1. Presenting: Asher Malka Supervisor: Prof. Hermona Soreq

2. miRNAs short (20-25nt) RNA molecules transcribed as a precursor RNA molecule bind mRNAs via base pairing cause mRNA degradation or translational repression more than 200 in humans (~1% of the genome)

3. Nucleus miRNA The role of miRNA in the regulation of protein synthesis miRNA

4. How do miRNA Control mRNA and Protein expression levels? Binding to mRNA interferes with translation. Facilitates cleavage of mRNA Reduces protein expression levels. Reduces mRNA expression levels. miRNA

5. Translational Inhibition Imperfect match between siRNA or miRNA in RISC and target mRNA RISC usually binds 3’ UTR Mechanism of inhibition... ????

6. mRNA Degradation Perfect complementarity between siRNA or miRNA in RISC and the target mRNA Cleavage by RISC ‘Slicer’ activity –Unknown protein

7. miRNA Function control and modulation of: –cell proliferation –cell death –fat metabolism –neuronal patterning –leaf and flower development –hematopoietic lineage differentiation

8. micro RNA – Computational Approach Problem 1: Finding putative microRNA from a sequence –Horesh et al Problem 2: Computing secondary structure of a given sequence –Zuker & Steigler, minimum free energy, using dynamic programming Problem 3: miRNA predicting algorithms –Lim et al, MiRscan Problem 4: Predicting miRNA target genes

10. base pair contribution cost of gap in mRNA cost of gap in microRNA cost of loop (i’, j’) (i, j) (i’, j) (i, j’) (i-1, j-1)

11. Prediction of miRNA Targets Fairly straightforward in plants [Rhoades et al., 2002] –miRNAs almost perfectly complementary to their targets –Methods search for near-perfect matches in 3’ UTRs –Also look for conservation of target sites Not so easy in animals –miRNA:target not very complementary –Method for plants tried in other organisms  Results same as would be expected by chance

12. Target Prediction in Vertebrates Some assumptions to start with: –Interaction of 5’ end of miRNA and the target most critical –Target binding sites likely conserved –binding sites common in 3’ UTR

13. Prediction of Mammalian MicroRNA Targets Benjamin P. Lewis, I-hung Shih, Matthew W. Jones-Rhoades, David P. Bartel, and Christopher B. Burge

14. Example - TargetScan Algorithm by Lewis et al 2003 The Goal – a ranked list of candidate target genes Stage 1: Search UTRs in one organism –Bases 2-8 from miRNA = “miRNA seed” –Perfect Watson-Crick complementarity –No wobble pairs (G-U) –7nt matches = “seed matches”

15. TargetScan Algorithm Stage 2: Extend seed matches –Allow G-U (wobble) pairs –Both directions –Stop at mismatches

16. TargetScan Algorithm Stage 3: Optimize basepairing –Remaining 3’ region of miRNA –35 bases of UTR 5’ to each seed match –RNAfold program (Hofacker et al 1994)

17. Stage 4: Folding free energy (G) assigned to each putative miRNA:target interaction Assign rank to each UTR Repeat this process for each of the other organisms with UTR datasets TargetScan Algorithm

18. TargetScan Program Flow

19. Mean number of predicted targets per miRNA for authentic miRNAs (filled bars) and for shuffled sequences of miRNAs (open bars)

20. Mean number of targets per miRNA using alternative miRNA seed positions for authentic miRNAs and for shuffled controls

21. Reporter construct used to evaluate the interaction between miR-26a and the SMAD-1 3 UTR. * Fragment of the UTR containing two miR target sites inserted within the luciferase 3 UTR. * Mutant construct with three point substitutions disrupting pairing to each miR seed.

22. Box plots showing the luciferase activity after reporter plasmids were transfected into HeLa cells.

23. Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that thousands of Human Genes are MicroRNA Targets Benjamin P. Lewis, Christopher B. Burge and David P. Bartel

24. What’s new? Chicken and dog genome assemblies available Updated annotations of the human, mouse and rat genomes Requiring conservation in all five genomes allows elimination of score cutoffs Using 2-7 seed instead of 2-8 still retains specificity

25. Alignment of orthologous segments of the HIC UTR, showing the conserved match to the miR-23a seed

26. What Else? Next, they looked at the sequence flanking the seed matches, searching for conserved positions Such positions might contribute specificity to the miRNA:target interaction

27. Number of miRNA:target relationships predicted, with estimates of the number of false positives, for searches based on the indicated criteria.

28. Sequence conservation in positions flanking conserved miRNA seeds. The percentage of seed matches in which that position was conserved in all five vertebrates is shown, (red - conservation of adenosine). The gray dashes indicate the same analysis for conserved matches to control sequences.