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Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition | PLOS Computational Biology

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Author Summary The DNA encoding most eukaryotic genes is interrupted by long sequences called introns. These introns need to be removed through the process of splicing to produce the mature messenger RNA. The process of splicing plays a critical role in determining the exact aminoacid content of the ensuing protein. Several molecules denominated small nuclear ribonucleo proteins (snRNPs) are involved in finding the appropriate 5′ donor splicing sites for splicing. Transcription and splicing occur simultaneously and the ultimate product depends on the relative speed of transcription and the stochastic dynamics underlying splicing. Here we propose a biophysically plausible theory that describes the ongoing interactions between transcription and splicing. We show that the theoretical predictions are consistent with experimental measurements of the abundance patterns of different exons and transcripts across tissues.

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Eukaryotic genes are typically split into exons that need to be spliced together to form the mature mRNA. The splicing process depends on the dynamics and interactions among transcription by the RNA polymerase II complex (RNAPII) and the spliceosomal complex consisting of multiple small nuclear ribonucleo proteins (snRNPs). Here we propose a biophysically plausible initial theory of splicing that aims to explain the effects of the stochastic dynamics of snRNPs on the splicing patterns of eukaryotic genes. We consider two different ways to model the dynamics of snRNPs: pure three-dimensional diffusion and a combination of three- and one-dimensional diffusion along the emerging pre-mRNA. Our theoretical analysis shows that there exists an optimum position of the splice sites on the growing pre-mRNA at which the time required for snRNPs to find the 5′ donor site is minimized. The minimization of the overall search time is achieved mainly via the increase in non-specific interactions between the snRNPs and the growing pre-mRNA. The theory further predicts that there exists an optimum transcript length that maximizes the probabilities for exons to interact with the snRNPs. We evaluate these theoretical predictions by considering human and mouse exon microarray data as well as RNAseq data from multiple different tissues. We observe that there is a broad optimum position of splice sites on the growing pre-mRNA and an optimum transcript length, which are roughly consistent with the theoretical predictions. The theoretical and experimental analyses suggest that there is a strong interaction between the dynamics of RNAPII and the stochastic nature of snRNP search for 5′ donor splicing sites.

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Microarrays,DNA transcription,Genomic signal processing,RNA splicing,Genomics,Introns,Mammalian genomics,Eukaryota

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https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002747

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Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition

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Author Summary The DNA encoding most eukaryotic genes is interrupted by long sequences called introns. These introns need to be removed through the process of splicing to produce the mature messenger RNA. The process of splicing plays a critical role in determining the exact aminoacid content of the ensuing protein. Several molecules denominated small nuclear ribonucleo proteins (snRNPs) are involved in finding the appropriate 5′ donor splicing sites for splicing. Transcription and splicing occur simultaneously and the ultimate product depends on the relative speed of transcription and the stochastic dynamics underlying splicing. Here we propose a biophysically plausible theory that describes the ongoing interactions between transcription and splicing. We show that the theoretical predictions are consistent with experimental measurements of the abundance patterns of different exons and transcripts across tissues.

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https://journals.plos.org/ploscompbiol/article/figure/image?id=10.1371/journal.pcbi.1002747.g007&size=inline

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Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition

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Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition

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