Abstract
The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = −0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation.
Highlights
Alternative splicing of pre-mRNA commonly determines the protein output of mammalian genes, with most genes generating multiple mRNA and protein products [1]
G containing triplets contribute to Polypyrimidine tract binding protein 1 (PTBP1) binding To examine the interactions of PTBP1 across many binding sites, we used a set of PTBP1-bound sequences identified by crosslinking immunoprecipitation (CLIP) [13]
In previous studies we found that a minimal high affinity binding site for the protein extended across 25 to 30 nucleotides, about the average size of the CLIP clusters (29 nt) [27]
Summary
Alternative splicing of pre-mRNA commonly determines the protein output of mammalian genes, with most genes generating multiple mRNA and protein products [1]. The expression and activity of these splicing regulatory proteins can vary with development, cell type, or cellular stimulus [3] This complex combinatorial regulation can be seen in the conserved sequences within and surrounding alternative exons, which generally contain the binding sites for many different regulators. These sequences make up what is sometimes called the splicing code as they determine where and when the exon is spliced into an mRNA [4,5,6,7] Such a code should allow the development of models that predict exon regulation based solely on the RNA binding affinity of the many regulatory proteins and their other interactions. This is not currently feasible, in part due to our incomplete understanding of RNA recognition by the splicing regulators and their mechanisms of action
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