1. CSLS Retreat 2007 Matan Hofree & Assaf Weiner 1

2. Outline  A brief introduction to microRNA  Project motivation and goal  Selecting the data sets  Features and data analysis  Results and conclusions 2

3. What are microRNAs?  Non-coding RNAs that regulate gene expression post- transcriptionaly  22~ nucleotide long  Evolutionary conserved  Located in intergenic regions or introns  Often specific to a particular tissue or a developmental stage  Human genomes are predicted to encode as many as 1000 unique miRs, that together possibly regulate one third of all genes 1 1 Bartel et. al. Cell (2005) 3

4. How do miRs work?  Ambion, Inc. Explorations http://www.ambion.com/main/explorations/mirna.htmlhttp://www.ambion.com/main/explorations/mirna.html 4

5. Possible mechanisms of miR-mediated inhibition of gene expression Two Types of mechanisms have been observed: 1. Direct effect on translation - Inhibition of initiation, elongation or termination. 2. Indirect effects – Affects the number of available transcripts by degradation or sequestration. 5 Timothy W. Nilsen (2007) Mechanisms of microRNA-mediated gene regulation in animal cells, Trends in Gen. 23, 5,243-249.

6. miR-target interaction principles 6  Target site in mRNA 3’UTR  Incomplete base pairing  G:U base-pairing  7 fully complementary bases in 5’ of miRNA – ‘Seed’ He-L& Hannon-GJ (2004)

7. miR-target interaction principles 7  Evolutionary conservation of target site  Repeating binding site in many of the known target genes Reinhart-BJ et. al. (2000) Nature 403: 901

8. Motivation for our work “Perfect seed pairing is not generally a reliable predictor for miRNA-target interactions” Didiano D. & Hobert O. (2006) Nat. Struct. Mol. 8 Experiment (modification in target) FunctionalNon- functional wt target site(control) 13 putative targets 3’ UTR with seed match and phylogenetic conservation Binding site in random 3’UTR Original target site in other miR regulated 3’UTR

9. Motivation for our work Conclusions from Hobert’s work:  The presence of a well matched miR target site in 3’ UTR alone, is not a reliable predictor for a miR- target functional interaction.  Proposed miR-target interactions possibly occur in very specific contexts. 9

10. The seed is not enough… 10

11. Discerning the true targets Possible factors contributing to miR-target functionality: 1. Unique miR-target duplex secondary structure 2. 3’ UTR accessibility and target site accessibility 3. RNA-binding protein cofactors 4. Sequence and structural motifs 5. Presence or absence of neighboring binding sites of the same or different miRs 11

12. The long and tedious tale of preparing the data sets... AATTCACATTCCGACTTT...... GGTAGACATTCCCTTAGA...... GGGCTACATTCCTTACGT...... CCGATACATTCCTACGGA...... AGATTACATTCCTGACCT...... 12 Functional miR-target interactionNon-functional miR-target interaction... GCCTCACATTCCCCTTGT...... CAGTGACATTCCCAGAGA...... CATTCACATTCCTCAGTT...... TGTCTACATTCCTTATCA...... GACCTACATTCCTCATTG......

13. The long and tedious tale of preparing the data sets 13 Over expression of miR miR silencing (antisense) treated wt wt treated Search for down- regulated genes Search for up- regulated genes

14. The long and tedious tale of preparing the data sets 14 Over expression of miR miR silencing (antisense) treated wt wt treated Search for down- regulated genes Search for up- regulated genes Lim et al. (2005) over expression of miR-1, miR- 124 and miR-373 in human Hela cells Esau et al. (2006) & Krutzfeldt et al. (2005) silenced miR-122 in mouse liver cells

15. Lim et al. (2005)  Fold change down regulation >= 1.4  Expression change P-Val < 0.001  Get equally expressed genes ( P-Val > 0.85)  Expression above median  Checked fold change ~1 miR-regulated groupmiR-non-regulated group 15 Screen out detection anomalies as defined by array Consistency between 12H and 24H sets Full 2-8(7 nt) seed match Screen out detection anomalies as defined by array Consistency between 12H and 24H sets Full 2-8(7 nt) seed match

16. Lim et al. (2005) 16 * Taking into account overlaps between groups

17. Results so far… 17

18. CLDN12 AAGAAAACTTCTTGTAGCCTCACATTCCCCTTGTGCAAAGAGCTC TAGLN2 TAGATATATATTTTAGCAGTGACATTCCCAGAGAGCCCCAGAGCT ARCN1 GCCACAGTCTGTAATCCATTCACATTCCTCAGTTTCACCACCTCC TIP120A TTTCATTCCGTTTGGATGTCTACATTCCTTATCAAAGGATATAAA PGM2 TTATGTGTTTTACAAAGACCTACATTCCTCATTGTTTCATGTTTG... Target conservation 18 (a)Targets alignment around seed

19. Target conservation 19 CLDN12 C A C A T T C C C 0.5 0.9 0.6 0.8 0.2 0.4 0.6 0.8 0.8 (a)Targets alignment around seed (b)Extracting conservation values for each target (UCSC hg17)

20. CLDN12 C A C A T T C C C 0.5 0.9 0.8 0.4 0.6 0.6 0.6 0.8 0.8 TAGLN2 G A C A T T C C C 0.5 ARCN1 C A C A T T C C T 0.5 TIP120A T A C A T T C C T 0.6 PGM2 T A C A T T C C T 0.7 ======================================================== Conservation: 0.5 Target conservation 20 (a)Targets alignment around seed (b)Extracting conservation values for each target (UCSC hg17) (c)Selecting the median value for each position

21. Target conservation 21 Corrected α = 0.0005

22. Target conservation 22 Corrected α = 0.0005

23. Target conservation 23 Corrected α = 0.0005

24. Seed repeats in 3’ UTR 24 The positive group has statistically significantly more sequences with repeating seeds (p=0.00027)

25. Neighboring seeds density CLDN12 AAGAAAACTTCTTGTAGCCTCACATTCCCCTTGTGCAAAGAGCTC TAGLN2 TAGATATATATTTTAGCAGTGACATTCCCAGAGAGCCCCAGAGCT ARCN1 GCCACAGTCTGTAATCCATTCACATTCCTCAGTTTCACCACCTCC TIP120A TTTCATTCCGTTTGGATGTCTACATTCCTTATCAAAGGATATAAA PGM2 TTATGTGTTTTACAAAGACCTACATTCCTCATTGTTTCATGTTTG -20 -10 0 10 20 25 hsa-miR-302b hsa-miR-507 0.2 0 0.1 Seed density of window Seeds density = mean of targets density 0.12

26. Neighboring seeds density 26 Corrected α= 0.00125  Overlapping seeds (miR binding sites) may interfere with miR function  Neighboring seeds contribute to functionality  Possibly favored periodical distances between seeds ?

27. miR-mRNA binding pattern 27  We need a way to determine what is the preferable secondary structure of the miR-target complex  RNAcofold from the Vienna package target 5' UUGGCUCACUUGCCUUAGTCATCGCTA 3' miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5' target 5' UUGGUCAUUUUUUUUAUAUUGCCUUAU 3' miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5' target 5' AUCAUAUUAAUUGUGCCUUAUAGTAAT 3' miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5'

28. (a) Using Vienna package RNAcofold we estimated the interaction between the miR and putative targets target 5' U G 3' UGGC UCACU UGCCUUA ACCG AGUGG ACGGAAU miRNA 3‘ UA CGC U 5‘ target 5' U U UUUUUAUAU U 3‘ UGG CAUUU UGCCUUA ACC GUAAG ACGGAAU miRNA 3‘ UGGCGC U 5‘ target 5' A AUAUUAAU U 3‘ UCA UGUGCCUUA AGU GCACGGAAU miRNA 3' ACCGUA GGC U 5' miR-mRNA binding pattern 28 Bound Not bound

29. target 5' U G 3' UGGC UCACU UGCCUUA ACCG AGUGG ACGGAAU miRNA 3‘ UA CGC U 5‘ target 5' U U UUUUUAUAU U 3‘ UGG CAUUU UGCCUUA ACC GUAAG ACGGAAU miRNA 3‘ UGGCGC U 5‘ target 5' A AUAUUAAU U 3‘ UCA UGUGCCUUA AGU GCACGGAAU miRNA 3' ACCGUA GGC U 5' (b) For each miR position we calculated the fraction of times the position was found bound in each set. miR-mRNA binding pattern 29 2/ 3 3/3

30. miR-mRNA binding pattern miR-1miR-124 30 Corrected p-value α = 0.0025

31. miR-mRNA binding pattern 31

32. miR-target bound positions 32

33. miR-target bound positions 33 Seed extension contributes to functionality miR-373 - another example of difference in its operation mechanism Seed extension contributes to functionality miR-373 - another example of difference in its operation mechanism

34. Target accessibility 34 Corrected α = 0.0003

35. Target accessibility 35 Corrected α = 0.0003

36. Conclusions 36 miR specific conclusions  Different binding patterns characterize each miR  miR-1 position 13  miR-373 low seed affinity  The function of certain miRs is perhaps more dependent on multiple binding sites (miR-373).

37. Conclusions 37 Generalizing properties  Positive group targets show evolutionary conservation  Positive group targets are more accessible  Positive group is enriched in repeating binding sites of the same miR  Indication for miR cooperation or interference (periodic binding sites?)

38. What’s next?  Apply all the tests on additional data sets  Redefine the data sets with imperfect seeds  Further search for structural motifs enriched in our positive set  Explore 5’ UTR of the sets we defined for sequence or structural motifs  Suggest a biological model to mechanism for miR-target specificity  Possibly try to generalize the features we’ve found in an SVM based classification program 38

39. Acknowledgements  Dr. Yael Altuvia  Prof. Hanah Margalit  All the wonderful people in Hanah’s lab. 39