTA1), tubulins (TUBA1A, TUBA1B, TUBA1C, TUBB4A, TUBB6, and TUBB8B), cell adhesion (NCAM1), and microtubule-linked proteins (MAP1B, MAP4, MAP6, and MAPT), was observed. Of note, abnormal downregulation in the bEVS of subjects with SZ was also affecting essential structural and vesicular proteins associated with active synaptic densities and spines, including bassoon (BSN), synaptopodin (SYNPO), synapsins (SYN1 and SYN2), and SNAP 25 (Figure 3A). Conversely, the upregulation of immunoglobulins (IGHA1, IGHA2, IHKC, IGHG1, IGLC2, IGLC3, IGHG4, and IGHG2) was clearly identified in the disordered bEV proteomes of subjects with SZ (Figure 3A). Worthy of note, these identified bEV protein alterations from subjects with SZ were primordially observed in the PFC bEV proteomes and were not mirrored in the other brain regions that were analyzed (Figure 3A). Finally, it is worthy of mention that significant downregulations of the myelin protein (MBP) and the astroglial marker (GFAP) were also observed in the bEV proteomes of the subjects with SZ (Figure 3A). A functional categorization of the significantly altered bEV proteomes of the subjects with SZ was also performed, as shown in Figure 3D. The upregulation of a subset of bEV proteins involved in immunity, antigen binding, and cellular transport was predominant in the PFC region of subjects with SZ (Figure 3D). Furthermore, the upregulation of a subset of bEV proteins involved in cell motility, cell maintenance, and cell structure was also predominant in the HC region of subjects with SZ (Figure 3D). Similarly, the upregulation of a high portion of bEV proteins involved in the maintenance of cell structure, cell growth and maintenance, cellular calcium homeostasis, cellular metabolism, and cellular transport Biomedicines 2024,12, 129 16 of 20 also contribute to explain the dichotomy that has been found in the brains of subjects with SZ regarding the hyperexcitability of the HC region [ 50 ] and the hypoexcitability of the PFC region [51], which is a fact that we also consider to be extremely worth further exploring. Our data also indicate an exacerbated upregulation of antibody immunoglobulins in the bEVs of the PFC region in SZ brains. These molecules are typically produced by B cells and kept away from the central nervous system (CNS) in normal neurologic conditions [ 52 ]. Moreover, its presence within the CNS has been associated with the apparition of psychotic symptomatology secondary to autoimmune diseases [ 53 , 54 ]. Although the presence of auto-antibodies in the brains of subjects affected by SZ has been previously reported, the mechanisms involved in specific blood–brain barrier permeability for these molecules remains unknown [ 55 ]. The fact that bEVs become a source that explains the arrival mechanism and increase in immunoglobulins in the PFC of brains affected by SZ, based on the findings here, is highly compelling, and after further validation, it holds promise to contribute to identifying new therapeutic targets for the disorder. Although bEVs seem to undoubtedly implicate in brain interregional molecular exchange, as recently indicated [ 35 ], clear evidence of that outcome in humans is still lacking. Of note, the findings reported in this study strongly support this relevant fact, for the first time, both in healthy and diseased brains. This study has shown that there is interaction affecting the levels of certain proteins that are commonly identified in bEVs in the three analyzed brain regions. This finding was interpreted as the uncovering of a potential active network of molecular exchange mediated by bEVs in the analyzed brains. Furthermore, our data show that bEVs in the CAU region show active exchange potentiality with HC and PFC in the cognitively normal brain, which is a significant discovery that needs more verification because it may have significant effects on how the brain communicates between its cells and domains under normal circumstances. Strikingly, we also observed that the potential active networks of molecular exchange between the three analyzed regions become altered in the analyzed brains from subjects with SZ, thus indicating that these active networks mediated by bEVs become disrupted in the disorder. Specifically, the myelin protein, MBP; the collapsin, DPYSL2; and the Tau protein, MAPT, were the relevant proteins clearly identified to be involved in that disrupted brain network mediated by EVs. All of these referred proteins were thus showing reversed strong correlations with the bEVs of subjects with SZ compared to those of C subjects. 5. Conclusions Collectively, the findings reported in this study identify crucial proteins linked to the bEVs in SZ and reinforce the initial hypothesis about the implication of these vesicles in the neuropathology of the disorder. Additionally, our research identifies specific protein markers in brain EVs with the ability to circulate in biological fluids. These markers may prove to be useful in stratifying clinical patients and correlating the symptomatology of their disorders with the predominance of neuropathology in specific brain regions. The obtained results also contribute to the evidence suggesting that bEVs orchestrate molecular exchange in the human brain, introducing the novelty that alterations to this process occur in psychiatric diseases, particularly SZ. As such, these vesicles might also constitute the brain’s blueprint for the connectome changes previously discovered by neuroimaging studies. Lastly, they offer a fresh and extremely valuable source of potential therapeutic targets for the illness, which, altogether, merits further research. Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/biomedicines12010129/s1, Supplementary Dataset S1: List of identified proteins in bEVs of C brains in each brain region. Supplementary Dataset S2: List of identified proteins in bEVs of SZ samples in each brain region. Supplementary Dataset S3: List of identified proteins in bEVs of control and SZ samples common to all brain regions; Supplementary Dataset S4: Data of the connectome analysis shown in Figure 4. On the right, average and standard deviation (SD) for every significative protein of bEVs consistently identified in the three analyzed anatomical brain regions. On the left, correlation matrix. Table S1: Proteins exclusively identified in Biomedicines 2024,12, 129 17 of 20 brain extracellular vesicles (bEVs) of subjects affected by schizophrenia (SZ) in all analyzed brain regions. Table S2: Proteins exclusively identified in brain extracellular vesicles (bEVs) of control (C) subjects in all analyzed brain regions. Table S3: Proteins exclusively identified in bEVs of the PFC region in SZ. Table S4: Proteins exclusively identified in bEVs of the HC in SZ. Table S5: Proteins exclusively identified in bEVs of the CAU in SZ. Author Contributions: Conceptualization: C.L., M.F.-R., A.R.-M., E.V., A.S. and X.G-P.; Methodology: C.L., M.F.-R., J.A.S.M., M.M. (María Mulet), F.E., M.M. (Maria Mur) and I.B.; Writing: C.L., M.F.-R., A.S. and X.G.-P.; Brain Samples: A.R.-M., L.F.C. and J.J.M.; Review and Editing: E.V., A.S. and X.G.-P.; Funding Acquisition: A.S. and X.G.-P.; Supervision: A.S. and X.G.-P. All authors have read and agreed to the published version of the manuscript. Funding: Support for this work was provided by the National Institute of Health/Instituto de Salud Carlos III-ISCIII, Spain (PI22/00443 to X.G.-P.) (grant co-funded by the European Union); the Ministry of Science and Innovation-MCIN, Spain and the National Research Council/Agencia Estatal de Investigación-AEI, Spain (PID2020-114885RB-C21 to A.S.) funded by MCIN/AEI/10.13039/501100011033. This research was also co-financed by the Spanish Ministry of Science and Innovation with funds from the European Union NextGenerationEU; from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1); and from the Autonomous Community of Catalonia within the framework of the Biotechnology Plan Applied to Health ((EVBRAINTARGET-Y7340-ACPPCCOL007 to X.G.-P., A.S., M.Mur, and A.R.-M.) coordinated by the Institute for Bioengineering of Catalonia (IBEC)); the Diputacióde Lleida, Spain (PIRS22/03 to X.G.-P. & I.B. and PIRS23/02 to A.S.); the Catalan Research Council-AGAUR (AGAUR 21SGR010065 to E.V.; 2023 LLAV 00056 to X.G.-P.; and 2022 DI 100 to X.G.-P.); and the Basque Government (IT211/19 and IT1512/22 to J.J.M and L.F.C.). X.G.- P. acknowledges a Miguel Servet program tenure track contract (CP21/00096) from the ISCIII, awarded on the 2021 call under the Health Strategy Action, co-funded by the European Union (FSE+). A.S. acknowledges a Ramón y Cajal program tenure track contract (RYC2021-030946-I) funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”; A.R.-M. acknowledges a Ramón y Cajal program tenure track contract (RYC-2016-19282) funded by MCIN/AEI/10.13039/501100011033. M.F.-R.’s postdoctoral contract is funded by PRTR-C17.I1 and EVBRAINTARGET-Y7340-ACPPCCOL007. C.L.’s PhD is funded by the European Social Fund for the recruitment of predoctoral researchers (PEJD-2019-PRE/BIO-16475); M.M.’s PhD is funded by the MCIN-AEI (PR2021-097934); and J.A.S.M.’s PhD is funded by AGAUR (2023 FI-1 00054), and J.A.S.M.’s contributions were also supported by Diputacióde Lleida ‘Ajuts al Talent en InvestigacióBiomèdica”. IRBLLEIDA, J.A.S.M., X.G.-P., and A.S. are co-funded by the CERCA Program/Generalitat de Catalunya. J.J.M., A.R.-M., and X.G.-P. are members of the ExoPsyCog Consortium, funded by IKUR-Neurobiosciences—Basque Government. Institutional Review Board Statement: All of the experimental procedures were approved by the ethical committee of the University Hospital Arnau de Vilanova—IRBLLEIDA, Spain (Ref# XI2022) and were performed in strict accordance with institutional guidelines, the Declaration of Helsinki, and the Spanish Organic Law 3/5 December 2018 for Protection of Personal Data (LOPD). Subject identification codes were kept anonymized in all cases. Informed Consent Statement: Informed consent for the donation of the included brain tissues was obtained from all subjects and/or their representatives at the Basque Institute of Legal Medicine. Data Availability Statement: All proteomics data generated for this study have been made publicly available through the specialized repository, PRIDE, with the following identifier: PXD042732. Acknowledgments: The authors extend their appreciation to Aina Iuliana Onoiu for her valuable assistance in the data analysis during the culmination of her undergraduate final year project within our group at IRBLLEIDA/UdL. Additionally, our gratitude goes to the staff members of the Basque Institute of Legal Medicine for their cooperative spirit and generous support. We also thank our peers for the enriching and valuable feedback provided at the following international scientific conferences: ISMND2022 in Athens, Greece; ISFMS2022 in Florence, Italy; Extracellular Vesicles: Friends and Foes II 2023 at the Weizmann Institute in Israel; WCN 2023 in Montreal, Canada; and ASCB-EMBO Cell Bio 2023 in Boston, MA, USA. Finally, we express gratitude to the altruistic donors and relatives whose generosity has made this study possible. Conflicts of Interest: The authors declare that they have no competing interests with regard to the data and conclusions reported. The interpretations provided in this work are based on the obtained Biomedicines 2024,12, 129 18 of 20 scientific data and are those of the authors and not necessarily those of the public bodies that funded the study. References 1. Charlson, F.J.; Ferrari, A.J.; Santomauro, D.F.; Diminic, S.; Stockings, E.; Scott, J.G.; McGrath, J.J.; Whiteford, H.A. Global Epidemiology and Burden of Schizophrenia: Findings from the Global Burden of Disease Study 2016. Schizophr. Bull. 2018,44, 1195–1203. [CrossRef] [PubMed] 2. Saleem, A.; Qurat Ul, A.; Akhtar, M.F. Alternative Therapy of Psychosis: Potential Phytochemicals and Drug Targets in the Management of Schizophrenia. Front. Pharmacol. 2022,13, 895668. [CrossRef] [PubMed] 3. Nucifora, F.C., Jr.; Woznica, E.; Lee, B.J.; Cascella, N.; Sawa, A. Treatment resistant schizophrenia: Clinical, biological, and therapeutic perspectives. Neurobiol. Dis. 2019,131, 104257. [CrossRef] [PubMed] 4. Huang, Z.H.; Hou, C.L.; Huang, Y.H.; He, X.Y.; Wang, Q.W.; Chen, X.; Wang, Z.L.; Wang, S.B.; Jia, F.J. Individuals at high risk for psychosis experience more childhood trauma, life events and social support deficit in comparison to healthy controls. Psychiatry Res. 2019,273, 296–302. [CrossRef] [PubMed] 5. Nakamura, T.; Takata, A. The molecular pathology of schizophrenia: An overview of existing knowledge and new directions for future research. Mol. Psychiatry 2023,28, 1868–1889. [CrossRef] [PubMed] 6. Owen, M.J.; Sawa, A.; Mortensen, P.B. Schizophrenia. Lancet 2016,388, 86–97. [CrossRef] [PubMed] 7. Golov, A.K.; Kondratyev, N.V.; Kostyuk, G.P.; Golimbet, A.V.E. Novel Approaches for Identifying the Molecular Background of Schizophrenia. Cells 2020,9, 246. [CrossRef] [PubMed] 8. Zamanpoor, M. Schizophrenia in a genomic era: A review from the pathogenesis, genetic and environmental etiology to diagnosis and treatment insights. Psychiatr. Genet. 2020,30, 1–9. [CrossRef] 9. Liu, C.; Kanazawa, T.; Tian, Y.; Mohamed Saini, S.; Mancuso, S.; Mostaid, M.S.; Takahashi, A.; Zhang, D.; Zhang, F.; Yu, H.; et al. The schizophrenia genetics knowledgebase: A comprehensive update of findings from candidate gene studies. Transl. Psychiatry 2019,9, 205. [CrossRef] 10. Iasevoli, F.; Avagliano, C.; D’Ambrosio, L.; Barone, A.; Ciccarelli, M.; De Simone, G.; Mazza, B.; Vellucci, L.; de Bartolomeis, A. Dopamine Dynamics and Neurobiology of Non-Response to Antipsychotics, Relevance for Treatment Resistant Schizophrenia: A Systematic Review and Critical Appraisal. Biomedicines 2023,11, 895. [CrossRef] 11. Kesby, J.P.; Eyles, D.W.; McGrath, J.J.; Scott, J.G. Dopamine, psychosis and schizophrenia: The widening gap between basic and clinical neuroscience. Transl. Psychiatry 2018,8, 30. [CrossRef] [PubMed] 12. Buckley, P.F. Neuroinflammation and Schizophrenia. Curr. Psychiatry Rep. 2019,21, 72. [CrossRef] [PubMed] 13. Calvin, O.L.; Redish, A.D. Global disruption in excitation-inhibition balance can cause localized network dysfunction and Schizophrenia-like context-integration deficits. PLoS Comput. Biol. 2021,17, e1008985. [CrossRef] [PubMed] 14. Kelly, S.; Jahanshad, N.; Zalesky, A.; Kochunov, P.; Agartz, I.; Alloza, C.; Andreassen, O.A.; Arango, C.; Banaj, N.; Bouix, S.; et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: Results from the ENIGMA Schizophrenia DTI Working Group. Mol. Psychiatry 2018,23, 1261–1269. [CrossRef] [PubMed] 15. Satterthwaite, T.D.; Vandekar, S.N.; Wolf, D.H.; Bassett, D.S.; Ruparel, K.; Shehzad, Z.; Craddock, R.C.; Shinohara, R.T.; Moore, T.M.; Gennatas, E.D.; et al. Connectome-wide network analysis of youth with Psychosis-Spectrum symptoms. Mol. Psychiatry 2015,20, 1508–1515. [CrossRef] 16. Gallart-Palau, X.; Serra, A.; Sze, S.K. Enrichment of extracellular vesicles from tissues of the central nervous system by PROSPR. Mol. Neurodegener. 2016,11, 41. [CrossRef] 17. Gallart-Palau, X.; Guo, X.; Serra, A.; Sze, S.K. Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles. Alzheimers Res. Ther. 2020,12, 54. [CrossRef] 18. Gallart-Palau, X.; Serra, A.; Hase, Y.; Tan, C.F.; Chen, C.P.; Kalaria, R.N.; Sze, S.K. Brain-derived and circulating vesicle profiles indicate neurovascular unit dysfunction in early Alzheimer’s disease. Brain Pathol. 2019,29, 593–605. [CrossRef] 19. Lorca, C.; Laparra, M.; Céspedes, M.V.; Casaní, L.; Florit, S.; Jové, M.; Mota-Martorell, N.; Vilella, E.; Gallart-Palau, X.; Serra, A. Industrial By-Products As a Novel Circular Source of Biocompatible Extracellular Vesicles. Adv. Funct. Mater. 2022,32, 2202700. [CrossRef] 20. Graykowski, D.R.; Wang, Y.Z.; Upadhyay, A.; Savas, J.N. The Dichotomous Role of Extracellular Vesicles in the Central Nervous System. iScience 2020,23, 101456. [CrossRef] 21. Herrmann, I.K.; Wood, M.J.A.; Fuhrmann, G. Extracellular vesicles as a next-generation drug delivery platform. Nat. Nanotechnol. 2021,16, 748–759. [CrossRef] [PubMed] 22. Aharon, A.; Spector, P.; Ahmad, R.S.; Horrany, N.; Sabbach, A.; Brenner, B.; Aharon-Peretz, J. Extracellular Vesicles of Alzheimer’s Disease Patients as a Biomarker for Disease Progression. Mol. Neurobiol. 2020,57, 4156–4169. [CrossRef] [PubMed] 23. Muraoka, S.; DeLeo, A.M.; Sethi, M.K.; Yukawa-Takamatsu, K.; Yang, Z.; Ko, J.; Hogan, J.D.; Ruan, Z.; You, Y.; Wang, Y.; et al. Proteomic and biological profiling of extracellular vesicles from Alzheimer’s disease human brain tissues. Alzheimers Dement. 2020,16, 896–907. [CrossRef] [PubMed] 24. Gratpain, V.; Mwema, A.; Labrak, Y.; Muccioli, G.G.; van Pesch, V.; des Rieux, A. Extracellular vesicles for the treatment of central nervous system diseases. Adv. Drug Deliv. Rev. 2021,174, 535–552. [CrossRef] [PubMed] Biomedicines 2024,12, 129 19 of 20 25. Cristina, L.; María, F.-R.; Jose Antonio Sánchez, M.; María, M.; Julia, L.; Xavier, G.-P.; Aida, S. BP-EVs: A Novel Source of EVs in the Nanocarrier Field. In Extracellular Vesicles—Applications and Therapeutic Potential; Manash, K.P., Ed.; IntechOpen: Rijeka, Croatia, 2023; Chapter 1. 26. Wang, Y.; Amdanee, N.; Zhang, X. Exosomes in schizophrenia: Pathophysiological mechanisms, biomarkers, and therapeutic targets. Eur. Psychiatry 2022,65, e61. [CrossRef] [PubMed] 27. Roy, R.; Lorca, C.; Mulet, M.; Sánchez Milán, J.A.; Baratas, A.; de la Casa, M.; Espinet, C.; Serra, A.; Gallart-Palau, X. Altered ureido protein modification profiles in seminal plasma extracellular vesicles of non-normozoospermic men. Front. Endocrinol. 2023,14, 1113824. [CrossRef] [PubMed] 28. Mun, D.G.; Budhraja, R.; Bhat, F.A.; Zenka, R.M.; Johnson, K.L.; Moghekar, A.; Pandey, A. Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF. Proteomics 2023,23, e2200507. [CrossRef] 29. Serra, A.; Gallart-Palau, X.; Wei, J.; Sze, S.K. Characterization of glutamine deamidation by LERLIC-MS/MS in shotgun proteomics. Anal. Chem. 2016,1, 10573–10582. [CrossRef] 30. Gallart-Palau, X.; Serra, A.; Sze, S.K. System-wide molecular dynamics of endothelial dysfunction in Gram-negative sepsis. BMC Biol. 2020,18, 175. [CrossRef] 31. Guo, X.; Park, J.E.; Gallart-Palau, X.; Sze, S.K. Oxidative Damage to the TCA Cycle Enzyme MDH1 Dysregulates Bioenergetic Enzymatic Activity in the Aged Murine Brain. J. Proteome Res. 2020,19, 1706–1717. [CrossRef] 32. Sánchez Milán, J.A.; Fernández-Rhodes, M.; Guo, X.; Mulet, M.; Ngan, S.C.; Iyappan, R.; Katoueezadeh, M.; Sze, S.K.; Serra, A.; Gallart-Palau, X. Trioxidized cysteine in the aging proteome mimics the structural dynamics and interactome of phosphorylated serine. Aging Cell 2023,00, e14062. [CrossRef] [PubMed] 33. Panahi, M.; Hase, Y.; Gallart-Palau, X.; Mitra, S.; Watanabe, A.; Low, R.C.; Yamamoto, Y.; Sepulveda-Falla, D.; Hainsworth, A.H.; Ihara, M.; et al. ER stress induced immunopathology involving complement in CADASIL: Implications for therapeutics. Acta Neuropathol. Commun. 2023,11, 76. [CrossRef] [PubMed] 34. Gallart-Palau, X. Systems Biology in Neuroscience: The Paramount Importance of Data Sharing and Citation. NeuroSci 2020,1, 98. [CrossRef] 35. Saeedi, S.; Israel, S.; Nagy, C.; Turecki, G. The emerging role of exosomes in mental disorders. Transl. Psychiatry 2019,9, 122. [CrossRef] [PubMed] 36. Perez-Gonzalez, R.; Gauthier, S.A.; Kumar, A.; Levy, E. The Exosome Secretory Pathway Transports Amyloid Precursor Protein Carboxyl-terminal Fragments from the Cell into the Brain Extracellular Space*. J. Biol. Chem. 2012,287, 43108–43115. [CrossRef] 37. Levy, E. Exosomes in the Diseased Brain: First Insights from In Vivo Studies. Front. Neurosci. 2017,11, 142. [CrossRef] 38. Gallart-Palau, X.; Tan, L.M.; Serra, A.; Gao, Y.; Ho, H.H.; Richards, A.M.; Kandiah, N.; Chen, C.P.; Kalaria, R.N.; Sze, S.K. Degenerative protein modifications in the aging vasculature and central nervous system: A problem shared is not always halved. Ageing Res. Rev. 2019,53, 100909. [CrossRef] 39. Gomes, P.A.; Bodo, C.; Nogueras-Ortiz, C.; Samiotaki, M.; Chen, M.; Soares-Cunha, C.; Silva, J.M.; Coimbra, B.; Stamatakis, G.; Santos, L.; et al. A novel isolation method for spontaneously released extracellular vesicles from brain tissue and its implications for stress-driven brain pathology. Cell Commun. Signal 2023,21, 35. [CrossRef] 40. Kandimalla, R.; Saeed, M.; Tyagi, N.; Gupta, R.C.; Aqil, F. Exosome-based approaches in the management of Alzheimer’s disease. Neurosci. Biobehav. Rev. 2023,144, 104974. [CrossRef] 41. Huang, Y.; Driedonks, T.A.P.; Cheng, L.; Rajapaksha, H.; Routenberg, D.A.; Nagaraj, R.; Redding, J.; Arab, T.; Powell, B.H.; Pletniková, O.; et al. Brain Tissue-Derived Extracellular Vesicles in Alzheimer’s Disease Display Altered Key Protein Levels Including Cell Type-Specific Markers. J. Alzheimers Dis. 2022,90, 1057–1072. [CrossRef] 42. Ruan, Z.; Pathak, D.; Venkatesan Kalavai, S.; Yoshii-Kitahara, A.; Muraoka, S.; Bhatt, N.; Takamatsu-Yukawa, K.; Hu, J.; Wang, Y.; Hersh, S.; et al. Alzheimer’s disease brain-derived extracellular vesicles spread tau pathology in interneurons. Brain 2020,144, 288–309. [CrossRef] [PubMed] 43. Keerthikumar, S.; Chisanga, D.; Ariyaratne, D.; Al Saffar, H.; Anand, S.; Zhao, K.; Samuel, M.; Pathan, M.; Jois, M.; Chilamkurti, N.; et al. ExoCarta: A Web-Based Compendium of Exosomal Cargo. J. Mol. Biol. 2016,428, 688–692. [CrossRef] [PubMed] 44. Pathan, M.; Fonseka, P.; Chitti, S.V.; Kang, T.; Sanwlani, R.; Van Deun, J.; Hendrix, A.; Mathivanan, S. Vesiclepedia 2019: A compendium of RNA, proteins, lipids and metabolites in extracellular vesicles. Nucleic Acids Res 2018,47, D516–D519. [CrossRef] [PubMed] 45. Montenegro-Venegas, C.; Guhathakurta, D.; Pina-Fernandez, E.; Andres-Alonso, M.; Plattner, F.; Gundelfinger, E.D.; Fejtova, A. Bassoon controls synaptic vesicle release via regulation of presynaptic phosphorylation and cAMP. EMBO Rep. 2022,23, e53659. [CrossRef] [PubMed] 46. Yap, K.; Drakew, A.; Smilovic, D.; Rietsche, M.; Paul, M.H.; Vuksic, M.; Del Turco, D.; Deller, T. The actin-modulating protein synaptopodin mediates long-term survival of dendritic spines. eLife 2020,9, e62944. [CrossRef] [PubMed] 47. Goldie, B.J.; Dun, M.D.; Lin, M.; Smith, N.D.; Verrills, N.M.; Dayas, C.V.; Cairns, M.J. Activity-associated miRNA are packaged in Map1b-enriched exosomes released from depolarized neurons. Nucleic Acids Res. 2014,42, 9195–9208. [CrossRef] [PubMed] 48. Henstridge, C.M.; Sideris, D.I.; Carroll, E.; Rotariu, S.; Salomonsson, S.; Tzioras, M.; McKenzie, C.-A.; Smith, C.; von Arnim, C.A.F.; Ludolph, A.C.; et al. Synapse loss in the prefrontal cortex is associated with cognitive decline in amyotrophic lateral sclerosis. Acta Neuropathol. 2018,135, 213–226. [CrossRef] Biomedicines 2024,12, 129 20 of 20 49. Casaletto, K.B.; Lindbergh, C.A.; VandeBunte, A.; Neuhaus, J.; Schneider, J.A.; Buchman, A.S.; Honer, W.G.; Bennett, D.A. Microglial Correlates of Late Life Physical Activity: Relationship with Synaptic and Cognitive Aging in Older Adults. J. Neurosci. 2022,42, 288–298. [CrossRef] 50. Lieberman, J.A.; Girgis, R.R.; Brucato, G.; Moore, H.; Provenzano, F.; Kegeles, L.; Javitt, D.; Kantrowitz, J.; Wall, M.M.; Corcoran, C.M.; et al. Hippocampal dysfunction in the pathophysiology of schizophrenia: A selective review and hypothesis for early detection and intervention. Mol. Psychiatry 2018,23, 1764–1772. [CrossRef] 51. Abi-Dargham, A.; Moore, H. Prefrontal DA transmission at D1 receptors and the pathology of schizophrenia. Neuroscientist 2003, 9, 404–416. [CrossRef] 52. Jain, R.W.; Yong, V.W. B cells in central nervous system disease: Diversity, locations and pathophysiology. Nat. Rev. Immunol. 2022,22, 513–524. [CrossRef] [PubMed] 53. Tin, S.K.; Xu, Q.; Thumboo, J.; Lee, L.Y.; Tse, C.; Fong, K.Y. Novel brain reactive autoantibodies: Prevalence in systemic lupus erythematosus and association with psychoses and seizures. J. Neuroimmunol. 2005,169, 153–160. [CrossRef] [PubMed] 54. Dalmau, J.; Lancaster, E.; Martinez-Hernandez, E.; Rosenfeld, M.R.; Balice-Gordon, R. Clinical experience and laboratory investigations in patients with anti-NMDAR encephalitis. Lancet Neurol. 2011,10, 63–74. [CrossRef] [PubMed] 55. Glass, L.J.; Sinclair, D.; Boerrigter, D.; Naude, K.; Fung, S.J.; Brown, D.; Catts, V.S.; Tooney, P.; O’Donnell, M.; Lenroot, R.; et al. Brain antibodies in the cortex and blood of people with schizophrenia and controls. Transl. Psychiatry 2017,7, e1192. 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