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

转录组 聚糖 生物 微阵列 细胞外小泡 自闭症谱系障碍 糖基化 基因 自闭症 生物信息学 分子生物学 遗传学 基因表达 细胞生物学 医学 糖蛋白 精神科
作者
Yannan Qin,Li Cao,Jinyuan Zhang,Haiqing Zhang,Shuang Cai,Bo Guo,Fei Wu,Lingyu Zhao,Wen Li,Lei Ni,Liying Liu,Xiaofei Wang,Yanni Chen,Chen Huang
出处
期刊:Journal of Molecular Neuroscience [Springer Nature]
卷期号:72 (6): 1274-1292 被引量:12
标识
DOI:10.1007/s12031-022-01994-z
摘要

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.
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