Cryptic splicing mediates genetic and therapeutic perturbation of human gene expression levels.
Benjamin Jung FairCarlos Buen Abad NajarJunxing ZhaoStephanie LozanoAustin ReillyJonathan StaleyJingxin WangYang I LiPublished in: bioRxiv : the preprint server for biology (2023)
Nonsense-mediated decay (NMD) is an RNA surveillance mechanism that degrades transcripts containing premature termination codons. Alternative splicing (AS) is known to affect the expression levels of some human genes by producing transcripts rapidly degraded by NMD. However, the importance of AS in determining expression levels of most genes is unclear because rapidly degraded mRNA isoforms are difficult to quantify. To assess the impact of AS on gene expression levels, we analyzed population-scale data across eight molecular assays that capture gene regulation before, during, and after transcription and mRNA decay. Sequencing nascent RNA revealed that ∼15% of all mRNA molecules are NMD targets, of which most result from low-usage cryptic splicing. Leveraging genetic variation across cell lines, we find that trait-associated loci explained by AS are similarly likely to associate with NMD-induced expression level differences as with differences in protein isoform usage, suggesting that much of the impact of AS is mediated by NMD. Finally, we used the splice-switching drug risdiplam to perturb AS at hundreds of genes, finding that ∼3/4 of the splicing perturbations induce NMD. The remarkable prevalence of cryptic splice sites across the genome presents a rich opportunity for natural selection and therapeutics to shape the expression levels of nearly all human genes.
Keyphrases
- genome wide
- gene expression
- poor prognosis
- binding protein
- endothelial cells
- dna methylation
- induced pluripotent stem cells
- bioinformatics analysis
- public health
- genome wide identification
- emergency department
- long non coding rna
- electronic health record
- high throughput
- machine learning
- copy number
- small molecule
- transcription factor
- drug induced
- data analysis