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Systematic Identification and Analysis of Exonic Splicing Silencers

Systematic Identification and Analysis of Exonic Splicing Silencers. Zefeng Wang, Michael E. Rolish, Gene Yeo, Vivian Tung, Matthew Mawson, and Christopher B. Burge Cell 17 December 2004. Exonic Splicing Silencers. Cis- Regulatory Enhancers often Serine/Argenine-residue proteins

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Systematic Identification and Analysis of Exonic Splicing Silencers

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  1. Systematic Identification and Analysis of Exonic Splicing Silencers Zefeng Wang, Michael E. Rolish, Gene Yeo, Vivian Tung, Matthew Mawson, and Christopher B. Burge Cell 17 December 2004

  2. Exonic Splicing Silencers • Cis-Regulatory • Enhancers often Serine/Argenine-residue proteins • Silencers often hnRNP A/B protiens Source: Fu, X. Toward a Splicing Code. Cell. 2005

  3. Method

  4. Control Test • Transfected 2 known ESS sequences and one random control • RT-PCR confirmed that the mRNAs produced had shorter transcripts

  5. Systematic Identification • Created 106 through foldback DNA creating an estimated 106 DNA decamers • Confirmed randomness by sampling and sequencing some of the sequence

  6. Experiment • Conducted 236 transfections in 17 batches and identified 141 ESS decamers • Clustered based on sequence similarity and multiply aligned with CLUSTALW • Weight matrices were calculated for each cluster • Re-inserted 21 of the ESSs into the reporter to assess silencer minigene • 45% of the cells expressed GFP, • < 1% of control cells expressed GFP.

  7. Novel Known ESS Known ESS Novel Novel Donor Known ESS/Donor

  8. Verifications • Transfected HeLa cells with 12 ESS decamers • lead to significant exon skipping • Ran RNAfold on the DHFR test exon and inserted ESSs • Found no significant difference in RNA secondary structure • Used a different test exon, SIRT1 • Gave rise to skipping • Inserted stop codons to detect NMD • No evidence of influence (exon skipping detected)

  9. Shortcomings • Cannot find ESS sequences of proteins underexpressed in transfected cell • Some ESS sequences might be overlapped by ESEs • Some ESS proteins are weak and/or require multiple copies in the gene, and cannot be activated

  10. Biases in Different Exon Types

  11. ExonScan • Developed an algorithm to simulate splicing • First-generation exon splice simulator (not gene/exon finder) • Ignores complexities of trans-acting factors • Ignores higher-order effects of cooperativity or interference between cis-elements • Ignores RNA secondary structures

  12. Scoring • Each potential exon is scored for its splicing candidacy with presence of • Strong/weak splice sites (+/-) • ESEs (+) • ESSs (-) • ISEs (+) • Scoring for regulatory motifs is done as

  13. Results /10,891

  14. Toward an RNA Splicing Code “A comprehensive description of for the sequence specificity of pre-mRNA splicing will require a precise knowledge of all the types of splicing and regulatory interactions. …Improved knowledge of such elements should facilitate the development of increasingly effective splicing simulation algorithms.”

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