Combinatorial genetics reveals a scaling law for the effects of mutations on splicing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA436581 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6796100.fastq.gz | Fastqsanger.gz | |||
| SRR6796110.fastq.gz | Fastqsanger.gz | |||
| SRR6796101.fastq.gz | Fastqsanger.gz | |||
| SRR6796111.fastq.gz | Fastqsanger.gz | |||
| SRR6796102.fastq.gz | Fastqsanger.gz |
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Project description:Despite a wealth of molecular knowledge, quantitative laws for accurate prediction of biological phenomena remain rare. Alternative pre-mRNA splicing is an important regulated step in gene expression frequently perturbed in human disease. To understand the combined effects of mutations during evolution, we quantified the effects of all possible combinations of exonic mutations accumulated during the emergence of an alternatively spliced human exon. This revealed that mutation effects scale non-monotonically with the inclusion level of an exon, with each mutation having maximum effect at a predictable intermediate inclusion level. This scaling is observed genome-wide for cis and trans perturbations of splicing, including for natural and disease-associated variants. Mathematical modelling suggests that competition between alternative splice sites is sufficient to cause this non-linearity in the genotype-phenotype map. Combining the global scaling law with specific pairwise interactions between neighbouring mutations allows accurate prediction of the effects of complex genotype changes involving >10 mutations.
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Project description:Combinatorial genetics reveals a scaling law for the effects of mutations on splicing
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Project description:Regulation of mRNA splicing is a critical and tightly regulated cellular function, underlying the majority of proteomic diversity in our genomes. While disruption of this process is common in disease, the basic genetic complexity of alternative splicing in vivo remains poorly understood. To delineate the splicing landscape in disease, we used an integrative genomics approach and combined both genome and exon level transcriptome data in two somatic tissues (cerebella and peripheral ganglia) from a transgenic mouse model of neuroblastoma, a tumor that arises from peripheral ganglia. These data identify splicing quantitative trait loci (sQTL) that modulate differential splicing across the genome. Among these, an sQTL at FUBP1 revealed a splicing event that modulated levels of the MYC oncoprotein in human neuroblastoma-derived cell lines and correlated with outcome in neuroblastoma. Through this unbiased sQTL analysis, we also define de novo splicing motifs that serve as sites for recurrent mutations in cancer and lead to functional changes in exon expression, enhancing our understanding of the cancer genome. Exon expression from Superior Cervical Ganglia and Cerebellum from 102 backcrossed mice (TH-MYCN, FVB/NJ and 129/SvJ) were correlated with 349 genotyping markers to identify putative sQTL The mice were the N1 generation of a backcross between TH-MYCN (FVB/NJ background) and wild-type 129/SvJ: TH-MYCN (FVB/NJ) + 129/SvJ -> F1 (TH-MYCN FVB/NJ,129/SVJ) F1 (TH-MYCN FVB/NJ,129/SVJ) + 129/SvJ -> N1
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