MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

Wong, Anthony T.C.; Zhou, Anbo; Cao, Xiaolong; Mahaganapathy, Vaidhyanathan; Azaro, Marco A.; Gwin, Christine; Wilson, Sherri; Buyske, Steven; Bartlett, Christopher W.; Flax, Judy; Brzustowicz, Linda M.; Xing, Jinchuan

doi:10.3390/genes13081329

Cited by 7 publications

(5 citation statements)

References 59 publications

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“…The GO enrichment and PPI network analyses pointed to several biological processes that are important for ASD etiology, such as neuronal development and histone modification. Several of the terms (e.g., "histone modification", "actin binding") also showed enrichment in the cohort in our previous studies of SNVs [32,46]. These genes are strong candidates for future study of their contribution to ASD.…”

Section: Discussionmentioning

confidence: 65%

“…Lastly, we limited our analysis to interactions among SVs. Several types of data, including coding SNVs, and microRNA variants have been identified in the NJLAGS cohort [32,46]. Integrative analysis of candidate genes from multiple types of variants could further improve the power of the analysis.…”

Section: Discussionmentioning

confidence: 99%

“…ConsensusPathDB [73], STRING [75], and GI-ANT_v2 [76,77] were used to generate a list of known gene interactions. Detailed data processing procedures have been described previously [46,70].…”

Section: Pathogenicity Predictionmentioning

confidence: 99%

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Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments

Alibutud,

Hansali,

Cao

et al. 2023

IJMS

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by restrictive interests and/or repetitive behaviors and deficits in social interaction and communication. ASD is a multifactorial disease with a complex polygenic genetic architecture. Its genetic contributing factors are not yet fully understood, especially large structural variations (SVs). In this study, we aimed to assess the contribution of SVs, including copy number variants (CNVs), insertions, deletions, duplications, and mobile element insertions, to ASD and related language impairments in the New Jersey Language and Autism Genetics Study (NJLAGS) cohort. Within the cohort, ~77% of the families contain SVs that followed expected segregation or de novo patterns and passed our filtering criteria. These SVs affected 344 brain-expressed genes and can potentially contribute to the genetic etiology of the disorders. Gene Ontology and protein–protein interaction network analysis suggested several clusters of genes in different functional categories, such as neuronal development and histone modification machinery. Genes and biological processes identified in this study contribute to the understanding of ASD and related neurodevelopment disorders.

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Section: Discussionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

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Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments

Alibutud,

Hansali,

Cao

et al. 2023

IJMS

Self Cite

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“…Studies have suggested the role of miRNAs in the development of autism. Wong et al [ 62 ] identified variants in five brain-expressed miRNAs, which target 326 genes, and 3′ UTR miRNA target regions of 152 genes potentially involved in neurodevelopmental disorders, including autism. miRNA could post-transcriptionally regulate autism-related risk genes and affect various molecular pathways related to autism and associated disorders.…”

Section: Discussionmentioning

confidence: 99%

Pre-autism: What a paediatrician should know about early diagnosis of autism‎

Al-Beltagi

2023

World J Clin Pediatr

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Autism, also known as an autism spectrum disorder, is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life. A range of symptoms characterizes it and can be diagnosed at any age, including adolescence and adulthood. However, early diagnosis is crucial for effective management, prognosis, and care. Unfortunately, there are no established fetal, prenatal, or newborn screening programs for autism, making early detection difficult. This review aims to shed light on the early detection of autism prenatally, natally, and early in life, during a stage we call as “pre-autism” when typical symptoms are not yet apparent. Some fetal, neonatal, and infant biomarkers may predict an increased risk of autism in the coming baby. By developing a biomarker array, we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient. These biomarkers could be genetic, immunological, hormonal, metabolic, amino acids, acute phase reactants, neonatal brainstem function biophysical activity, behavioral profile, body measurements, or radiological markers. However, every biomarker has its accuracy and limitations. Several factors can make early detection of autism a real challenge. To improve early detection, we need to overcome various challenges, such as raising community awareness of early signs of autism, improving access to diagnostic tools, reducing the stigma attached to the diagnosis of autism, and addressing various culturally sensitive concepts related to the disorder.

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“…Non-coding de novo mutations in chromatin interactions were found to alter the expression of target genes at early neurodevelopmental stages ( 30 ). Another study identified miRNA and miRNA targeting variants associated with ASD and related disorders ( 31 ). Inflammatory mechanisms and vitamin D are also reported to play an important role in the pathophysiology of ASD, and 25-OH-vitamin D levels in ASD patients were significantly negatively correlated with c-reactive protein, neutrophil-lymphocyte ratio, and neutrophil counts.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of genomic and immune infiltration patterns in autism spectrum disorder

Wei¹,

Guo²,

Wu³

et al. 2022

Ann Transl Med

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Background: Autism spectrum disorder (ASD) is a specific type of pervasive developmental disorder, and most studies suggest that the onset of autism may be related to genetic and immune factors. The etiology of autism and the underlying molecular events need to be further addressed. Methods:The ASD-related dataset GSE18123 was downloaded from the Gene Expression Omnibus (GEO) database. Gene set enrichment analysis (GSEA) was used to screen for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that may be associated with autism. The top 5,000 genes with an absolute median difference were obtained, and a co-expression network was constructed using weighted correlation network analysis (WGCNA). In addition, GO and KEGG enrichment analyses were performed for genes in the modules most closely related to ASD. Hub genes were found in the significant modules, and the expression values and receiver operating characteristic (ROC) curves of the hub genes were analyzed and validated. Immune cell infiltration in ASD was calculated using the CIBERSORT algorithm, and the relationship between hub genes and immune cells was analyzed. Finally, GSEA was used to explore the potential pathways of hub genes affecting ASD. Results:The 5,000 DEGs were divided into eight significant modules by WGCNA. The green module was most significantly associated with ASD, and two hub genes [fatty acid-binding protein 2 (FABP2) and Janus kinase 2 (JAK2)] were found. Immune cell infiltration showed that resting dendritic cells and monocytes differed significantly in ASD and healthy individuals. FABP2 was significantly associated with memory B cells and CD8 T cells. JAK2 was significantly associated with monocytes, CD4 activated memory T cells, CD4 resting memory T cells, activated dendritic cells, gamma delta T cells, regulatory T cells (Tregs), CD8 T cells, and naïve CD4 T cells. FABP2 and JAK2 were found to affect multiple pathways of immunity.Conclusions: FABP2 and JAK2 may influence the immune microenvironment of ASD by regulating immune cells and immune-related pathways and are candidate molecular markers for the development of ASD.

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MicroRNA and MicroRNA-Target Variants Associated with Autism Spectrum Disorder and Related Disorders

Cited by 7 publications

References 59 publications

Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments

Structural Variations Contribute to the Genetic Etiology of Autism Spectrum Disorder and Language Impairments

Pre-autism: What a paediatrician should know about early diagnosis of autism‎

Bioinformatics analysis of genomic and immune infiltration patterns in autism spectrum disorder

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