Whole-Transcriptome Analysis of Serum L1CAM-Captured Extracellular Vesicles Reveals Neural and Glycosylation Changes in Autism Spectrum Disorder

The pathophysiology of autistic spectrum disorder (ASD) is not fully understood and there are no diagnostic or predictive biomarkers. Extracellular vesicles (EVs) are cell-derived nano-sized vesicles, carrying nucleic acids, proteins, lipids, and other bioactive substances. As reported, serum neural cell adhesion molecule L1 (L1CAM)-captured EVs (LCEVs) can provide reliable biomarkers for neurological diseases; however, little is known about the LCEVs in children with ASD. The study enrolled 100 children with ASD (2.5–6 years of age; 90 males) and 60 age-matched TD children (54 males) as control. The serum sample was collected and pooled into five ASD subgroups and three TD subgroups (n = 20). LCEVs were isolated and characterized meticulously. Whole-transcriptome of LCEVs was analyzed by lncRNA microarray and RNA-sequencing. All raw data was submitted on GEO Profiles, and GEO accession numbers is GSE186493. RNAs expressed differently in LCEVs from ASD sera vs. TD sera were screened, analyzed, and further validated. A total of 1418 mRNAs, 1745 lncRNAs, and 11 miRNAs were differentially expressed, and most of them were downregulated in ASD. Most RNAs were involved in neuron- and glycan-related networks implicated in ASD. The levels of EDNRA, SLC17A6, HTR3A, OSTC, TMEM165, PC-5p-139289_26, and hsa-miR-193a-5p were validated in at least 15 ASD and 15 TD individual serum samples, which were consistent with the results of transcriptome analysis. In conclusion, whole-transcriptome analysis of serum LCEVs reveals neural and glycosylation changes in ASD, which may help detect predictive biomarkers and molecular mechanisms of ASD, and provide reference for diagnoses and therapeutic management of the disease.


Introduction
Autism spectrum disorder (ASD) refers to a set of earlyappearing social communication deficits and repetitive sensory-motor behaviors associated with a strong genetic component as well as other causes (Lord et al. 2018). The prevalence of ASD is region-specific, varying from 0.9 case out of 1000 children in India to 1 case within 59children in the USA (Hyman et al. 2020). In China, the prevalence is now around 1% (Sun et al. 2019). Screening of high-risk population is the first step toward early detection and diagnosis of ASD, thereby influencing the likelihood of patients accessing early intervention and, importantly, improving long-term outcomes. Currently, clinical diagnosis of ASD is still based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) (American Psychiatric Association 2013). Such diagnosis is focused on assessing patients' behaviors, but it lacks quantizable indicators and cannot accurately identify mild or non-typical autism or autism in very young patients (Weiss et al. 2009). There is a need to find useful and reliable biomarkers to facilitate the diagnosis of autism.
In the past decade, searching for genetic biomarkers has been a hot spot in ASD research. Numerous related genes have been reported, including NRXN1, SHANK3, SHANK2, MECP2, SNC2A, CHD8, DYRKIA, POG2, GRIN2B, KATNAL2, NLGN3, NLGN4, CNTN4, CDH10, CDH9, and SEMA5A (Kramer et al. 2020;Sanders et al. 2012;Wakefield 2016). Unfortunately, only 10-38% of ASD cases have been reported with known genetic deficits (Lin et al. 2021;Vorstman et al. 2017). In recent years, blood/ serum biomarkers have drawn much attention due to their accessibility, low cost, and rapid detection. In our previous studies, we identified four candidate peptides (SerpinA5-A, ApoC1-A, FABP1-A, and PF4-A) (Yang et al. 2018) and α2-3-linked sialylation of apolipoprotein D (APOD) (Qin et al. 2017) as potential biomarkers for ASD. A recent study also reported that SLC25A12, LIMK1, and RARS might serve as potential blood protein biomarkers for ASD (Yao et al. 2021). However, the reported potential biomarkers cannot specifically reflect the abnormality of brain neurons preferentially affected in autism and reveal the dysregulation of specific genes in neurons correlated with clinical severity (Jin et al. 2020;Velmeshev et al. 2019).
Extracellular vesicles (EVs) are a heterogeneous group of nano-sized, cell-derived membranous structures comprising exosomes, microvesicles, and others. They contain components from the cells that release them, such as proteins, lipids, nucleic acids, and glycoconjugates. In the central nervous system, almost all types of cells secrete EVs, which mediate neuron-glial cell communication, promote neuronal repair and growth, and promote the progression of glioblastoma and neurological diseases (Fayazi et al. 2021). EVs are structurally stable and protect the "biological cargo" they carry from degradation and denaturation in the extracellular environment. Compared with biological fluids, such as cerebrospinal fluid, blood, or urine, EVs can provide more reliable and accurate biomarkers for neurological diseases (Filippone and Pratico 2021;Wang et al. 2021). More importantly, they can cross the blood-brain barrier and have low immunogenicity (Saint-Pol et al. 2020). Overall, EVs are a promising source for biomarkers and ideal vehicles for drug delivery, and might be used in the diagnosis and treatment of neurological diseases (Andjus et al. 2020;Hill 2019). Currently, it is found that secreted EVs increase in the serum of ASD children and contain IL-1β that stimulates secretion of human microglia cells (Tsilioni and Theoharides 2018). As has been reported, mesenchymal stem cell-derived exosomes can improve autism-like behavior in BTBR mice and may be a cell-free therapeutic tool for ASD (Alessio et al. 2020;Perets et al. 2020). These findings uncover important roles of EVs, suggesting the necessity of characterizing the detailed molecular status of brain-derived EVs in ASD.
Recent studies have shown that the surface of EVs derived from neurons carries neural cell adhesion molecule L1 (L1CAM) that can be utilized to isolate the specific EVs from serum/plasma (Goetzl et al. 2018(Goetzl et al. , 2020Nogueras-Ortiz et al. 2020). Proteins in L1CAM captured exosomes can reflect brain injury, progression from acute mild traumatic brain injury to chronic traumatic brain disease, cognitive dysfunction caused by HIV infection, and neurological abnormalities such as Alzheimer's disease (Goetzl et al. 2018(Goetzl et al. , 2020. Blood neuron-derived EVs (using anti-L1CAM antibody) from Alzheimer's disease patients effect complement-mediated neurotoxicity (Nogueras-Ortiz et al. 2020). However, the expression of proteins or RNAs in L1CAM captured EVs (LCEVs) of ASD children is rarely reported.
It is well known that EVs miRNAs can be used as potential diagnostic and prognostic biomarkers as well as therapeutic tools for a variety of neuropsychiatric diseases, such as dementia, Alzheimer's disease, depression, and schizophrenia (Fries and Quevedo 2018;Hill 2019). In light of this, the present study was conducted to examine the expression of RNAs in LCEVs from children with ASD and to reveal the potential biomarkers and possible mechanisms of the disease. We collected serum samples from 100 ASD children and 60 age-matched typically developed (TD) children. LCEVs were isolated using L1CAM antibody mediated immunosorbent assay and characterized by nanoparticle tracking analysis, transmission electron microscopy and western blot. Whole-transcriptome of the LCEVs was analyzed by lncRNA microarray and RNA-sequencing. In brief, a total of 1418 mRNAs, 1745 lncRNAs and 11 miRNAs were found differentially expressed. Most of these RNAs were downregulated in ASD and enriched in neuron-related and glycan-related networks associated with ASD. Levels of some potential biomarkers were found significantly changed in ASD.

Study Approval
Approval for this study was obtained from the Ethics Committee of Xi'an Jiaotong University (Xi'an, China). A parent of each participant signed a written informed consent. The experiments were carried out in accordance with the ethical guidelines of the Declaration of Helsinki.

Subjects
The study enrolled 100 children with ASD (2.5-6 years of age; 90 males) and 60 age-matched TD children (54 males) as control. The ASD children were recruited from Xi'an Children's Hospital, Xi'an, China. The healthy children were recruited from the same region to minimize the influence of different environments. A developmental behavioral pediatrician and a pediatric neurologist or psychiatrist examined the ASD children. All the consultants agreed on ASD diagnosis according to the DSM-V criteria. Children with tuberous sclerosis complex, Rett syndrome, Prader Willi syndrome, or Angelman syndrome were excluded. All the participants were screened via a parental interview for current and past physical illnesses. Those who had any type of infection or disease within two weeks before the time of examination or take any medicines were excluded. ASD was evaluated with the autism diagnostic observation schedule (Table 1).

Collection and Grouping of Serum Samples
Venous blood samples were collected by a pediatric nurse. The blood was allowed to clot at room temperature for 30 min, and the clot was then removed by centrifuging at 1,500 × g for 10 min. The resulting supernatant is immediately transferred to a clean polypropylene tube, and EDTAfree inhibitor cocktail (Halt protease inhibitor; Thermo Scientific Pierce Protein Research Products, Rockford, IL, USA) was added at a concentration of 10 μL/mL serum. The obtained serum was aliquot into small portions and was immediately frozen on dry ice and stored at − 80 °C. To tolerate individual variation, 25 μL of each serum sample was collected and every 20 samples were pooled into one subgroup. Altogether, we got 5 ASD subgroups and 3 TD subgroups (n = 20) for lncRNA microarray detection and RNA sequencing. To avoid bias caused by gender difference, proportion of males in each subgroup was the same (90%). The remaining serum in each sample was maintained for further individual validation.

nFCM Analysis
LCEVs suspension at a concentration between 1 × 10 7 /mL and 1 × 10 9 /mL was examined using a Flow NanoAnalyzer (NanoFCM, Xiamen, China) to determine the size and quantity of particles isolated as described before (Tian et al. 2020). Briefly, the sample stream is completely illuminated within the central region of the focused laser beam, and the detection efficiency is approximately 100%. The concentration of each LCEV sample was determined by employing 100 nm orange FluoSpheres of known particle concentration to calibrate the sample flow rate.

Transmission Electron Microscopy (TEM)
LCEVs solution (20 μL) was dropped on a copper grid, stained with 2% phosphor tungstic acid for 10 min, and dried under incandescent light for 2 min. The copper grid was observed and photographed using a transmission electron microscope (H-7650 Hitachi microscope; Hitachi, Tokyo, Japan).

Cytoflex Flow Cytometer Analysis
The LCEVs solution (5-10 μL) was diluted by phosphate balanced solution, and diluted into three concentration gradients of 1/10, 1/100, and 1/1000. The Cytoflex Flow Cytometer (Beckman, USA) was preliminary used to detect the number of EV particles to select the most suitable dilution concentration (the average particle number is less than 10,000). Then, sample was incubated with 1 μg of PE-anti-mouse/cat CD81 (104905, Biolegend) or APC-CD63 (143905, Biolegend) antibodies for 15 min at room temperature. After the incubation, the Cytoflex Flow Cytometer was used to detect the expression of markers.

Extraction of Total RNA in LCEVs
Total RNA in LCEVs was isolated using the Exosomal RNA isolation kit (NorgenBiotek, 58000) according to the manufacturer's instructions. Briefly, 200 μL of the transferred supernatant containing purified LCEVs was incubated with 300 μL Lysis Buffer A and 37.5 μL Lysis Additive B at room temperature for 10 min, following which 500 μL of 96-100% Ethanol was added and mixed well via 10 s vortexing. Then, 500 μL of the mixture was transferred into a Mini Spin column and centrifuged at 3,000 × g for 1 min, and the remaining mixture was transferred and centrifuged by repeating the steps. After that, 600 μL Wash Solution A was applied and the column was centrifuged at 3,300 × g for 30 s twice. The spin column was then moved to a fresh 1.7 mL Elution tube, and 50 μL Elution Solution A was added. Finally, centrifugation was performed at 400 × g for 1 min and 5,800 × g for 2 min to obtain total RNA.

Human lncRNA Microarray and Data Analysis
Total RNA was purified using an RNeasy Mini Kit (Qiagen, Germany) and was checked for a RIN number to inspect RNA integration with an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US). LC Biotech Human lncRNA Microarray 4 × 180 K (Agilent Technologies; Santa Clara, CA) was utilized to detect the expression of mRNAs and lncRNAs in LCEVs. The microarray slide contained 26,083 mRNA probes and 1,05,135 lncRNA probes, and lncRNA sequencing data were obtained from Gencode, UCSC, Ensembl, Refseq, LNCIpedia, NONCODE, LNcRNA Disease, Ernas, NRED, and other databases. Amplification of cRNA, fluorescent labeling, and hybridization of the microarray were performed by following the protocol of Agilent Technologies. Briefly, equal amount of RNA from each subgroup was reversely transcribed into cDNA, which was then labeled with Cy3 (GE Healthcare; Biosciences, Piscataway, NJ, USA) and hybridized with the microarray slide. The slide was then scanned with the Agilent Microarray Scanner G5761A (Agilent Technologies). Data were extracted with Feature Extraction software 12.0.3.1 (Agilent Technologies), and raw data were normalized by Quantile algorithm. Genes with a p value less than 0.05 and a fold change of at least 2 were selected for further analysis. GO/KEGG pathway enrichment analyses of the target genes were performed by Fisher's exact test. The function of lncRNAs was predicted by analyzing the functional annotations of mRNAs that were highly co-expressed with lncRNAs. All raw data of our study was submitted on GEO Profiles, and GEO accession numbers is GSE186493.

Small RNA Library Construction, Sequencing, and Data Processing
Approximately 1 ug total RNA was used to prepare small RNA library according to the protocol of TruSeq Small RNA Sample Prep Kit (Illumina, San Diego, USA). Singleend sequencing (36 bp) was performed with an Illumina Hiseq2500 at LC-BIO (Hangzhou, China). Briefly, the raw reads were subjected to the Illumina pipeline filter (Solexa 0.3), and the dataset was further processed with an in-house program, ACGT101-miR (LC Sciences, Houston, Texas, USA), to remove adapter dimers, junk, low complexity, common RNA families (rRNA, tRNA, snRNA, snoRNA) and repeats. Subsequently, unique sequences with a length of 18-26 nucleotides were mapped to Homo species precursors in miRBase20.0 by BLAST search to identify known miRNAs and novel 3p-and 5p-derived miRNAs. The hairpin RNA structures containing such sequences were predicated from the flank 80 snt sequences using the RNA fold software (http:// rna. tbi. univie. ac. at/ cgi-bin/ RNAfo ld. cgi). miRNA differential expression based on normalized deepsequencing counts was analyzed using the Fisher exact test and Student t test, and the significance threshold was set to be 0.01 or 0.05. To predict the genes targeted by most abundant miRNAs, two computational target prediction algorithms (TargetScan50 and miRanda 3.3a) were used to identify miRNA binding sites. Finally, the data predicted by both algorithms were combined and the overlaps were calculated. The GO terms and KEGG pathways of these most abundant miRNAs and miRNA targets were also annotated.

Quantitative Real-Time PCR
All primers were designed and synthesized by Takara (Takara Biotechnology, Dalian, China). To avoid falsepositive amplification of contaminated genomic DNA in the mRNA samples, all the primers spanning different exons were designed (Table 2). For mRNA, cDNA was synthesized using a PrimeScript RT reagent kit (Takara Biotechnology Co, Ltd, Dalian, China). Quantitative realtime PCR (qRT-PCR) was performed using the IQ5 realtime PCR detection system, and GADPH was taken as a control. Relative quantification of mRNA expression levels was performed using SYBR Premix Ex Taq II on an FTC-3000TM System (Funglyn Biotech Inc., Toronto, Canada). For miRNA, cDNA was synthesized using the service bio RT First strand cDNA Synthesis Kit (Servicebio, Wuhan, China), qRT-PCR was carried out using the SYBR Premix Ex Taq™ II (TaKaRa), and U6 was taken as a control. PCR conditions consisted of a 5 min pre-incubation at 95 °C, followed by 40 cycles of incubation at 95 °C for 10 s plus  TTC TTA GTG  OSTC-R  TGC CCA TGT TCA TCA GTC  SLC17A6-F  GGG AGA CAA TCG AGC TGA CG  SLC17A6-R  TGC AGC GGA TAC CGA AGG A  EDNRA-F  TCG GGT TCT ATT TCT GTA TGCCC  EDNRA-R  TGT TTT TGC CAC TTC TCG TGC TTG GGT CTT TGC GGG C 60 °C for 20 s. At least 15 ASD serum samples and 15 TD samples were randomly selected to examine the differential expression of the candidate RNAs in individual samples. All samples were run in triplicate and the average values were calculated. The relative levels of mRNAs, including EDNRA, SLC17A6, HTR3A, OSTC, and TMEM165, as well as of miRNAs, including PC-5p-139289_26 and hsa-miR-193a-5p, were calculated using the 2 −ΔΔCt method.

Statistics
All statistical analyses were performed using SPSS (version 17). Group statistics are presented as mean ± SD. The t test for independent variables was used to examine the inter-group differences and a significance level of 0.05 was adopted.

Community Involvement
Members of the autism community were not involved in the development of research questions, outcome measures, study design, or implementation of this trial.

Characterization of Serum LCEVs
Basic characteristics of the participants were shown in Table 1. Sera LCEVs in the ASD group (including 5 subgroups) and the TD group (including 3 subgroups) were isolated using L1CAM antibody mediated immunoadsorption (Fig. 1). Nanoparticle tracking analysis showed a higher LCEVs concentration in the ASD group (2.04 ± 4.35 × 10 10 / Fig. 1 Schematic flow diagram of the integrated strategy used herein mL) than in the TD group (1.20 ± 3.28 × 10 10 /mL). The average particle size of LCEVs was 61.50 ± 20.71 nm in the ASD group and 62.07 ± 20.75 nm in the TD group, showing no significant difference ( Fig. 2A). Under TEM, both groups of LCEVs presented a "saucer"-like structure (Fig. 2B). Meanwhile, LCEVs were identified by WB for EVs markers (CD63, CD81, and TSG101) (Fig. 2C) as well as L1CAM (Fig. 2D). In addition, Cytoflex Flow Cytometer analysis revealed that LCEVs marked with CD63 and CD81 accounted for 57.78% and 76.63% of the total LCEVs in TD, and 55.82% and 69.29% of the total LCEVs in ASD (Supplementary Fig. 1).

Differential Expression and Bioinformatic Analysis of mRNAs in ASD Serum LCEVs
Based on lncRNA microarray detection and original data normalization, air-wise Pearson's correlation coefficients of all RNAs among subgroups were shown in Fig. 3A. The coefficients between the biological replicates (subgroups) within each group were obviously higher than coefficients between two groups (Fig. 3A). mRNAs and lncRNAs with at least twofold differential expression and a p value of less than 0.05 were subjected to further examination. This resulted in 167 upregulated and 1251 downregulated mRNAs in ASD sera LCEVs ( Fig. 3B and Supplementary  Table 1). Hierarchical clustering analysis (HCA) of these 1418 differentially expressed mRNAs (DEmRs) showed similar expression profiles among the biological replicates within each group but differential profiles between the two groups (Fig. 3C). Principal component analysis (PCA) of the DEmRs showed that the two groups were separated and the biological replicates in the TD group clustered more closely than the replicates in the ASD group (Fig. 3D). To characterize the distribution of genes for DEmRs on chromosomes and to reveal the susceptible chromosomes, the genes on each chromosome were counted and the ratio of the number of such genes to the total number of genes present  on the chromosome (data from Human Genome Resources at NCBI, GRCh37) was calculated. As a result, chromosomes 1 and 2 had the largest number of genes for DEmRs, while chromosomes 21 and Y had the least number of such genes. However, genes with the maximum ratio were on chromosomes 17 and 5, and those with the minimum ratio were on chromosomes 13 and Y (Fig. 3E). GO annotation of DEmRs showed that their products were mainly distributed in cytoplasm, nucleus, and plasma membrane; were able to bind with proteins, metal ions, and DNA; and were involved mainly in DNA-dependent transcription, small molecule metabolism, transcriptional regulation, and other biological processes (Fig. 3F). KEGG analysis revealed that DEmRs participated in mainly three types of processes: (1) the signal transduction processes, such as MAPK signaling pathway, calcium signaling pathway, PI3K-Akt signaling pathway, and cAMP signaling pathway; (2) neuron-related pathways such as neuroactive reception-ligand interaction, axon guidance, and synaptic vesicle circulation; and (3) glycosylation related pathways such as N-glycan biosynthesis, endoplasmic reticulum protein processing, sugar binding, and glycosaminoglycan degradation (Fig. 3G).

Differential Expression and Functional Prediction of lncRNAs in ASD Serum LCEVs
According to the results of lncRNA microarray, 239 lncRNAs were significantly upregulated and 1506 lncRNAs were significantly downregulated in ASD serum LCEVs ( Fig. 4A and Supplementary Table 2). HCA of these 1745 differentially expressed lncRNAs (DElnRs) showed similar expression profiles among the biological replicates within each group but differential profiles between the two groups (Fig. 4B). PCA of the DElnRs separated the subgroups into TD and ASD groups as their natural grouping (Fig. 4C). To characterize the distribution of DElnRs on chromosomes and to reveal the susceptible chromosomes, the DElnRs on each chromosome were counted and the ratio of the number of DElnRs to the length of that chromosome was calculated (data from Human Genome Resources at NCBI, GRCh37; the unit of length is Mbp). It was found that chromosomes 1 and 2 had the largest number of DElnRs and chromosomes 21 and Y had the least number of DElnRs, whereas chromosomes 19 and 17 had the maximum ratio and chromosomes X and Y had the minimum ratio (Fig. 4D). Genes with a distance of less than 100 kb from lncRNA were regarded as the target genes for cis-acting. As a result, 382 DElnRs were predicted to be positively or negatively correlated with their target genes (R > 0.8) (Supplementary Table 3). Of these genes, 107 were also DEmRs ( Fig. 4E and Supplementary Table 3). Doubleomic analysis (https:// www. omics tudio. cn/ tool) of the 107 pairs of DElnR-DEmR revealed that 81.3% pairs were positively correlated and commonly downregulated in ASD (Fig. 4F). GO annotation showed that these 107 target genes were mainly cytoskeleton-related proteins (Fig. 4G) performing functions such as auxiliary transport protein activity, protein binding, and translation regulation (Fig. 4H), and were involved in protein metabolism, transport, and cell growth and/or maintenance (Fig. 4I).

Glycan-Related Network and Candidate Markers in ASD
Most eukaryotic proteins are modified by covalent addition of glycan molecules, which modulates the structures and functions of the proteins. Glycans are synthesized in the presence of glycosyltransferases, sulfonyl transferases, glycosidases, and other glycan-binding proteins; aberrant expression of these enzymes may result in many complicated pathological conditions, such as inflammation, diabetes, cancer, and neurological abnormality (Kronimus et al. 2019;Pinho and Reis 2015). According to lncRNA microarray and bioinformatic analysis, 54 DEmRs were carbohydrate-related genes, of which 5 (e.g., HPSE, and GALM) were significantly upregulated and others (e.g., OSTC and MAN1B1) were significantly downregulated in ASD (Fig. 7A). DElnRs including LOC101927919, lnc-KIAA1919-1, lnc-SHCBP1L-1, and RP11-177J6.1 were positively correlated with their predicted target genes NUS1, KIAA1919, NPL, and TMEM165, respectively (Fig. 7B). Based on GO and KEGG analysis, these DEmRs and DEl-nRs were mainly involved in carbohydrate metabolic process (e.g., HPSE and SLC37A4), protein N-linked glycosylation (e.g., MGAT5 and OSTC), carbohydrate binding (e.g., MSI-GLEC5 and CLEC1A), glycolysis (e.g., GALM and PGM1), glycosaminoglycan metabolic process (e.g., CHST12 and HPSE), and glycolipid metabolic process (e.g., NEU1 and ST8SIA6) (Fig. 7C). Expressions of OSTC, MAN1B1, and MGAT5 were downregulated in N-glycan biosynthesis Fig. 6 Neuron-related network and significant markers in ASD. A The log 2 foldchanges and p values of 104 DEmRs that were related to neuron in ASD. B The positive relationship of lncRNAs SNHG1, lnc-C20orf201-1, and lnc-TRPV5-1 with their target genes CHRM1, OPRL1, and EPHB6, respectively. The r value and p value were calculated. The p value was lower than 0.05. C The relationship of miRNA PC-5p-139289_26 with its target genes EDNRA, PPP2CB, GCLM, and PTGER3, respectively. The r value and p value were calculated. The p value was lower than 0.05. D Neuron-related network associated with ASD according to GO and KEGG analyses. The red square represented neuron-related functions; the yellow circle represented DEmRs; and the blue diamond represented DElnRs. E The most enriched pathway, neuroactive ligand-receptor interaction, which contained 5 upregulated mRNAs and 19 downregulated mRNAs in ASD. Red arrow represented upregulated and blue arrow represented downregulated in ASD. F Individual validation of the differential expression of EDNRA, SLC17A6, and HTR3Aby qRT-PCR. The sample size (n) used was shown in the figure ◂ implicated in ASD (Fig. 7D). To validate the differential expression of these RNAs, qRT-PCR was utilized to examine TMEM165 in 32 TD and 41 ASD individual serum samples and OSTC in 19 TD and 27 ASD samples. All data were truly retained without artificial removal of the extreme and singular values. The results showed that the levels of OSTC and TMEM165were significantly lower in the ASD children (p = 0.001 and p = 0.02) (Fig. 7E).

Discussion
At present, the diagnosis of ASD is still based on symptom evaluation, as the underlying pathological mechanism remains unclear. There are no blood-based diagnostic tools or approved drugs for ASD. Research to identify reliable biological markers of disease status and symptomology is therefore urgently needed. Neurobiological systems critical to social functioning are arguably the most promising biological sources for ASD biomarkers and therapeutic targets. However, existing methods for brain detection mostly relied on autopsy or animal models, which are limited because of poor timeliness and species differences. Most cells in the nervous system, including neurons, astrocytes, oligodendrocytes, and microglia, secrete EVs under normal or pathological conditions. EVs can reflect the host cell proteins and nucleic acids at the time of secretion and can diffuse across the blood brain barrier into the periphery. Serum/plasma LCEVs can be captured by antibodies directed against the cell surface protein L1CAM embedded in the vesicle membrane (Goetzl et al. 2018(Goetzl et al. , 2020Nogueras-Ortiz et al. 2020). Although investigation of LCEVs is relatively novel, attractive evidence from other fields suggests that such investigation can afford insight into the pathological mechanisms and processes associated with Alzheimer's disease and depressive disorder (Kuwano et al. 2018;Song et al. 2020). A recent study developed a panel of single-molecule array assays to evaluate the use of L1CAM for neuron-derived EV (LCEVs) isolation, and demonstrated that L1CAM behaved as a soluble protein, not as an EV-associated protein, and therefore recommend against its use as a marker in LCEV isolation protocols (Norman et al. 2021). It is mentionable that they fractionated plasma and cerebrospinal fluid using size exclusion chromatography (SEC) and density gradient centrifugation (DGC), and found that L1CAM expression overlapped at the tails of earlier fractions with the later fractions. Actually, in this study, total EVs were extracted from serum preferentially and then were used to isolate LCEVs by immunoprecipitation to avoid soluble protein interference as much as possible. The average particle size of LCEVs was 61.50 ± 20.71 nm in the ASD group and 62.07 ± 20.75 nm in the TD group, which was consistent with a latest study that reported that LCEVs were smaller than other EVs isolated from plasma (p < 0.0001) (Saeedi et al. 2021). Overall, LCEVs can be enriched by L1CAM antibody in peripheral blood but the size was smaller than most EVs without L1CAM.
Thus far, a putative speech and language region at chromosome 7q31-q33 seems most strongly linked to autism. Cytogenetic abnormalities at the 15q11-q13 locus are fairly frequent in people with autism, and a "chromosome 15 phenotype" is described in individuals with chromosome 15 duplications (Nakatani et al. 2009). Some candidate genes are considered located at chromosomes 7q22-q33 and 15q11-q13 (Muhle et al. 2004), and 21 genes in chromosomal 8p region are identified as most likely to contribute to neuropsychiatric disorders and neurodegenerative disorders (Tabares-Seisdedos and Rubenstein 2009). Variant alleles of the serotonin transporter gene (5-HTT) on chromosome 17q11-q12 are more frequent in individuals with autism than in healthy people (Nakatani et al. 2009). In addition, many mutations on NLGN4X, an X-linked cell adhesion molecule, result in ASD ). In the present study, chromosome 17 was the commonly and mostly enriched chromosome for both DEmRs and DElnRs in ASD. A large portion of the DEmRs on chromosome 17 participates in cell communication and signal transduction, which are essential for synapse formation and neurotransmitter release. Abnormal expression of such mRNAs implies the abnormality of these functions in ASD.
Brain-derived EVs carry and release multiple molecules related to neuronal function and neurotransmission in the brain, which is beneficial for the reciprocal communication between neural cells (e.g., neuron − glia interactions), synaptic plasticity, neuronal development, and neuroimmune communication. In the present study, 104 DEmRs were annotated to be related to neuroactive ligand-receptor interaction, pathways of neurodegeneration, glutamatergic synapse, axon guidance, synaptic vesicle cycle, dendrite, neuron projection development, neuron migration, and apoptotic process. Most (81.7%) of these neuron-related mRNAs were downregulated in ASD. As demonstrated in the pathway of Fig. 7 Glycan-related network and candidate markers in ASD. A The log 2 foldchanges and p values of 54 DEmRs that were related to glycan metabolism in ASD. B The positive relationship of lncR-NAs LOC101927919, lnc-KIAA1919-1, lnc-SHCBP1L-1, and RP11-177J6.1 with their target genes NUS1, KIAAet al., NPL, and TMEM165, respectively. The r value and p value were calculated. The p value was lower than 0.05. C Glycan-related network associated with ASD according to GO and KEGG analyses. The green square represented carbohydrate associated functions; the orange circle represented DEmRs; and the blue diamond represented DElnRs. D Expressions of OSTC, MAN1B1, and MGAT5 were downregulated in N-glycan biosynthesis implicated in ASD. Blue arrow represented downregulated in ASD. E Individual validation of the differential expression of OSTC and TMEM165 by qRT-PCR. The sample size (n) used was shown in the figure ◂ neuroactive ligand-receptor interaction (Fig. 3H), 5 receptors (e.g., EDNRA) were upregulated and 19 (e.g., HTR3A) were downregulated in ASD. A previous study reported that neuropeptide receptor gene expression was lower in children with autism and the lower neuropeptide receptor gene expression predicted greater social impairments and greater stereotyped behaviors (Oztan et al. 2018). We found that 5-hydroxytryptamine receptor 3A (HTR3A) significantly decreased in the ASD serum LCEVs in this study. HTR3A is one of the receptors for 5-hydroxytryptamine (serotonin), a biogenic hormone that also functions as a neurotransmitter and a mitogen. Ample evidence suggests that levels of serotonin and serotonin transporter (SERT) increase significantly in autistic children than in gender and age-matched nonautistic children (Abdulamir et al. 2018;Meyyazhagan et al. 2020). It thus can be hypothesized that increase of serotonin and SERT may be a kind of cell self-help that compensates for the loss of receptors, but it needs to be experimentally confirmed in the future. Another specific signature is the decreased expression of vesicular glutamate transporter 2 (SLC17A6) in the ASD serum LCEVs. Receptors for glutamate (Glu), GRIK5, GRIK2, and GRIA4 were also downregulated. Glu acts as an excitatory neurotransmitter at many synapses in the central nervous system. SLC17A6 mediates the uptake of Glu into synaptic vesicles at presynaptic nerve terminals of excitatory neural cells. The postsynaptic actions of Glu are mediated by a variety of receptors expressed on postsynaptic cell membrane. Emerging evidence suggests that imbalance between excitatory (Glu-mediated) and inhibitory (GABA-mediated) neurotransmission may be a common pathophysiological mechanism in ASD (Horder et al. 2018;Rojas 2014). These studies, together with the findings in the present study, suggest that reduction in the expression of Glu transporter and receptors might be the main reason for the abnormalities of Glu-mediated neurotransmission and hence a therapeutic target in ASD.
Glycans and their conjugates (glycoproteins, proteoglycans, and glycolipids) are major constituents of the neural cell membrane and extracellular matrix (ECM). Glycans and glycoconjugates participate in nearly every biological process in the developing brain. A potential link between ASD and changes in glycosylation was first observed in patients with congenital glycosylation disorders (CDGs) (Freeze et al. 2015). Recent advances in genome sequencing have identified many genetic variants that occur in genes encoding glycosylated proteins (proteoglycans or glycoproteins) or enzymes involved in glycosylation (glycosyltransferases and sulfotransferases) (Dwyer and Esko 2016;Yu et al. 2013). However, it remains unknown whether "glycogene" variants cause changes in glycosylation and whether they contribute to the etiology and pathogenesis of ASDs. In the present study, we analyzed the whole transcriptome of serum LCEVs in ASD to screen potential biomarkers and explore the important molecular events in brain neurons of ASD children. Our results showed that a total of 54 DEmRs (3.8%) were glycogenes, and most of them (90.7%) were downregulated in ASD. The 54 DEmRs mainly participated in carbohydrate metabolic process, protein N-linked glycosylation, carbohydrate binding, glycolysis, glycosaminoglycan metabolic process, and glycolipid metabolic process. Thereinto, OSTC, MAN1B1, and MGAT5, translating to key enzymes for N-linked glycosylation, were significantly downregulated in ASD. In our previous study, we found a significant decrease of STL binding glycans or glycoproteins that contain trimers and tetramers of GlcNAc (core structure of N-glycans) in ASD versus in TD (fold change = 0.54, p = 0.0057) (Qin et al. 2017). In all, no matter at the gene level, the transcription level, or the level of translation and post-translation modification, abnormalities of glycosylation and carbohydrate metabolism might be an important molecular mechanism of ASD. Moreover, the decrease of receptors and transporters of neurotransmitters may be related with the decrease of glycogenes as most of the receptors and transporters are highly glycosylated. OSTC is a subunit of the oligosaccharyl transferase (OST) complex that catalyzes the initial transfer of a defined glycan (Glc3Man9GlcNAc2 in eukaryotes) from the lipid carrier dolichol-pyrophosphate to an asparagine residue within an Asn-X-Ser/Thr consensus motif in nascent polypeptide chains. In the present study, expression of OSTC significantly decreased in ASD serum LCEVs, suggesting it as a candidate biomarker for ASD diagnosis.
Recent studies have shown that abnormal expression of miRNAs could be involved in the underlying pathogenesis of ASD. miRNAs are small noncoding mRNAs that regulate gene expression and are often linked to biological processes and implicated in neurodevelopment. A dozen of miRNAs, such as miRNA-125b and miRNA-132, have been observed to regulate the expression of ASD risk genes, act differently on the morphology of the spine and synaptic plasticity in brain neurons, and participate in ASD etiopathogenesis (Schepici et al. 2019). However, compared with mRNA and lncRNA, fewer miRNAs were found differentially expressed in ASD serum LCEVs in the present study. Among 11 DEmiRs, PC-5p-139289_26 was significantly upregulated and hsa-miR-193a-5p was significantly downregulated in ASD, and both of them had the largest number of predicted targets that were differentially expressed in ASD, indicating that these two miRNAs might play important roles in ASD. These targets were mostly involved in glutathione synthesis and recycling and mannosyltransferase activity, which are closely correlated with synthesis of Glu and glycans involved in the neuron-and glycan-related networks in ASD. However, the relationships between miRNAs and their target genes have not yet been verified.