1. Pre-mRNA secondary structures influence exon recognition Michael Hiller Bioinformatics Group University of Freiburg, Germany

2. Michael Hiller Current model of splicing enhancer silencer

3. Michael Hiller Secondary structure of (pre-)mRNA (pre-)mRNA is not a linear sequence: structural elements: IRE, IRES, SECIS, A to I editing secondary structure and splicing: –stem structure containing the exon 10 donor leads to exon skipping of MAPT –regulation of mutually exclusive exons in FGFR2 and Drosophila DSCAM SR proteins / hnRNPs have “single-stranded RNA binding domains” bind hairpin loops (Nova1, hnRNP A1, SRp55)

4. Buratti et al. Mol and Cell Bio. 24(3) 2004 Michael Hiller Fibronectin EDA exon General trend for splicing motifs to be single-stranded? wt mutant

5. Michael Hiller 1. Data set Experimentally verified splicing motifs AEDB motif database: –motifs with their natural pre-mRNA sequence context –only motifs shorter than 10 nt final set of 77 motifs intronic/exonic enhancers/silencers from >6 species including CFTR, FN1, CD44, FGFR1/2, SMN1, tra2beta

6. Michael Hiller 2. How to measure single-strandedness? Probability that an mRNA part is completely Unpaired the higher PU, the higher the single-strandedness use all (sub)optimal structures consider the free energies of structures allow comparison for motifs of the same length

7. Michael Hiller 3. In which region is pre-mRNA free to fold? long range base pairs are less likely –protein binding –co-transcriptional structure formation –need more time experimental evidence that folding is limited to ≈ 50 nt

8. Michael Hiller 3. In which region is pre-mRNA free to fold? consider short range base pairs symmetrical context lengths 11 – 30 nt compute average PU

9. Michael Hiller Results and Statistics Results real data: PU = 0.25 control 1:PU = 0.15P<0.01 control 2:PU = 0.18P<0.01 control 3:PU = 0.15P<0.01 control 4:PU = 0.12P=0.046 control 5:PU = 0.15P<0.001 77 experimentally verified motifs: average PU = 0.25

10. Michael Hiller Results and Statistics negative correlation between PU value and GC content of the flanks (r = -0.64)  all null models have the same GC content Consistent results for: different measurements for single-strandedness different context lengths (11-20 nt and 11-50 nt) verified motifs are significantly more single-stranded attributed to the flanks

11. Michael Hiller Experimental testing inserts with known splicing motifs (TAGGGT, hnRNP A1)

12. Michael Hiller Experimental testing secondary structure of ESE / ESS affects splicing

13. Michael Hiller Can we detect structural selection on predicted motifs ? divide all 4096 hexamers into [Stadler et al. PLoS Genet. 2006] –enhancers –splicing neutral –silencers for each hexamer get „overall PU value“ in –real exons –pseudo exons –intronic regions between a real and a decoy donor/acceptor site

14. Michael Hiller Selection on structural context of predicted motifs Compare motifs with equal number of GC´s ( e.g. GAAGAA with AACCTA ) higher single-strandedness - for enhancers in exons - for silencers in pseudo exons and decoy regions

15. Michael Hiller Selection on structural context of predicted motifs structural context has a widespread and general importance secondary structures are subject to selection How often is selection strong enough to overcome the correlation between PU and GC?

16. Michael Hiller SNP can change secondary structures [Shen et al. PNAS, 1999] secondary structure might be important for –design and interpretion of mutagenesis experiments –basis of mutations that affect splicing Implications – splicing effect of mutations

17. Michael Hiller Implications – splicing effect of mutations human CFTR exon 12 : 25G  A mutation reduces exon inclusion from 80 to 25%

18. Michael Hiller Implications – splicing effect of mutations rat beta-tropomyosin exon 8: - mutations in the first enhancer  no effect on splicing - mutations in the second enhancer  strong effect - mutations in the third enhancer  weak effect

19. Michael Hiller Conclusion verified splicing motifs are more single-stranded structural context of predicted ESEs/ESSs under natural selection selection pressure on a coding exon: –coding sequence –splicing signals –structural context for splicing motifs another piece for the ‘mRNA splicing code’

20. Michael Hiller Thank you University Freiburg –Rolf Backofen University of Erlangen-Nürnberg –Stefan Stamm –Zhaiyi Zhang