Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder

Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder | 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 Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder Houda Yasmine Ali Moussa, Kyung Chul Shin, Alberto de la Fuente, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4212009/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by symptoms that include social interaction deficits, language difficulties and restricted, repetitive behavior. Early intervention through medication and behavioral therapy can eliminate some ASD-related symptoms and significantly improve the life-quality of the affected individuals. Currently, the diagnosis of ASD is highly limited. Method s To investigate the feasibility of early diagnosis of ASD, we tested extracellular vesicles (EVs) proteins obtained from ASD cases. First, plasma EVs were isolated from healthy controls (HCs) and ASD individuals and were analyzed using proximity extension assay (PEA) technology to quantify 1196 protein expression level. Second, machine learning analysis and bioinformatic approaches were applied to explore how a combination of EV proteins could serve as biomarkers for ASD diagnosis. Results No significant differences in the EV morphology and EV size distribution between HCs and ASD were observed, but the EV number was slightly lower in ASD plasma. We identified the top five downregulated proteins in plasma EVs isolated from ASD individuals: WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha). Machine learning analysis and correlation analysis support the idea that these five EV proteins can be potential biomarkers for ASD. Conclusion We identified the top five downregulated proteins in ASD EVs and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis. Extracellular vesicle biomarker Olink Figures Figure 1 Figure 2 Figure 3 Introduction Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, characterized by stereotyped repetitive behaviors and communication deficits 1 . An increasing number of genetic variants implicated in ASD have been reported, suggesting a high degree of locus heterogeneity and contributions from rare and de novo variants 2 . Comorbidity is common in ASD, including attention-deficit hyperactivity disorder (ADHD) and epilepsy 3 . One of the major challenges in ASD research is to find reliable biomarkers that can help with early detection of ASD. Although some genetic factors have been linked to ASD risk, there is no definitive or consistent biomarker for ASD yet. EVs are a group of vesicles surrounded by a lipid bilayer and are secreted by almost all cell types 4 . They mediate intercellular communication by transferring their contents horizontally 5 . EVs have critical functions in health and disease and offer potential clinical value as new biomarkers for early detection and therapeutic targets for treatment 6 . EVs can cross the blood-brain barrier (BBB) 7-9 , thereby circulating through the bloodstream. Since EVs mirror the cell and tissue of origin in terms of disease outcome and severity, their contents can serve as non-invasive biomarkers for various diseases 6,10 and plasma EVs can be used as biomarkers of neurological disorders 11 . EV proteins are promising liquid biopsy targets for early detection of Parkinson’s disease, as their profiles change in disease conditions 12 . Three plasma EV proteins (clusterin, complement C1r subcomponent, and apolipoprotein A1) could serve as diagnostic biomarkers for Parkinson’s disease, and the expression of EV proteins is associated with disease progression 12 . Plasma EV proteins could also help distinguish Alzheimer’s disease (AD) patients from healthy controls 13 . However, no specific EV protein biomarkers have been yet identified for ASD. In this study, we used size exclusion chromatography (SEC) to isolate EVs from the plasma of healthy controls (HCs) and ASD cases from Qatari and non-Qatari individuals living in Qatar. We then applied the proximity extension assay (PEA) Olink platform to analyze the EV proteome profiles. We identified the top five downregulated proteins in plasma EVs isolated from ASD cases and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis. Material and Methods 1.1 Study cohort and blood collection All procedures were performed under the approval of the Institutional Review Board (IRB# 2018-024) of Qatar Biomedical Research Institute (QBRI). The study cohort was obtained from QBRI’s Interdisciplinary Research Program (IDRP) depository and included plasma samples from 81 ASD and 26 healthy control (HCs) individuals, all residing in Qatar. The communication and social skills for the HCs were evaluated using the Social Communication Questionnaire (SCQ). ASD individuals were clinically diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Written informed consent and assent were obtained from all the HCs, ASD individuals and their surrogates. Demographical information of the participants is summarized in Table 1. Human peripheral blood samples were drawn from the HCs and ASD individuals into EDTA tubes. In the processing of blood samples, blood components were separated using density gradient centrifugation as previously described 11 . The plasma supernatant was further centrifuged for 15 minutes to remove platelets and blood cells to obtain platelet-free plasma, which was aliquoted and stored at −80°C until further use for EV isolation. 1.2 Extracellular vesicles isolation EVs were isolated from 250uL of human plasma by size exclusion chromatography (SEC) using the qEVOriginal/35nm columns (SP5, Izon Science, Christchurch, New Zealand). The plasma was thawed on ice and diluted with PBS for a final volume of 500uL. It was then centrifuged at 3000g for 10 min and 10,000g for 30 min to remove cell debris and large vesicles, followed by purification on the SEC columns. Fractions 1-25, including void volume were collected for some samples to verify the particle/protein profile. For the remaining samples, the void volume was discarded and only the high particle/low protein fractions were collected. EV-enriched fractions 1 and 2 with total volume of 1 mL, were pooled and concentrated using pre-conditioned 100 KDa Amicon Ultra-15 centrifuge filters (UFC9100, Millipore) to a final volume of 170 µL. The amount of EV proteins was estimated by measuring the absorbance at 280 nm (A280). EV samples were aliquoted to minimize the freeze-thaw cycles and stored at −80°C until further analyzed. 1.3 Nanoparticle tracking analysis (NTA) Particle size and particle number were determined using nanoparticle tracking analysis (NTA) (ZetaView, Particle Metrix, Germany). EV samples were diluted with filtered PBS to an average of 100 particles per frame and a final volume of 1 mL. Zetaview software (version 8.04.02 SP2) recorded particles at 11 camera positions and 30 frames per second. 1.4 Transmission electron microscopy (TEM) Five microliter of EV suspension was deposited on carbon-coated 400-mesh copper grids (CF400-CU, Electron Microscopy Sciences) and incubated for 10 min. The EVs were then washed with ddH 2 O and excess fluid was absorbed with filter paper. Grids were negatively stained with uranyl acetate and embedded in methylcellulose-uranyl acetate. EVs were examined at 80 kV in Talos F200C Transmission Electron Microscope (Thermo Fisher Scientific). The images were acquired using bottom-mounted CETA camera. 1.5 Olink proximity extension assay Protein profiling of EV samples was carried out using the proximity extension assay (PEA) from the Olink Target 96, testing a total of 1196 proteins (13 panels including Neurology, Development, Neuro-exploratory, Inflammation, Immune Response, Cell Regulation, Organ Damage, Metabolism, Oncology II, Oncology III, Cardiometabolic, Cardiovascular II, and Cardiovascular III; Olink Bioscience, Uppsala, Sweden). Following the standard protocol, the runs were performed by Olink-certified proteomics core facility at QBRI and were all validated by the Olink support team in Uppsala, Sweden. PEA is an ultrasensitive technology based on dual recognition of target proteins through matched pairs of antibodies labeled with DNA oligonucleotides 14 . Quality control and data normalization were carried out using the Normalized Protein eXpression (NPX) software. Protein expression values were calculated as NPX; NPX is an arbitrary unit by Olink to quantify protein expression level on a log2 scale. Olink data that did not pass quality control were excluded from the analyses. 1.6 Bioinformatics The analysis for EV characterization experiments was conducted using GraphPad Prism software. Statistical analysis for proteomics data was performed using R software. Differentially expressed proteins were identified using the Limma (Linear Models for Microarray Data) package in R. The p -values of all the proteins were adjusted for multiple testing using the Benjamini–Hochberg (BH) method. The analytical model accounted for age, sex, and EV particle counts as influential covariates. Top differentially expressed proteins (TopDEPs) were selected based on a dual criterion: a fold change (FC ≥ 2) and a BH-adjusted p -value (adj p -value ≤ 0.05). For Gene Ontology (GO) Enrichment Analysis, the R library clusterProfiler was used with focus on Cellular Components and Biological Process, and enriched GO terms passing the threshold of adjusted p -values < 0.05 were identified. For Machine Learning, variable selection was performed using the MUVR, Boruta, and VSURF R packages, all set to their default parameters for optimal performance. The MLR3 library served as the foundation for training and evaluating an array of methods through repeated 4-fold cross validation. The MLR3 library also provides a function to create an ROC curve averaged over all validation folds and calculate the 95% Confidence Interval. Results Study cohort characteristics We recruited 109 participants for our study, consisting of 81 ASD cases and 26 healthy controls (HCs) who were aged between 6 and 15 years. The mean ages for ASD cases and HCs were 8.56 ± 2.17& and 11.08 ± 2.20, respectively ( Table 1 ). The HC group had an equal proportion of males and females (50%); however, the ASD group had a higher percentage of males (79%) due to the male predominance of ASD, which can be as high as 4:1 15 . All ASD cases had a clinical diagnosis of ASD based on DSM-5 criteria and were evaluated using the ADOS-2 score. Table 1 shows the demographical information of ASD cases and HCs. Characterization of EVs isolated from blood plasma of ASD and HCs We have optimized the protocol of plasma EV isolation using size exclusion chromatography (SEC), as previously described 11 . The larger molecules elute first from the SEC column, followed by EVs, and plasma protein complexes are the last to elute ( Figure 1A,B ). We measured the particle number of EVs in each fraction by using nanoparticle tracking analysis (NTA). We combined fractions 2 and 3 as EV samples for higher purity, and also monitored the absorbance at 280 nm for protein elution profiles ( Figure 1A,B ). We confirmed that abundant plasma proteins are removed from EV samples to improve the purity 11 and soluble protein elution increases sharply from fraction 5; the elution profiles of EVs and plasma proteins obtained by SEC did not reveal any differences between HCs and ASD cases ( Figure 1A,B ). We then further analyzed the EV samples from fractions 2 and 3. The plasma EVs were measured by NTA to determine their size distribution. The results showed that the plasma EVs from HCs and ASD had similar sizes, ranging from 50 to 200 nm; mean diameter (nm) of 121.3 ± 40.32 SD for HCs and 120.3 ± 40.53 SD for ASD ( Figure 1C,D ). Intriguingly, the number of EV particles was reduced in ASD plasma samples ( Figure 1E ), while the plasma protein concentration was similar between HCs and ASD ( Figure 1F ); we used 0.25 mL plasma for this study. The median diameter of EVs did not differ between HCs and ASD ( Figure 1G ). The structure of HCs- and ASD-derived EVs characterized by atomic force microscopy (AFM) was comparable ( Figure 1H ). Overall, these data suggest that there were no significant differences in the morphology and size distribution, but the EV number was lower in ASD plasma. EV protein profiling using the Olink platform Olink analysis of 1196 proteins demonstrated a distinct plasma EVs protein expression profile in individuals with ASD compared to HCs (see Method section for details). Differentially expressed proteins were identified using Limma package in R. A list of the top differentially expressed proteins (TopDEPs) was summarized with a BH-adjusted p -value < 0.05 and fold change (FC) ³ 2. A total of five downregulated proteins in ASD EVs were listed in the TopDEPs; no proteins were upregulated in ASD EVs ( Figure 2A ). Further details of all the significantly downregulated proteins are listed in Table 2 . Top five downregulated proteins include WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha)( Figure 2B ). Gene Ontology enrichment analysis of TopDEPs We performed Gene Ontology (GO) enrichment analysis of TopDEPs to evaluate functional annotation of these downregulated proteins in ASD EVs. The cellular components in GO analysis only included external side of plasma membrane ( Figure 2C ), supporting that TopDEPs are derived from EVs. Top five significantly downregulated proteins are associated with EVs and can be potential biomarkers for ASD (see Discussion ); WWP2, HSP27, CLEC1B, CD40, and FRalpha. Next, we performed GO enrichment analysis of TopDEPs to identify the biological processes that were significantly dysregulated in ASD EVs ( Figure 2D ). We sorted the enriched biological terms by counts, which represent numbers of proteins associated with each term. The immune responses including inflammation and cytokine production were affected by downregulated proteins in ASD EVs ( Figure 2D ), implying that ASD EVs might be associated with immune dysregulation. Machine learning to identify potential biomarkers To further demonstrate potential biomarkers for ASD, we applied machine learning algorithms including minimally biased variable selection in R (MUVR), Boruta, and variable selection using random forests (VSURF) ( Figure 3 ). We used three different feature selection methods to identify the most potential proteins for predicting the outcome, and then compared the performance of different classification algorithms. Six proteins overlapped between MUVR and Boruta, and four proteins were among MUVR, Boruta, and VSURF: WWP2, CD40, PAR1, FRalpha, CLEC1B, and HSP27 ( Figure 3A ). Details of six proteins are listed in Table 2 ; note that five out of these six proteins were also found to from the list of TopDEPs. The diagnostic performance was tested using multiple multivariant supervised machine learning algorithms (random forest, generalized linear model, and support vector machines (SVM)). Six proteins were internally validated with four-fold cross-validations and 100 repeats ( Figure 3B,C ). The average ROC curve suggested that six proteins are strong candidates for diagnostic biomarkers for ASD with average AUC = 0.923, accuracy = 86.3%, sensitivity = 95.3%, specificity = 66.2% ( Figure 3C ). Discussion ASD affects approximately 1% of the global population, creating a significant public health burden in different communities including Qatar. According to our QBRI study on ASD 16 , the prevalence of ASD in Qatar is 1.14% (one in every 87 children), leading to the financial burden and stress on parents and caregivers. Early intervention, whether through medication or behavioral therapy, can alleviate some ASD-related symptoms, significantly improving the life-quality of the affected individuals 17-19 . Currently, early detection and intervention of ASD are highly limited and there are no medical kits or blood tests available for ASD diagnosis. Medical doctors can only check the child's behavior and development to make a diagnosis of ASD, thereby limiting early intervention of ASD until kids become at least 4 or 5 years old. Early intervention and detection are critical to help ASD children effectively improve their language ability and social interaction. In the literature, several genetic variants have been proposed as promising biomarkers for ASD 20 . Yet, because of the numerous gene mutations, ASD is extremely heterogenous and cannot be defined by unique polymorphisms. Other studies have identified differences in the microbiota and metabolic, immune, and nutritional markers, between control and ASD individuals 21-23 . These potential biomarkers are all yet to be confirmed by large validation studies which can turn out to be extremely challenging. The various findings do, however, present valuable clues into the underlying molecular mechanisms and as to which biological processes are affected in ASD. In the present study, we have isolated and characterized plasma EVs in ASD and control individuals. We performed an extensive proteomics profiling, screening over 1000 proteins, of which 5 are significantly downregulated in ASD EVs. To our knowledge, this study is the first and unique to investigate the EV protein cargo in ASD. Top five significantly downregulated proteins are related to EV biogenesis, function and signaling: 1) WWP2, an E3 ubiquitin ligase, regulates EV release by ubiquitination of EV proteins 24 ; 2) HSP27 is a heat shock protein, which is elevated in the blood in various diseases 25 and extracellular HSP27 may have functions in pathological conditions 26 . HSP27 is present in EVs released from THP-1 cells 27 and can be transferred to recipient cells via EVs 25 ; 3) CLEC1B is a receptor involved in transmembrane signaling 28 and is highly expressed in neuron-derived exosomes 29 ; 4) CD40 is a protein present in plasma EVs from non-Hodgkin lymphoma patients 30 and tumor-derived EVs 31 , suggesting its potential as a cancer biomarker; 5) FRalpha is present in EVs and involved in folate transport into the brain through EVs 32 . Altogether, our data support that these five proteins may serve as useful EV biomarkers for ASD diagnosis. Among the proteins we show to be downregulated in ASD individuals is HSP27 which is thought to have major protective effects against many cellular stresses 33 . This was in accordance with a previously published study evaluating protein levels in the blood of ASD children and found HSP27 to be decreased 34 . Over-expression of HSP27 has been shown to protect and rescue neuronal and non-neuronal cells from cell damage and death 33,35 . The downregulation seen in our results indicate a potential susceptibility of ASD neurons to cell death. Due to the limited accessibility to the brain and cerebrospinal fluid (CSF) for biomarker discovery, blood is ideal for liquid biopsy, given its easier accessibility and non-invasive collection 36 . EVs are very attractive diagnostic and therapeutic tools, particularly for brain disorders, because of their property to cross the BBB 7-9 . Thus, plasma EVs provide a potential therapeutic approach to neurological disorders. Brain-derived EVs might provide biomarkers for neuronal disorders, and EVs can be used in therapeutics as a drug delivery system to the brain 37,38 . EV proteins and RNA are considered promising biomarkers for neurodegenerative disease and neurodevelopmental disorders 8,29,39 . Our data support that five EV proteins can pave the way for early diagnosis of ASD as novel biomarkers and have the potential to enhance diagnostic accuracy and facilitate earlier intervention strategies. Noteworthy is the connection of the five TopDEPs identified in ASD EVs with immune responses and cytokine production ( Figure 2D ). This suggests that ASD EVs may play a role in modulating chronic inflammation. While chronic inflammation and immune dysregulation have been proposed as potential contributors to the characteristic features of autism 40 , the mechanisms by which ASD EVs regulate chronic inflammation remain to be elucidated in further studies. Abbreviation ASD: Autism spectrum disorder AFM: Atomic force microscopy AD: Alzheimer’s disease ADHD: Attention-deficit hyperactivity disorder BBB: Blood-brain barrier CLEC1B: C-type lectin domain family 1 member B CD40: Cluster of differentiation 40 DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition EVs: Extracellular vesicles Fralpha: Folate receptor alpha FC: Fold change GO: Gene Ontology HCs: Healthy controls HSP27: Heat shock protein 27 MUVR: Minimally biased variable selection in R NTA: Nanoparticle tracking analysis NPX: Normalized Protein eXpression PAR1: Protease-activated receptor-1 PEA: Proximity extension assay SEC: Size exclusion chromatography SVM: Support vector machines TopDEPs: Top differentially expressed proteins VSURF: Variable selection using random forests WWP2: WW domain-containing protein 2 Declarations Ethics declarations Ethics approval and consent to participate. All procedures were performed under the approval of the Institutional Review Board (IRB# 2018-024) of Qatar Biomedical Research Institute (QBRI). Consent for publication. Not applicable. Availability of data and material. The datasets supporting the conclusions of this article are available from the corresponding author on reasonable request. Competing interests. The authors declare no conflict of interest. Funding This work was supported by the grant from Qatar Biomedical Research Institute (Project Number SF 2019 004 and IGP5-2022-001 to Y.P.) and the HBKU Thematic Research Grant (Project Number VPR-TG02-06 to Y.P.). Author Contributions H.Y.A.M. and K.C.S contributed to EV isolation and experiments. A.F. contributed to bioinformatics analysis. I.B. and H.B.A. contributed to Olink. F.A.A. recruited plasma samples. J.P. and S.M. contributed to TEM. L.W.S., S.A.A. contributed to conceptualization, design, and supervision. Y.P. contributed to conceptualization and design, funding acquisition, project management, resources, supervision, and review and editing. H.Y.A.M. and Y.P. wrote the manuscript and all authors read it and provided their comments. Acknowledgments We would like to thank Dr. Salam Salloum-Asfar, Dr. Areej Mesleh, Rowaida Z Taha, Iman Ghazal, and Fatema Al-Faraj for sample recruitment and collection. We thank QBRI’s Proteomics Core Labs, the HBKU Core Labs for the TEM support, and Sidra Medicine for NTA. References Diagnostic and statistical manual of mental disorders: DSM-5™, 5th ed . (American Psychiatric Publishing, Inc., 2013). State, M. W. & Levitt, P. The conundrums of understanding genetic risks for autism spectrum disorders. Nat Neurosci 14 , 1499-1506 (2011). https://doi.org/10.1038/nn.2924 Carter, M. T. & Scherer, S. W. Autism spectrum disorder in the genetics clinic: a review. Clin Genet 83 , 399-407 (2013). https://doi.org/10.1111/cge.12101 Mulcahy, L. A., Pink, R. 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Rank Protein Symbol Protein Full Name Gini Impurity Score Fold Change (FC) Adjusted p -Value 1 WWP2 WW Domain Containing E3 Ubiquitin Protein Ligase 2 0.204 ↓ 1.55 1.80 ´ 10 -7 2 CD40 CD40 Molecule 0.221 ↓ 1.73 1.00 ´ 10 -5 3 CLEC1B C-Type Lectin Domain Family 1 Member B 0.239 ↓ 1.73 2.23 ´ 10 -6 4 PAR1 Protease-activated receptor-1 0.257 ↓ 0.66 1.51 ´ 10 -3 5 HSP27 Heat shock protein 27 0.269 ↓ 1.6 2.34 ´ 10 -5 6 FRalpha Folate Receptor alpha 0.294 ↓ 1.01 2.71 ´ 10 -4 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4212009","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288803268,"identity":"9738b3a9-3004-4212-812c-8d7b1820eaea","order_by":0,"name":"Houda Yasmine Ali Moussa","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Houda","middleName":"Yasmine Ali","lastName":"Moussa","suffix":""},{"id":288803269,"identity":"99885854-2a14-45ca-8fbf-06a80fc0a614","order_by":1,"name":"Kyung Chul Shin","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Kyung","middleName":"Chul","lastName":"Shin","suffix":""},{"id":288803270,"identity":"7a11c09e-8c49-4f59-9a67-a81713c6e1c0","order_by":2,"name":"Alberto de la Fuente","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"de la","lastName":"Fuente","suffix":""},{"id":288803271,"identity":"68eb7877-1051-4d9c-b749-c3378a0ae4c2","order_by":3,"name":"Ilham Bensmail","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Ilham","middleName":"","lastName":"Bensmail","suffix":""},{"id":288803272,"identity":"6a7c0575-d993-4774-ac21-e9ae88f50674","order_by":4,"name":"Houari B. Abdesselem","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Houari","middleName":"B.","lastName":"Abdesselem","suffix":""},{"id":288803273,"identity":"9e85c5d6-af9e-416f-9635-2a0abd7e14b9","order_by":5,"name":"Janarthanan Ponraj","email":"","orcid":"","institution":"HBKU Core Labs, Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Janarthanan","middleName":"","lastName":"Ponraj","suffix":""},{"id":288803274,"identity":"53087d76-62f7-468d-8f4c-eb7f45a6dce1","order_by":6,"name":"Said Mansour","email":"","orcid":"","institution":"HBKU Core Labs, Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"Mansour","suffix":""},{"id":288803275,"identity":"e823a5e2-07b8-403c-9e35-ae9b16f01ab3","order_by":7,"name":"Fouad A. Al-Shaban","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Fouad","middleName":"A.","lastName":"Al-Shaban","suffix":""},{"id":288803276,"identity":"7e9316b9-2c68-4ac9-8a8e-a07527f91914","order_by":8,"name":"Lawrence W Stanton","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"W","lastName":"Stanton","suffix":""},{"id":288803277,"identity":"02328502-5dcd-47b2-a475-582a2d25f81a","order_by":9,"name":"Sara A. Abdulla","email":"","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"A.","lastName":"Abdulla","suffix":""},{"id":288803278,"identity":"d575e1c7-c4c8-40bb-8bc7-23c8ddedb98a","order_by":10,"name":"Yongsoo Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYJACxoaKAwkSIBYP8VrOkKylsY0ULQa3m599nDnvTp7kjATGB2/bGGT7GwhpuXPMeObGbc+KpSUSmA3ntjEYzzhASMuNBGPGh9sOJ86TSGCT5m1jSGwgrCX9M+PDOWAt7L9BWuYT1pJjzLix4XDibKAtzCAtGwhpkbxzpphxxrHDxZI9D5sl55yTMN5ISAvf7fbNjD01h/Mkjicf/PCmzEZ2HiEtCjfgTMYGICEBJvEC+RloAoS1jIJRMApGwYgDAO0cStcz6xjbAAAAAElFTkSuQmCC","orcid":"","institution":"Hamad Bin Khalifa University (HBKU)","correspondingAuthor":true,"prefix":"","firstName":"Yongsoo","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-04-03 10:30:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4212009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4212009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54520136,"identity":"65b2aa92-f1c5-4a4a-9ed0-31d3316752f0","added_by":"auto","created_at":"2024-04-11 17:56:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":403760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of plasma EVs isolated from healthy control (HC) and ASD individuals. \u003c/strong\u003e(\u003cstrong\u003eA,B\u003c/strong\u003e) Representative elution profiles of plasma EVs and plasma proteins from HC (\u003cstrong\u003eA\u003c/strong\u003e) and ASD individuals (\u003cstrong\u003eB\u003c/strong\u003e). EVs are the first to elute, followed by smaller protein complexes. Fractions 2 and 3 were pooled together as EV samples. EV particle numbers and protein concentration in each fraction were determined by NTA and the absorbance at a wavelength of 280 nm, respectively. (\u003cstrong\u003eC,D\u003c/strong\u003e) Representative size distribution of plasma EVs determined by NTA. Mean diameter (nm), 121.3 ± 40.32 SD for HC and 120.3 ±40.53 SD for ASD. (\u003cstrong\u003eE\u003c/strong\u003e) EV particle numbers analyzed using NTA; HCs (n=26) and ASD (n=81). (\u003cstrong\u003eF\u003c/strong\u003e) Plasma protein concentration of HCs (n=9) and ASD (n=18). (\u003cstrong\u003eG\u003c/strong\u003e) Violin plots showing statistical median diameter (X50, nm) of EVs isolated from HC (n=26) and ASD (n=81) plasma; 117.6 nm ± 13.99 SD for HC and 126.1 nm ± 13.51 SD for ASD. (\u003cstrong\u003eH\u003c/strong\u003e) Morphological characterization of HC and ASD EVs using negative-stain transmission electron microscopy (TEM). Data in \u003cstrong\u003eE,F\u003c/strong\u003e are means ± SEM. Unpaired two-tailed t-test was used; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-4212009/v1/23b7eadbe187c6fcd08fe7b6.png"},{"id":54520137,"identity":"a679729b-e1be-42cb-b644-a9b685dba1a4","added_by":"auto","created_at":"2024-04-11 17:56:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":660224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of EV proteins. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The volcano plot of the differentially expressed (DE) proteins in plasma EVs isolated from HCs and ASD cases; log\u003csub\u003e2\u003c/sub\u003e fold change (FC) against Limma −log\u003csub\u003e10\u003c/sub\u003e BH-adjusted \u003cem\u003ep\u003c/em\u003e-value. Color indicates significantly upregulated (red) and downregulated (blue) proteins with FC ³ 2 and adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05. (\u003cstrong\u003eB\u003c/strong\u003e) Box plots of the expression level presented as Olink’s normalized protein expression (NPX) for EV proteins from control (n=26) and ASD (n=60). (\u003cstrong\u003eC\u003c/strong\u003e) Gene Ontology (GO) enrichment analysis for cellular components of downregulated proteins in ASD EVs. The GO cut-off criteria included adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 and FC ³ 1.5. (\u003cstrong\u003eD\u003c/strong\u003e) GO enrichment analysis for biological process of dysregulated proteins in ASD EVs. The GO cut-off criteria included \u003cem\u003eq\u003c/em\u003e (adjusted \u003cem\u003ep\u003c/em\u003e value) \u0026lt; 0.05 and 1.5 FC.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-4212009/v1/98707677c5a1d9e1e4243178.png"},{"id":54520135,"identity":"01563273-3402-4cf4-9cc2-c99e672ad4df","added_by":"auto","created_at":"2024-04-11 17:56:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine learning outcome.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) A Venn diagram of the overlapping proteins selected by MUVR, VSURF, and Boruta methods. (\u003cstrong\u003eB\u003c/strong\u003e) An ROC curve of the true positive rate versus the false positive rate for different threshold values of the classifier. (\u003cstrong\u003eC\u003c/strong\u003e) A table summarizing the performance metrics of the best classifier, which is the random forest using the intersect of the features selected by MUVR, Boruta and VSURF.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-4212009/v1/0f9607728fedfc81828263e1.png"},{"id":54606262,"identity":"2faaefc8-ab92-4993-9501-a18a6e9b393e","added_by":"auto","created_at":"2024-04-13 02:52:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":898999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4212009/v1/3f647522-4120-4fe9-8105-5e33c994d077.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a complex neurodevelopmental condition, characterized by stereotyped repetitive behaviors and communication deficits\u003csup\u003e1\u003c/sup\u003e. An increasing number of genetic variants implicated in ASD have been reported, suggesting a high degree of locus heterogeneity and contributions from rare and \u003cem\u003ede novo\u003c/em\u003e variants\u003csup\u003e2\u003c/sup\u003e. Comorbidity is common in ASD, including attention-deficit hyperactivity disorder (ADHD) and epilepsy\u003csup\u003e3\u003c/sup\u003e. One of the major challenges in ASD research is to find reliable biomarkers that can help with early detection of ASD. Although some genetic factors have been linked to ASD risk, there is no definitive or consistent biomarker for ASD yet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEVs are a group of vesicles surrounded by a lipid bilayer and are secreted by almost all cell types\u003csup\u003e4\u003c/sup\u003e. They mediate intercellular communication by transferring their contents horizontally\u003csup\u003e5\u003c/sup\u003e. EVs have critical functions in health and disease and offer potential clinical value as new biomarkers for early detection and therapeutic targets for treatment\u003csup\u003e6\u003c/sup\u003e. EVs can cross the blood-brain barrier (BBB)\u003csup\u003e7-9\u003c/sup\u003e, thereby circulating through the bloodstream. Since EVs mirror the cell and tissue of origin in terms of disease outcome and severity, their contents can serve as non-invasive biomarkers for various diseases\u003csup\u003e6,10\u003c/sup\u003e and plasma EVs can be used as biomarkers of neurological disorders\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEV proteins are promising liquid biopsy targets for early detection of Parkinson\u0026rsquo;s disease, as their profiles change in disease conditions\u003csup\u003e12\u003c/sup\u003e. Three plasma EV proteins (clusterin, complement C1r subcomponent, and apolipoprotein A1) could serve as diagnostic biomarkers for Parkinson\u0026rsquo;s disease, and the expression of EV proteins is associated with disease progression\u003csup\u003e12\u003c/sup\u003e. Plasma EV proteins could also help distinguish Alzheimer\u0026rsquo;s disease (AD) patients from healthy controls\u003csup\u003e13\u003c/sup\u003e. However, no specific EV protein biomarkers have been yet identified for ASD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we used size exclusion chromatography (SEC) to isolate EVs from the plasma of healthy controls (HCs) and ASD cases from Qatari and non-Qatari individuals living in Qatar. We then applied the proximity extension assay (PEA) Olink platform to analyze the EV proteome profiles. We identified the top five downregulated proteins in plasma EVs isolated from ASD cases and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis.\u003c/p\u003e"},{"header":"Material and Methods ","content":"\u003ch2\u003e1.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Study cohort and blood collection\u003c/h2\u003e\n\u003cp\u003eAll procedures were performed under the approval of the Institutional Review Board (IRB# 2018-024) of Qatar Biomedical Research Institute (QBRI). The study cohort was obtained from QBRI\u0026rsquo;s Interdisciplinary Research Program (IDRP) depository and included plasma samples from 81 ASD and 26 healthy control (HCs) individuals, all residing in Qatar. The communication and social skills for the HCs were evaluated using the Social Communication Questionnaire (SCQ). ASD individuals were clinically diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Written informed consent and assent were obtained from all the HCs, ASD individuals and their surrogates. Demographical information of the participants is summarized in \u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eHuman peripheral blood samples were drawn from the HCs and ASD individuals into EDTA tubes. In the processing of blood samples, blood components were separated using density gradient centrifugation as previously described\u003csup\u003e11\u003c/sup\u003e. The plasma supernatant was further centrifuged for 15 minutes to remove platelets and blood cells to obtain platelet-free plasma, which was aliquoted and stored at \u0026minus;80\u0026deg;C until further use for EV isolation.\u003c/p\u003e\n\u003ch2\u003e1.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Extracellular vesicles isolation\u003c/h2\u003e\n\u003cp\u003eEVs were isolated from 250uL of human plasma by size exclusion chromatography (SEC) using the qEVOriginal/35nm columns (SP5, Izon Science, Christchurch, New Zealand). The plasma was thawed on ice and diluted with PBS for a final volume of 500uL. It was then centrifuged at 3000g for 10 min and 10,000g for 30 min to remove cell debris and large vesicles, followed by purification on the SEC columns. Fractions 1-25, including void volume were collected for some samples to verify the particle/protein profile. For the remaining samples, the void volume was discarded and only the high particle/low protein fractions were collected. EV-enriched fractions 1 and 2 with total volume of 1 mL, were pooled and concentrated using pre-conditioned 100 KDa Amicon Ultra-15 centrifuge filters (UFC9100, Millipore) to a final volume of 170 \u0026micro;L. The amount of EV proteins was estimated by measuring the absorbance at 280\u0026thinsp;nm (A280).\u0026nbsp;EV samples were aliquoted to minimize the freeze-thaw cycles and stored at \u0026minus;80\u0026deg;C until further analyzed.\u003c/p\u003e\n\u003ch2\u003e1.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Nanoparticle tracking analysis (NTA)\u003c/h2\u003e\n\u003cp\u003eParticle size and particle number were determined using\u0026nbsp;nanoparticle tracking analysis (NTA)\u0026nbsp;(ZetaView, Particle Metrix, Germany).\u0026nbsp;EV samples were diluted with filtered PBS to an average of 100 particles per frame and a final volume of 1 mL. Zetaview software (version 8.04.02 SP2) recorded particles at 11 camera positions and 30 frames per second.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e1.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Transmission electron microscopy (TEM)\u003c/h2\u003e\n\u003cp\u003eFive microliter of EV suspension was deposited on carbon-coated 400-mesh copper grids (CF400-CU, Electron Microscopy Sciences) and incubated for 10 min. The EVs were then washed with ddH\u003csub\u003e2\u003c/sub\u003eO and excess fluid was absorbed with filter paper. Grids were negatively stained with uranyl acetate and embedded in methylcellulose-uranyl acetate. EVs were examined at 80 kV in Talos F200C Transmission Electron Microscope (Thermo Fisher Scientific). The images were acquired using bottom-mounted CETA camera.\u003c/p\u003e\n\u003ch2\u003e1.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Olink proximity extension assay\u003c/h2\u003e\n\u003cp\u003eProtein profiling of EV samples was carried out using the proximity extension assay (PEA) from the Olink Target 96, testing a total of 1196 proteins (13 panels including Neurology, Development, Neuro-exploratory, Inflammation, Immune Response, Cell Regulation, Organ Damage, Metabolism, Oncology II, Oncology III, Cardiometabolic, Cardiovascular II, and Cardiovascular III; Olink Bioscience, Uppsala, Sweden). Following the standard protocol, the runs were performed by Olink-certified proteomics core facility at QBRI and were all validated by the Olink support team in Uppsala, Sweden. PEA is an ultrasensitive technology based on dual recognition of target proteins through matched pairs of antibodies labeled with DNA oligonucleotides\u003csup\u003e14\u003c/sup\u003e. Quality control and data normalization were carried out using the Normalized Protein eXpression (NPX) software. Protein expression values were calculated as NPX; NPX is an arbitrary unit by Olink to quantify protein expression level on a log2 scale. Olink data that did not pass quality control were excluded from the analyses.\u003c/p\u003e\n\u003ch2\u003e1.6\u0026nbsp; \u0026nbsp; \u0026nbsp;Bioinformatics\u003c/h2\u003e\n\u003cp\u003eThe analysis for EV characterization experiments was conducted using GraphPad Prism software. Statistical analysis for proteomics data was performed using R software. Differentially expressed proteins were identified using the Limma (Linear Models for Microarray Data) package in R. The \u003cem\u003ep\u003c/em\u003e-values of all the proteins were adjusted for multiple testing using the Benjamini\u0026ndash;Hochberg (BH) method.\u0026nbsp;The analytical model accounted for age, sex, and EV particle counts as influential covariates.\u0026nbsp;Top differentially expressed proteins (TopDEPs) were selected based on\u0026nbsp;a dual criterion: a fold change (FC \u0026ge; 2) and a BH-adjusted \u003cem\u003ep\u003c/em\u003e-value (adj \u003cem\u003ep\u003c/em\u003e-value \u0026le; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor Gene Ontology (GO) Enrichment Analysis, the R library clusterProfiler was used with focus on Cellular\u0026nbsp;Components and Biological Process, and enriched GO terms passing the\u0026nbsp;threshold of adjusted \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 were identified. For Machine Learning, variable selection was performed using the MUVR, Boruta, and VSURF R packages, all set to their default parameters for optimal performance. The MLR3 library served as the foundation for training and evaluating an array of methods through repeated 4-fold cross validation. The MLR3 library also provides a function to create an ROC curve averaged over all validation folds and calculate the 95% Confidence Interval.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStudy cohort characteristics\u003c/h2\u003e\n\u003cp\u003eWe recruited 109 participants for our study, consisting of 81 ASD cases and 26 healthy controls (HCs) who were aged between 6 and 15 years. The mean ages for ASD cases and HCs were 8.56 ± 2.17\u0026 and 11.08 ± 2.20, respectively (\u003cstrong\u003eTable 1\u003c/strong\u003e). The HC group had an equal proportion of males and females (50%); however, the ASD group had a higher percentage of males (79%) due to the male predominance of ASD, which can be as high as 4:1\u003csup\u003e15\u003c/sup\u003e. All ASD cases had a clinical diagnosis of ASD based on DSM-5 criteria and were evaluated using the ADOS-2 score. \u003cstrong\u003eTable 1\u003c/strong\u003e shows the demographical information of ASD cases and HCs.\u003c/p\u003e\n\u003ch2\u003eCharacterization of EVs isolated from blood plasma of ASD and HCs\u003c/h2\u003e\n\u003cp\u003eWe have optimized the protocol of plasma EV isolation using\u0026nbsp;size exclusion chromatography (SEC), as previously described\u003csup\u003e11\u003c/sup\u003e. The larger molecules elute first from the SEC column, followed by EVs, and plasma protein complexes are the last to elute (\u003cstrong\u003eFigure 1A,B\u003c/strong\u003e). We measured the particle number of EVs in each fraction by using nanoparticle tracking analysis (NTA). We combined fractions 2 and 3 as EV samples for higher purity, and also monitored the absorbance at 280 nm for protein elution profiles (\u003cstrong\u003eFigure 1A,B\u003c/strong\u003e). We confirmed that abundant plasma proteins are removed from EV samples to improve the purity\u003csup\u003e11\u003c/sup\u003e and soluble protein elution increases sharply from fraction 5; the elution profiles of EVs and plasma proteins obtained by SEC did not reveal any differences between HCs and ASD cases (\u003cstrong\u003eFigure 1A,B\u003c/strong\u003e). We then further analyzed the EV samples from fractions 2 and 3.\u003c/p\u003e\n\u003cp\u003eThe plasma EVs were measured by NTA to determine their size distribution. The results showed that the plasma EVs from HCs and ASD had similar sizes, ranging from 50 to 200 nm; mean diameter (nm) of 121.3 ± 40.32 SD for HCs and 120.3 ± 40.53 SD for ASD (\u003cstrong\u003eFigure 1C,D\u003c/strong\u003e). Intriguingly, the number of EV particles was reduced in ASD plasma samples (\u003cstrong\u003eFigure 1E\u003c/strong\u003e), while the plasma protein concentration was similar between HCs and ASD (\u003cstrong\u003eFigure 1F\u003c/strong\u003e); we used 0.25 mL plasma for this study. The median diameter of EVs did not differ between HCs and ASD (\u003cstrong\u003eFigure 1G\u003c/strong\u003e). The structure of HCs- and ASD-derived EVs characterized by atomic force microscopy (AFM) was comparable (\u003cstrong\u003eFigure 1H\u003c/strong\u003e). Overall, these data suggest that there were no significant differences in the morphology and size distribution, but the EV number was lower in ASD plasma.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEV protein profiling using the Olink platform\u003c/h2\u003e\n\u003cp\u003eOlink analysis of 1196 proteins demonstrated a distinct plasma EVs protein expression profile in individuals with ASD compared to HCs (see \u003cstrong\u003eMethod\u003c/strong\u003e section for details). Differentially expressed proteins were identified using \u003cem\u003eLimma\u003c/em\u003e package in R. A list of the top differentially expressed proteins (TopDEPs) was summarized with a BH-adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 and fold change (FC)\u0026nbsp;³\u0026nbsp;2. A total of five downregulated proteins in ASD EVs were listed in the TopDEPs; no proteins were upregulated in ASD EVs (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Further details of all the significantly downregulated proteins are listed in \u003cstrong\u003eTable 2\u003c/strong\u003e. Top five downregulated proteins include WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha)(\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGene Ontology enrichment analysis of TopDEPs\u003c/h2\u003e\n\u003cp\u003eWe performed Gene Ontology (GO) enrichment analysis of TopDEPs to evaluate functional annotation of these downregulated proteins in ASD EVs. The cellular components in GO analysis only included external side of plasma membrane (\u003cstrong\u003eFigure 2C\u003c/strong\u003e), supporting that TopDEPs are derived from EVs. Top five significantly downregulated proteins are associated with EVs and can be potential biomarkers for ASD (see \u003cstrong\u003eDiscussion\u003c/strong\u003e); WWP2, HSP27, CLEC1B, CD40, and FRalpha.\u003c/p\u003e\n\u003cp\u003eNext, we performed GO enrichment analysis\u0026nbsp;of TopDEPs\u0026nbsp;to identify the biological processes that were significantly dysregulated in ASD EVs (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). We sorted the enriched biological terms by counts, which represent numbers of proteins associated with each term. The immune responses including inflammation and cytokine production were affected by downregulated proteins in ASD EVs (\u003cstrong\u003eFigure 2D\u003c/strong\u003e), implying that ASD EVs might be associated with immune dysregulation.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMachine learning to identify potential biomarkers\u003c/h2\u003e\n\u003cp\u003eTo further demonstrate potential biomarkers for ASD, we applied machine learning algorithms including minimally biased variable selection in R (MUVR), Boruta, and variable selection using random forests (VSURF) (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u0026nbsp;We used three different feature selection methods to identify the most potential proteins for predicting the outcome, and then compared the performance of different classification algorithms. Six proteins overlapped between MUVR and Boruta, and four proteins were among MUVR, Boruta, and VSURF: WWP2, CD40, PAR1, FRalpha, CLEC1B, and HSP27 (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). Details of six proteins are listed in \u003cstrong\u003eTable 2\u003c/strong\u003e; note that five out of these six proteins were also found to from the list of TopDEPs.\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance was tested using multiple multivariant supervised machine learning algorithms (random forest, generalized linear model, and support vector machines (SVM)). Six proteins were internally validated with four-fold cross-validations and 100 repeats\u0026nbsp;(\u003cstrong\u003eFigure 3B,C\u003c/strong\u003e). The average ROC curve suggested that six proteins are strong candidates for diagnostic biomarkers for ASD with average AUC = 0.923, accuracy = 86.3%, sensitivity = 95.3%, specificity = 66.2% (\u003cstrong\u003eFigure 3C\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eASD affects approximately 1% of the global population, creating a significant public health burden in different communities including Qatar. According to our QBRI study on ASD\u003csup\u003e16\u003c/sup\u003e, the prevalence of ASD in Qatar is 1.14% (one in every 87 children), leading to the financial burden and stress on parents and caregivers.\u0026nbsp;Early intervention, whether through medication or behavioral therapy, can alleviate some ASD-related symptoms, significantly improving the life-quality of the affected individuals\u003csup\u003e17-19\u003c/sup\u003e. Currently, early detection and intervention of ASD are highly limited and there are no medical kits or blood tests available for ASD diagnosis. Medical doctors can only check the child\u0026apos;s behavior and development to make a diagnosis of ASD, thereby limiting early intervention of ASD until kids become at least 4 or 5 years old. Early intervention and detection are critical to help ASD children effectively improve their language ability and social interaction. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the literature, several genetic variants have been proposed as promising biomarkers for ASD\u003csup\u003e20\u003c/sup\u003e. Yet, because of the numerous gene mutations, ASD is extremely heterogenous and cannot be defined by unique polymorphisms. Other studies have identified differences in the microbiota and metabolic, immune, and nutritional markers, between control and ASD individuals\u003csup\u003e21-23\u003c/sup\u003e. These potential biomarkers are all yet to be confirmed by large validation studies which can turn out to be extremely challenging. The various findings do, however, present valuable clues into the underlying molecular mechanisms and as to which biological processes are affected in ASD. In the present study, we have isolated and characterized plasma EVs in ASD and control individuals. We performed an extensive proteomics profiling, screening over 1000 proteins, of which 5 are significantly downregulated in ASD EVs. To our knowledge, this study is the first and unique to investigate the EV protein cargo in ASD.\u003c/p\u003e\n\u003cp\u003eTop five significantly downregulated proteins are related to EV biogenesis, function and signaling: 1) WWP2, an E3 ubiquitin ligase, regulates EV release by ubiquitination of EV proteins\u003csup\u003e24\u003c/sup\u003e; 2) HSP27 is a heat shock protein, which is elevated in the blood in various diseases\u003csup\u003e25\u003c/sup\u003e and extracellular HSP27 may have functions in pathological conditions\u003csup\u003e26\u003c/sup\u003e. HSP27 is present in EVs released from THP-1 cells\u003csup\u003e27\u003c/sup\u003e and can be transferred to recipient cells via EVs\u003csup\u003e25\u003c/sup\u003e; 3) CLEC1B is a receptor involved in transmembrane signaling\u003csup\u003e28\u003c/sup\u003e and is highly expressed in neuron-derived exosomes\u003csup\u003e29\u003c/sup\u003e; 4) CD40 is a protein present in plasma EVs from non-Hodgkin lymphoma patients\u003csup\u003e30\u003c/sup\u003e and tumor-derived EVs\u003csup\u003e31\u003c/sup\u003e, suggesting its potential as a cancer biomarker; 5) FRalpha is present in EVs and involved in folate transport into the brain through EVs\u003csup\u003e32\u003c/sup\u003e. Altogether, our data support that these five proteins may serve as useful EV biomarkers for ASD diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the proteins we show to be downregulated in ASD individuals is HSP27 which is thought to have major protective effects against many cellular stresses\u003csup\u003e33\u003c/sup\u003e. This was in accordance with a previously published study evaluating protein levels in the blood of ASD children and found HSP27 to be decreased\u003csup\u003e34\u003c/sup\u003e. Over-expression of HSP27 has been shown to protect and rescue neuronal and non-neuronal cells from cell damage and death\u003csup\u003e33,35\u003c/sup\u003e. The downregulation seen in our results indicate a potential susceptibility of ASD neurons to cell death.\u003c/p\u003e\n\u003cp\u003eDue to the limited accessibility to the brain and cerebrospinal fluid (CSF) for biomarker discovery, blood is ideal for liquid biopsy, given its easier accessibility and non-invasive collection\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e. EVs are very attractive diagnostic and therapeutic tools, particularly for brain disorders, because of their property to cross the BBB\u003csup\u003e7-9\u003c/sup\u003e. Thus, plasma EVs provide a potential therapeutic approach to neurological disorders. Brain-derived EVs might provide biomarkers for neuronal disorders, and EVs can be used in therapeutics as a drug delivery system to the brain\u0026nbsp;\u003csup\u003e37,38\u003c/sup\u003e. EV proteins and RNA are considered promising biomarkers for neurodegenerative disease and neurodevelopmental disorders\u0026nbsp;\u003csup\u003e8,29,39\u003c/sup\u003e. Our data support that five EV proteins can pave the way for early diagnosis of ASD as novel biomarkers and have the potential to enhance diagnostic accuracy and facilitate earlier intervention strategies.\u003c/p\u003e\n\u003cp\u003eNoteworthy is the connection of the five TopDEPs\u0026nbsp;identified in\u0026nbsp;ASD EVs\u0026nbsp;with immune responses and cytokine production\u0026nbsp;(\u003cstrong\u003eFigure 2D\u003c/strong\u003e). This suggests that ASD EVs may play a role in modulating chronic inflammation. While chronic inflammation and immune dysregulation\u0026nbsp;have been proposed as potential contributors to the characteristic features of autism\u003csup\u003e40\u003c/sup\u003e, the mechanisms by which ASD EVs regulate chronic inflammation remain to be elucidated in further studies.\u003c/p\u003e"},{"header":"Abbreviation ","content":"\u003cp\u003eASD: \u0026nbsp;Autism spectrum disorder\u003c/p\u003e\n\u003cp\u003eAFM: Atomic force microscopy\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer\u0026rsquo;s disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eADHD: Attention-deficit hyperactivity disorder\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBBB: Blood-brain barrier\u003c/p\u003e\n\u003cp\u003eCLEC1B: C-type lectin domain family 1 member B\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCD40: Cluster of differentiation 40\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e\n\u003cp\u003eEVs: Extracellular vesicles\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFralpha: Folate receptor alpha\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFC: Fold change\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCs: Healthy controls\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHSP27: Heat shock protein 27\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMUVR: Minimally biased variable selection in R\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNTA: Nanoparticle tracking analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPX: Normalized Protein eXpression\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAR1:\u0026nbsp;Protease-activated receptor-1\u003c/p\u003e\n\u003cp\u003ePEA: Proximity extension assay\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEC: Size exclusion chromatography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSVM: Support vector machines\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTopDEPs: Top differentially expressed proteins\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVSURF: Variable selection using random forests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWWP2: WW domain-containing protein 2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were performed under the approval of the Institutional Review Board (IRB# 2018-024) of Qatar Biomedical Research Institute (QBRI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the grant from Qatar Biomedical Research Institute (Project Number SF 2019 004 and IGP5-2022-001 to Y.P.) and the HBKU Thematic Research Grant (Project Number VPR-TG02-06 to Y.P.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Y.A.M. and K.C.S contributed to EV isolation and experiments. A.F. contributed to bioinformatics analysis. I.B. and H.B.A. contributed to Olink. F.A.A. recruited plasma samples. J.P. and S.M. contributed to TEM. L.W.S., S.A.A. contributed to conceptualization, design, and supervision. Y.P. contributed to conceptualization and design, funding acquisition, project management, resources, supervision, and review and editing. H.Y.A.M. and Y.P. wrote the manuscript and all authors read it and provided their comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Dr. Salam Salloum-Asfar, Dr. Areej Mesleh, Rowaida Z Taha, Iman Ghazal, and Fatema Al-Faraj for sample recruitment and collection. We thank QBRI’s Proteomics Core Labs, the HBKU Core Labs for the TEM support, and Sidra Medicine for NTA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eDiagnostic and statistical manual of mental disorders: DSM-5\u0026trade;, 5th ed\u003c/em\u003e. (American Psychiatric Publishing, Inc., 2013).\u003c/li\u003e\n\u003cli\u003eState, M. W. \u0026amp; Levitt, P. The conundrums of understanding genetic risks for autism spectrum disorders. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1499-1506 (2011). https://doi.org/10.1038/nn.2924\u003c/li\u003e\n\u003cli\u003eCarter, M. T. \u0026amp; Scherer, S. W. Autism spectrum disorder in the genetics clinic: a review. \u003cem\u003eClin Genet\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 399-407 (2013). https://doi.org/10.1111/cge.12101\u003c/li\u003e\n\u003cli\u003eMulcahy, L. A., Pink, R. C. \u0026amp; Carter, D. R. 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S. HSP27 and cell survival in neurones. \u003cem\u003eInt J Hyperthermia\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 393-402 (2005). https://doi.org/10.1080/02656730400023664\u003c/li\u003e\n\u003cli\u003eTsukurova, L. A. [A neuroprotective approach to optimizing treatment and correction activities in children with autism spectrum disorders]. \u003cem\u003eZh Nevrol Psikhiatr Im S S Korsakova\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 51-56 (2018). https://doi.org/10.17116/jnevro20181185251\u003c/li\u003e\n\u003cli\u003eDave, K. M.\u003cem\u003e et al.\u003c/em\u003e Mitochondria-containing extracellular vesicles (EV) reduce mouse brain infarct sizes and EV/HSP27 protect ischemic brain endothelial cultures. \u003cem\u003eJ Control Release\u003c/em\u003e \u003cstrong\u003e354\u003c/strong\u003e, 368-393 (2023). https://doi.org/10.1016/j.jconrel.2023.01.025\u003c/li\u003e\n\u003cli\u003eMarrugo-Ramirez, J., Mir, M. \u0026amp; Samitier, J. Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e (2018). https://doi.org/10.3390/ijms19102877\u003c/li\u003e\n\u003cli\u003eYoo, Y. K.\u003cem\u003e et al.\u003c/em\u003e Toward Exosome-Based Neuronal Diagnostic Devices. \u003cem\u003eMicromachines (Basel)\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e (2018). https://doi.org/10.3390/mi9120634\u003c/li\u003e\n\u003cli\u003eMustapic, M.\u003cem\u003e et al.\u003c/em\u003e Plasma Extracellular Vesicles Enriched for Neuronal Origin: A Potential Window into Brain Pathologic Processes. \u003cem\u003eFront Neurosci\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 278 (2017). https://doi.org/10.3389/fnins.2017.00278\u003c/li\u003e\n\u003cli\u003eGuix, F. X.\u003cem\u003e et al.\u003c/em\u003e Detection of Aggregation-Competent Tau in Neuron-Derived Extracellular Vesicles. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e (2018). https://doi.org/10.3390/ijms19030663\u003c/li\u003e\n\u003cli\u003eArteaga-Henriquez, G., Gisbert, L. \u0026amp; Ramos-Quiroga, J. A. Immunoregulatory and/or Anti-inflammatory Agents for the Management of Core and Associated Symptoms in Individuals with Autism Spectrum Disorder: A Narrative Review of Randomized, Placebo-Controlled Trials. \u003cem\u003eCNS Drugs\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 215-229 (2023). https://doi.org/10.1007/s40263-023-00993-x\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Participants\u0026rsquo; demographical information.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eASD Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32%\"\u003e\n \u003cp\u003eHealthy Controls (HCs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38%\"\u003e\n \u003cp\u003eNumber of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003eN = 81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32%\"\u003e\n \u003cp\u003eN = 26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38%\"\u003e\n \u003cp\u003eAge (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e8.56 \u0026plusmn; 2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32%\"\u003e\n \u003cp\u003e11.08 \u0026plusmn; 2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38%\"\u003e\n \u003cp\u003eGender (F/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30%\"\u003e\n \u003cp\u003e17 / 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32%\"\u003e\n \u003cp\u003e13 / 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38%\" valign=\"top\"\u003e\n \u003cp\u003eADOS-2 scores (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30%\" valign=\"top\"\u003e\n \u003cp\u003e3.77 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Predictive proteins using MUVR, Boruta, and VSURF.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eProtein Symbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eProtein Full Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\"\u003e\n \u003cp\u003eGini Impurity Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003eFold Change (FC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eWWP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eWW Domain Containing E3 Ubiquitin Protein Ligase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e1.80\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCD40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eCD40 Molecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eCLEC1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eC-Type Lectin Domain Family 1 Member B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e2.23\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003ePAR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eProtease-activated receptor-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eHSP27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eHeat shock protein 27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e2.34\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.207207207207207%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eFRalpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.48348348348348%\"\u003e\n \u003cp\u003eFolate Receptor alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.066066066066067%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.966966966966968%\"\u003e\n \u003cp\u003e\u0026darr;\u0026nbsp;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.165165165165165%\"\u003e\n \u003cp\u003e2.71\u0026nbsp;\u0026acute;\u0026nbsp;10\u003csup\u003e-4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Extracellular vesicle, biomarker, Olink","lastPublishedDoi":"10.21203/rs.3.rs-4212009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4212009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by symptoms that include social interaction deficits, language difficulties and restricted, repetitive behavior. Early intervention through medication and behavioral therapy can eliminate some ASD-related symptoms and significantly improve the life-quality of the affected individuals. Currently, the diagnosis of ASD is highly limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003es To investigate the feasibility of early diagnosis of ASD, we tested extracellular vesicles (EVs) proteins obtained from ASD cases. First, plasma EVs were isolated from healthy controls (HCs) and ASD individuals and were analyzed using proximity extension assay (PEA) technology to quantify 1196 protein expression level. Second, machine learning analysis and bioinformatic approaches were applied to explore how a combination of EV proteins could serve as biomarkers for ASD diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e No significant differences in the EV morphology and EV size distribution between HCs and ASD were observed, but the EV number was slightly lower in ASD plasma. We identified the top five downregulated proteins in plasma EVs isolated from ASD individuals: WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha). Machine learning analysis and correlation analysis support the idea that these five EV proteins can be potential biomarkers for ASD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e We identified the top five downregulated proteins in ASD EVs and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis.\u003c/p\u003e","manuscriptTitle":"Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 17:56:51","doi":"10.21203/rs.3.rs-4212009/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8dde2b5d-f145-408d-83e5-81936d289544","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-13T02:44:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 17:56:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4212009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4212009","identity":"rs-4212009","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}