EGR1 Regulates SHANK3 Transcription at Different Stages of Brain Development.

The expression levels of SHANK3 are associated with autism spectrum disorder (ASD). The dynamic changes in SHANK3 expression during different stages of brain development may impact the progression of ASD. However, no studies or detailed analyses exploring the upstream mechanisms that regulate SHANK3 expression have been reported. In this study, we employed immunofluorescence to examine the expression of SHANK3 in brain organoids at various stages. Our results revealed elevated levels of SHANK3 expression in brain-like organoids at Day 60. Additionally, we utilized bioinformatics software to predict and analyze the SHANK3 gene's transcription start site. Through the dual luciferase reporter gene technique, we identified core transcription elements within the SHANK3 promoter. Site-directed mutations were used to identify specific transcription sites of SHANK3. To determine the physical binding of potential transcription factors to the SHANK3 promoter, we employed electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP). Our findings demonstrated that the transcription factor EGR1 regulates SHANK3 expression by binding to the transcription site of the SHANK3 promoter. Although this study did not investigate the pathological phenotypes of human brain organoids or animal model brains with EGR1 deficiency, which could potentially substantiate the findings observed for SHANK3 mutants, our findings provide valuable insights into the relationship between the transcription factor, EGR1, and SHANK3. This study contributes to the molecular understanding of ASD and offers potential foundations for precise targeted therapy.

Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder mainly showed atypical social interaction, communication, and restricted, repetitive patterns of behavior, interests and activities. Now clinic diagnosis of ASD is mostly based on psychological evaluation, clinical observation and medical history. All these behavioral indexes could not avoid defects such as subjectivity and reporter-dependency. Therefore researchers devoted themselves to seek relatively stable biomarkers of ASD as supplementary diagnostic evidence. The goal of present study is to generate relatively stable predictive model based on anatomical brain features by using machine learning technique. Forty-six ASD children and thirty-nine development delay children aged from 18 to 37 months were evolved in. As a result, the predictive model generated by regional average cortical thickness of regions with top 20 highest importance of random forest classifier showed best diagnostic performance. And random forest was proved to be the optimal approach for neuroimaging data mining in small size set and thickness-based classification outperformed volume-based classification and surface area-based classification in ASD. The brain regions selected by the models might attract attention and the idea of considering biomarkers as a supplementary evidence of ASD diagnosis worth exploring. Autism Res 2017, 0: 000-000. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 620-630. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Two years changes in the development of caudate nucleus are involved in restricted repetitive behaviors in 2-5-year-old children with autism spectrum disorder.

Caudate nucleus volume is enlarged in autism spectrum disorder (ASD) and is associated with restricted and repetitive behaviors (RRBs). However, the trajectory of caudate nucleus volume in RRBs of young children remains unclear. Caudate nucleus volume was measured in 36 children with ASD and 18 matched 2-3-year-old subjects with developmentally delayed (DD) at baseline (Time 1) and at 2-year follow-up (Time 2). The differential growth rate in caudate nucleus volume was calculated. Further, the relationships between the development of caudate nucleus volume and RRBs were analyzed. Our results showed that caudate nucleus volume was significantly larger in the ASD group at both time points and the magnitude of enlargement was greater at Time 2. The rate of caudate nucleus growth during this 2-year interval was faster in children with ASD than DD. Right caudate nucleus volume growth was negatively correlated with RRBs. Findings from this study suggest developmental abnormalities of caudate nucleus volume in ASD. Longitudinal MRI studies are needed to explore the correlation between atypical growth patterns of caudate nucleus and phenotype of RRBs.