Bioinformatics detection of modulators controlling splicing factor‐dependent intron retention in the human brain

Chen, Steven; Simpson, Edward; Reiter, Jill L.; Liu, Yunlong

doi:10.1002/humu.24379

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…The reporters used in the screening were aimed at only identifying factors that bind to intronic sequences of CIRs, and the complete picture of EIciRNA biogenesis requires further studies. Consistent with the findings of previous studies (Middleton et al 2017;Monteuuis et al 2019;Chen et al 2022b), we also showed that SRSF1 was involved in regulating LIRs, and there was limited overlap between regulated CIR and LIR events for both introns and parental genes (Fig. 6D).…”

Section: Discussionsupporting

confidence: 92%

Systematic identification and characterization of exon–intron circRNAs

Zhong,

Yang,

Wang

et al. 2024

Genome Res.

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Exon–intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5′-end, whereas LIRs are closer to the 3′-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis ofEIciLIMK1, which enhances the expression ofLIMK1incisto boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

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Section: Discussionsupporting

confidence: 92%

Systematic identification and characterization of exon–intron circRNAs

Zhong,

Yang,

Wang

et al. 2024

Genome Res.

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“…RNA sequencing data from the dorsolateral prefrontal cortex (DLPFC) and DNA genotyping data from 991 samples were downloaded from the CommonMind Consortium (CMC) 20 . The RNA-seq data were processed as described previously 25 . Genetic variants that had minor allele frequency (MAF) ≥ 0.03, Hardy-Weinberg equilibrium P > 0.001, and genotyping rate ≥ 0.95, were used as the input for imputation using the Michigan Imputation Server with default parameters.…”

Section: Methodsmentioning

confidence: 99%

RNA alternative splicing impacts the risk for alcohol use disorder

Liu

Reiter³

et al. 2023

Preprint

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Alcohol use disorder (AUD) is a complex genetic disorder characterized by problems arising from excessive alcohol consumption. Identifying functional genetic variations that contribute to risk for AUD is a major goal. Alternative splicing of RNA mediates the flow of genetic information from DNA to gene expression and expands proteome diversity. We asked whether alternative splicing could be a risk factor for AUD. Herein, we used a Mendelian randomization (MR)-based approach to identify skipped exons (the dominant splicing event in brain) that contribute to AUD risk. Genotypes and RNA-seq data from the CommonMind Consortium were used as the training dataset to develop predictive models linking individual genotypes to exon skipping in the prefrontal cortex. We applied these models to data from the Collaborative Studies on Genetics of Alcoholism to examine the association between the imputed cis-regulated splicing outcome and the AUD-related traits. We identified 27 exon skipping events that were predicted to affect AUD risk; six of these were replicated in the Australian Twin-family Study of Alcohol Use Disorder. Their host genes are DRC1, ELOVL7, LINC00665, NSUN4, SRRM2 and TBC1D5. The genes downstream of these splicing events are enriched in neuroimmune pathways. The MR-inferred impacts of the ELOVL7 skipped exon on AUD risk was further supported in four additional large-scale genome-wide association studies. Additionally, this exon contributed to changes of grey matter volumes in multiple brain regions, including the visual cortex known to be involved in AUD. In conclusion, this study provides strong evidence that RNA alternative splicing impacts the susceptibility to AUD and adds new information on AUD-relevant genes and pathways. Our framework is also applicable to other types of splicing events and to other complex genetic disorders.

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Bioinformatics detection of modulators controlling splicing factor‐dependent intron retention in the human brain

et al. 2022

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Alternative RNA splicing is an important means of genetic control and transcriptome diversity. However, when alternative splicing events are studied independently, coordinated splicing modulated by common factors is often not recognized. As a result, the molecular mechanisms of how splicing regulators promote or repress splice site recognition in a context-dependent manner are not well understood. The functional coupling between multiple gene regulatory layers suggests that splicing is modulated by additional genetic or epigenetic components. Here, we developed a bioinformatics approach to identify causal modulators of splicing activity based on the variation of gene expression in large RNA sequencing datasets. We applied this approach in a neurological context with hundreds of dorsolateral prefrontal cortex samples. Our model is strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. We identified novel modulators of the splicing factor SRSF1, including UIMC1 and the long noncoding RNA CBR3-AS1, that function over dozens of SRSF1 intron retention splicing targets. This strategy can be widely used to identify modulators of RNAbinding proteins involved in tissue-specific alternative splicing.

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Bioinformatics detection of modulators controlling splicing factor‐dependent intron retention in the human brain

Cited by 5 publications

References 51 publications

Systematic identification and characterization of exon–intron circRNAs

Systematic identification and characterization of exon–intron circRNAs

RNA alternative splicing impacts the risk for alcohol use disorder

Bioinformatics detection of modulators controlling splicing factor‐dependent intron retention in the human brain

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