Non-coding de novo mutations in chromatin interactions are implicated in autism spectrum disorder

Kim, Il Bin; Lee, Taeyeop; Lee, Junehawk; Kim, Jonghun; Lee, Suho; Koh, In Gyeong; Kim, Jae Hyun; An, Joon-Yong; Kim, Woo Kyeong; Ju, Young Seok; Cho, Yongseong; Yu, Seok Jong; Kim, Soon Ae; Oh, Miae; Han, Dong Wook; Kim, Eunjoon; Choi, Jung Kyoon; Yoo, Hee Jeong; Lee, Jeong Ho

doi:10.1038/s41380-022-01697-2

Cited by 15 publications

(13 citation statements)

References 111 publications

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“…As the list of hcASD risk genes has expanded, strategies for determining the convergence of gene expression patterns or biological functions across various ASD risk genes have repeatedly highlighted neurogenesis in the human mid-gestational prefrontal cortex as an important nexus of pathobiology 7,[11][12][13][14][15][16][17] . At the same time, gene ontology enrichment analyses have implicated broad functional categories of these genes, such as chromatin modification, transcriptional regulation,, cell signaling, and synaptic function 6,[18][19][20][21] . However, gene ontology-based approaches may be incomplete as they are limited by a priori knowledge.…”

Section: Introductionmentioning

confidence: 99%

A foundational atlas of autism protein interactions reveals molecular convergence

Wang,

Vartak,

Zaltsman

et al. 2023

Preprint

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SummaryTranslating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation inXenopus tropicalisand human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

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Section: Introductionmentioning

confidence: 99%

A foundational atlas of autism protein interactions reveals molecular convergence

Wang,

Vartak,

Zaltsman

et al. 2023

Preprint

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“…CNV deletions and duplications of the TBX1 gene may be associated with ASD ( 29 ). 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 ).…”

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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“…Sequencing is different from closed‐system microarrays. It is an open detection platform that can discover new sequences and new mutations and provide transcript information 17 …”

Section: Discussionmentioning

confidence: 99%

“…Sequencing is different from closed-system microarrays. It is an open detection platform that can discover new sequences and new mutations and provide transcript information 17. Although some studies have sequenced hair follicles under different culture conditions, most data have been obtained from laboratory cell or tissue culture, which is not closely related to clinical practice, and these results are difficult to translate into clinical application.…”

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confidence: 99%

Application of lncRNA–miRNA–mRNA ceRNA network analysis in the treatment of androgenic alopecia

Wei

et al. 2022

Clinical Laboratory Analysis

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Background Long noncoding RNAs (lncRNAs) can be used as competitive endogenous RNAs (ceRNAs) to bind to microRNAs (miRNAs) to regulate gene expression. Previous studies have demonstrated that ceRNAs play an important role in the development of tumors. However, it is not clear whether the lncRNA–miRNA–mRNA ceRNA network plays a role in androgenic alopecia (AGA). Methods The hair follicles of three AGA patients and three healthy individuals were collected for high‐throughput whole transcriptome sequencing to screen for differentially expressed lncRNAs. Differentially expressed lncRNA target genes were subjected to databases to predict miRNA–mRNA and lncRNA–miRNA relationship pairs, and a ceRNA network was constructed using Cytoscape software. Relative expression was verified by real‐time quantitative reverse transcription–polymerase chain reaction (qRT‐PCR). Results 84 lncRNAs were significantly differentially expressed between the hair follicles of AGA patients and those of healthy individuals; 30 were upregulated, and 54 were downregulated. The top 10 upregulated lncRNAs were ENST00000501520, ENST00000448179, ENST00000318291, ENST00000568280, ENST00000561121, ENST00000376609, ENST00000602414, ENST00000573866, ENST00000513358, and ENST00000564194. The top 10 downregulated lncRNAs were ENST00000566804, ENST00000561973, ENST00000587680, ENST00000569927, ENST00000340444, ENST00000424345, ENST00000589787, NR_024344, NR_073026, and NR_110001. The qRT‐PCR validation results and receiver‐operating characteristic curve analysis indicated that one upregulated lncRNA, LOXL1‐AS1 (ENST00000564194), had the most significant clinical diagnostic potential. After further analysis, it was concluded that LOXL1‐AS1 could be used as a sponge to target hsa‐miR‐5193, thereby regulating TP53 expression. Conclusion The ceRNA network‐regulating AGA was constructed through high‐throughput sequencing. Our study also identified a key lncRNA that is possibly related to the AGA pathological process.

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Non-coding de novo mutations in chromatin interactions are implicated in autism spectrum disorder

Cited by 15 publications

References 111 publications

A foundational atlas of autism protein interactions reveals molecular convergence

A foundational atlas of autism protein interactions reveals molecular convergence

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

Application of lncRNA–miRNA–mRNA ceRNA network analysis in the treatment of androgenic alopecia

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