Where Will They Strike Next? microRNA targeting tactics in the war on gene expression

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

5. mRNA target site A U U C U A G G C A A A U C G C G U G G C G C A C G U U C C G A RISC M= 2 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 3’ 5’ 3’ 5’

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… APETALA2 transcription factor *Rhoades, et. al. (2002) Cell 110, 513-520.

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 beenspectacularly 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 bi • For ask Bartel model bi = 1 • Quantify target “strength” with binding probability • pt is the probability of finding the miRNA bound to target site t in the mRNA population

9. m = miRNA* sequence t = target site sequence b = mismatch parameters A b2 b1 b3 U U b4 C U A G b5 G C A A A U C G mRNA C G U G G C G C A C G U U C C G A RISC Boltzmann factors • Now “mismatch score” is position-dependent • Boltzmann factor gives binding probability • Quantitative model built, but how to find bi? 3’ 5’ 3’ 5’

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 • ft= nt/ N • If the model works ft and pt must agree! • Determine bi by looking for this agreement • Maximize the probability that the data (ft) could have come from the model (pt)… *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 biwhich maximizes the probability of getting the data from our model

12. b4 b1 b5 b3 b2 p24 f24 Optimization Cartoon • Maximize L to get bi miRNA sequence measured fraction bound UAGCA f1f2f3f4f5 ... f24 Parameter Controls Inputs Binding Probabilities miRNAs 0 data

13. b4 b1 b5 b3 b2 p24 f24 Optimization Cartoon • Maximize L to get bi miRNA sequence measured fraction bound UAGCA f1f2f3f4f5 ... f24 Parameter Controls Inputs Binding Probabilities miRNAs 0 data

14. b4 b1 b5 b3 b2 p24 f24 Optimization Cartoon • Maximize L to get bi miRNA sequence measured fraction bound UAGCA f1f2f3f4f5 ... f24 Parameter Controls Inputs Binding Probabilities miRNAs 0 data

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 biwhich 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: pg • Ask Bartel complementarity model: fg • 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 bi 3’ 5’ (i)

19. target 5’ 3’ 5’ 3’ mir162a 14 15 1 21 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…

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 N(E) training targets majority function majority function not training targets! N(E)

22. mir159b -- MYB N(E) 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 ofNetworks • 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