1. Where Will They Strike Next? microRNA targeting tactics in the war on gene expression Jeff Reid Miller “Lab” Baylor College of Medicine

2. Outline Introduction to miRNAs The “ask Bartel” model for targeting Our proposed model Discuss predictions made by our model –All positions on the miRNA are not equal –A given miRNA’s targets share function Have a quantitative model that does not suffer from the arbitrariness of ask Bartel

3. Plant microRNAs This talk is about plant miRNAs –Animal miRNAs different, more complicated –If you want to know more about them ask Tuan Tran! What is a microRNA (miRNA)? ~21nt single-stranded non-coding RNAs Processed from stem/loop precursors Bind to mRNA in the cytoplasm Regulate genes –Often relevant to development

4. microRNA biogenesis (conventional wisdom) 1.miRNA gene is transcribed producing primary transcript 2.pri-miRNA processed by dicer… 3...producing miRNA duplex 4.duplex moves out of the nucleus 5.helicase activity unzips duplex 6.mature miRNA forms RNA- induced silencing complex (RISC) 7.RISC recognizes a target site 8.Targeted mRNA is regulated (mRNA cleavage or translational repression) Figure from Bartel, D.P. (2004). Cell 116, 281-297.

5. Target “Acquisition” How does the RISC identify target sites? Based solely on mature miRNA sequence –Consistent all with known examples –“just” string manipulation –With that in mind, consider a simple model… Targets have small “mismatch score” – M –Count non-WC pairs in miRNA/target duplex –Score is independent of position A C G C U C C C C C C U U U U U A A A A G G G G G G G U A G A C target site RISC mRNA M = 2 5’ 3’ 5’ 3’

6. Complementarity Model* Look for 21-mers (mRNA sequence) with M < 4 –Find targets… –mir172a1 [AP2]:At5g60120(1) At4g36920(2) At2g28550(3) At5g67180(3) At5g12900(3) –…turns out most targets of a given miRNA are in genes which share a common function There are some ask Bartel elements to the model –M = 4 targets sharing function included case-by-case –Single bulges are sometimes allowed (mir162, mir163) –Model specificity is problematic… *Rhoades, et. al. (2002) Cell 110, 513-520. APETALA2 transcription factor

7. Selectivity and Specificity Selectivity (false negatives) –Bartel’s model finds “everything” for M < 5 Putative targets from this model (most confirmed by experiment) define the target population Specificity (false positives) –Bartel’s model is problematic M < 5 includes many false positives M < 4 and qualitative ask Bartel elements are necessary for model specificity Our goal is to develop a quantitative model

8. Position Dependent Model Ask Bartel has been spectacularly successful Build on existing model & make it quantitative No a priori justification of position-independence –assumed by the ask Bartel model Extend to a position-dependent mismatch model –Assign mismatch at position i weight  i For ask Bartel model  i = 1 Quantify target “strength” with binding probability –p t is the probability of finding the miRNA bound to target site t in the mRNA population

9. Now “mismatch score” is position-dependent Boltzmann factor gives binding probability Quantitative model built, but how to find  i ? Boltzmann factors m = miRNA* sequence t = target site sequence  = mismatch parameters A C G C U C C C C C C U U U U U A A A A G G G G G G G U A G A C RISC mRNA 11 22 33 44 55 5’ 3’ 5’ 3’

10. Model Comparison Follow DNA binding protein example* –Consider a thought experiment…. Mix many copies of the genome and N copies of the protein and count the number of examples of protein bound to site t – f t = n t / N If the model works f t and p t must agree! Determine  i by looking for this agreement –Maximize the probability that the data ( f t ) could have come from the model ( p t )… *Brown, C.T., and Callan, C.G. (2004). Proc. Natl. Acad. Sci. 101, 2404.

11. Model Testing Probability of data arising from our position dependent mismatch model Obtain best match of model to data by maximizing the log probability Yields set of parameters  i which maximizes the probability of getting the data from our model

12. Optimization Cartoon 11 22 33 44 55 Parameter ControlsInputs miRNAs data Binding Probabilities miRNA sequence UAGCA measured fraction bound f 1 f 2 f 3 f 4 f 5... f 24 0 Maximize L to get  i f 24 p 24

13. Optimization Cartoon 11 22 33 44 55 Parameter ControlsInputs miRNAs data Binding Probabilities miRNA sequence 0 f 1 f 2 f 3 f 4 f 5... f 24 UAGCA f 24 p 24 Maximize L to get  i measured fraction bound

14. Optimization Cartoon 11 22 33 44 55 Parameter ControlsInputs miRNAs data Binding Probabilities miRNA sequence 0 f 1 f 2 f 3 f 4 f 5... f 24 UAGCA f 24 p 24 Maximize L to get  i measured fraction bound

15. Model Testing Probability of data arising from our position dependent mismatch model Obtain best match of model to data by maximizing the log probability Yields set of parameters  i which maximizes the probability of getting the data from our model

16. Review Application of this procedure to miRNAs Optimize to get best agreement between –position-dependent mismatch model: p g –Ask Bartel complementarity model: f g Equal binding probability for each training target Minimal binding to everything else (background) –A contribution we made to the method –necessary to avoid overfitting

17. Multi-miRNA Optimization Given the amount of data we have This method would fail on DNA binding proteins All miRNAs share the same machinery for target recognition (all form the RISC) –DNA binding protein recognition depends on the each specific protein Solution to our problem –Simultaneously optimize for several miRNAs

18. Results - Parameters Multi-miRNA optimization of nine Arabidopsis miRNAs –157b, 159b, 160b, 164a, 165b, 167b, 168a, 171, 172a1 –A set of functionally diverse (21-mer) miRNAs 3’5’ (i)(i) ii

19. Position 14 Mismatch at position 14 –Has no effect on a target’s binding probability! Surprising and exciting because… …this position is known to be special –mir162a target 1g01040 DEAD/DEAH box helicase –Has a bulge at position 14 This analysis did not include mir162a! A provocative result… 14 15 121 3’ 5’ target mir162a

20. Results - Targets Training targets should have low energy –Found by ask Bartel model –Reside in genes which share majority function Targets in the background have high energy –Background targets with low energy are interesting We are particularly interested all the majority function targets for a given miRNA –Especially those which are not training targets Look at distributions of target energies –For each value of M

21. mir165b -- HD-Zip majority function not training targets! training targets majority function N(E)

22. mir159b -- MYB N(E)

23. Conclusions Refined the qualitative complementarity model –A quantitative model which is much less arbitrary Whatever we get, we get – not “ask Miller” –Majority function targets group together at low energy –Bartel finds most targets, our model finds all targets Appropriate experiments could falsify our model –How important is position 14? –Look at some specific ask Bartel targets Advanced technology of optimization –Resolution of the overfitting problem –Simultaneous optimization

24. Encoding of Networks Networks –miRNA families A single target mRNA can be regulated by different miRNAs And a single miRNA can regulate many different mRNAs –Apparently an overlapping and probably redundant regulatory network Encoding –All this regulation encoded in mere text! –How is this encoded in the sequence? –Why is it encoded in this way?

25. Acknowledgements Miller Lab Posse –Jon Miller –Tuan Tran –Will Salerno –Gerald Lim Curtis Callan (Princeton) Keck Center for Computational and Structural Biology BCM Biochemistry Department