Networks of Splice Factor Regulation by Unproductive Splicing Coupled With Nonsense Mediated mRNA Decay
Desai, A.; Hu, Z.; French, C. E.; Lloyd, J. P. B.; Brenner, S. E.
Show abstract
BackgroundNonsense mediated mRNA decay (NMD) is an RNA surveillance pathway that degrades aberrant transcripts harboring premature termination codons. This pathway, in conjunction with alternative splicing, regulates gene expression post-transcriptionally. Nearly all serine and arginine-rich (SR) proteins and many heterogeneous nuclear ribonucleoproteins (hnRNPs) produce isoforms that can be degraded by the NMD pathway. Many splicing factors have been reported to be regulated via alternative splicing coupled to NMD. However, it is still uncharacterized that to what extent NMD contributes to the regulation of splicing factors. ResultsHere, we characterized a regulatory network of splicing factors through alternative splicing coupled to NMD. Based upon an extensive literature search, we first assembled a network that encompasses the current knowledge of splice factors repressing or activating the expression of other splicing factors through alternative splicing coupled to NMD. This regulatory network is limited, including just a handful of well-studied splicing factors. To gain a more global and less biased overview, we examined the splicing factor-mRNA interactions from public crosslinking-immunoprecipitation (CLIP)-seq data, which provides information about protein-RNA interactions. A network view of these interactions reveals extensive binding among splicing regulators. We also found that splicing factors bind more frequently to transcripts of other splicing factors than to other genes. In addition, many splicing factors are targets of NMD, and might be regulated via alternative splicing coupled to NMD, which is demonstrated by the significant overlap between the experimental network and eCLIP-network. We found that hierarchy of the splicing-factor interaction network differs from the hierarchy observed for transcription factors. ConclusionThe extensive interaction between splicing factors and transcripts of other splicing factors suggests that the potential regulation via alternative splicing coupled with NMD is widespread. The splicing factor regulation is fundamentally different from that of transcription factors.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Direct and indirect effects of spliceosome disruption compromise gene regulation by Nonsense-Mediated mRNA Decay 96%
- RNA polymerase II is recruited to DNA double-strand breaks for dilncRNA transcription in Drosophila 93%
- Circular RNA Pde4dip regulates myogenesis by interacting with Zfp143 mRNA: A novel regulatory axis 92%
Similar papers in this journal
- Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases 94%
- CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure 94%
- Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention 94%
Similar papers in this journal
- Recursive splicing is a rare event in the mouse brain 94%
- Common tissue-specific expressions and regulatory mechanisms of c-KIT isoforms with and without GNNK and GNSK sequences across five mammals 94%
- Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions 93%
Similar papers in this journal
- The Exon Junction Complex Core Represses Caner-specific Mature mRNA Re-splicing: A Potential Key Role in Terminating Splicing 94%
- ADAR1 regulates alternative splicing through an RNA editing-independent mechanism. 93%
- Sequence-based models for RNA-Protein interactions imputation might be insufficient for novel signal prediction in eCLIP data 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.