| Autism Spectrum Disorder(ASD)is a serious neurodevelopmental disorder,affected by both genetics and environment,characterized by impaired social interaction and communication,as well as the presence of repetitive and restrictive behaviors.In recent years,the prevalence of ASD has increased year by year.However,there is no effective method for early diagnosis and treatment of ASD.Gut microbiota,as a consistent and special environmental factor,participates in various physiological processes of human body,and its influence involves digestive system,immune system,nervous system,behavior and cognitive function.Among them,the two-way control system of gut microbiota and brain is called microbiota-gut-brain axis.The discovery of this system provides a new possibility for the diagnosis and treatment of ASD.At the same time,the progress of sequencing technology and the subsequent large-scale sequencing microbiome project promoted the construction of databases and network servers related to microorganisms,and also provided a basis and basis for building computational models to predict the relationship between microorganisms and diseases.This makes the use of gut microbiota and machine learning technology to diagnose ASD show great prospects.Recently,more and more studies have shown that gut microbiota plays a crucial role in the pathogenesis of ASD,and changing the composition of gut microbiota has great potential in the treatment of ASD.Therefore,since the occurrence of mental diseases is affected by many factors in the process of individual development,this topic combines the results of clinical data analysis with animal experiments to further explore the differences of gut microbiota in ASD patients,the relationship between gut microbiota and ASD related behavioral symptoms and the internal mechanism,and provide a new targeted intervention and treatment method to improve the correlation behavior of ASD children.Study 1: This study screened and summarized the high-throughput sequencing data of ASD gut microbiota in National Center for Biotechnology Information-Sequence Read Archive(NCBI-SRA)database,analyzed the diversity and difference of their gut microbiota by comparing it with the gut microbiota data of typical developing children,and established the early recognition model of ASD using machine learning method.Study 2: To further investigate the relationship between altered gut microbiota and ASDrelated behavioral symptoms,this study used a rodent model widely used to study ASD,Maternal Immune Activation(MIA),based on the results of data analysis in Study 1.offspring mice.A mixed experimental design of 2(pre-test,post-test)x 2(MIA offspring mice,healthy control mice)x 3(antibiotics + prebiotics,prebiotics,drinking water)was used to analyse and compare changes in gut microbiota,behavioral symptoms and brain gene expression in mice.The main conclusions of this study are as follows:(1)The gut microbiota of ASD patients is different from that of healthy individuals,and shows low diversity and richness in the overall environment,with Bacteroides,Bifidobacterium,Escherichia-Shigella,Roseburia,Faecalibacterium and Paraprevotella as potential key bacteria.(2)The machine learning model was able to exclude the interference of external factors such as geography,diet and medication,and achieved ASD identification through gut microbiota data only,with an identification rate of 91.9%.(3)The probiotic intervention and the antibiotic+probiotic intervention were able to change the overall environment of the gut microbiota and the relative abundance of potential key bacteria for ASD in mice,and showed positive effects in improving the behavioral defects of MIA offspring,among which the antibiotic+probiotic intervention method showed better results.(4)The prebiotic intervention and the antibiotic+prebiotic intervention were able to suppress the expression of the immune inflammation-related gene IFNG,and the improvement of behavioral deficits in MIA offspring mice might be achieved through the immune pathway. |
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