1. Target mRNA abundance dilutes microRNA and siRNA activity Aaron Arvey ISMB 2010 MicroRNA Mike needs help to degrade all the mRNA transcripts!

2. Target mRNA abundance dilutes microRNA and siRNA activity Erik Larsson Chris Sander Christina Leslie Debbie Marks

3. Background: Small RNAs mediate mRNA degradation Small RNAs are 19-25nt RNA Induced Silencing Complex (RISC) –Protein complex that uses small RNA to guide degradation microRNAs –Processed from non-coding genes siRNAs (for our purposes) –Transfected into the cell –Knockdown specific gene –Unintended “off-targets” are also downregulated microRNA pathway siRNA pathway

4. Background: Transfected small RNAs induce target mRNA degradation Double stranded RNA molecules are transfected into cell lines Concentrations of small RNA are very high, ~100nM Target mRNAs are degraded microRNAs and siRNAs

5. Background: Target Prediction microRNAs –Transcript 3’ UTR is most likely to be targeted –microRNA 5’ “seed” region guides targeting siRNAs –Off-targets have similar targeting rules as microRNAs –Primary targets have nearly exact complementarity microRNA targets and siRNA off-targets siRNA primary targets

6. microRNAs induce different amounts of downregulation Big Shift Little Shift

7. Concept: Small RNAs with many targets downregulate each individual target to a lesser extent

8. Meta-analysis of high throughput studies to explore hypothesis 178 transfection experiments in HeLa and HCT116 cell lines –61 miRNA-mimics (Lim 2005, Grimson 2007, He 2007, Linsley 2007, Selbach 2008) –98 siRNA (Kittler 2007, Anderson 2008, Jackson 2006, Schwarz 2006) –19 chimeras (Lim et al, 2005, Anderson 2008) Microarray assay pre- and post-transfection RNA-Seq quantifies mRNA target abundance (Morin 2008)

9. Downregulation is significantly correlated with target concentration

10. Downregulation of siRNA primary target and off-targets is significantly correlated with off-target concentration

11. Shared targets show that downregulation is determined by target abundance Measure target abundance on all targets Measure downregulation on shared targets

12. Pairwise examples Examples of differential regulation on shared targets

13. Pairwise examples Smad5 downregulation –miR-155: -1.29 –miR-106: -0.1 Target abundance –miR-155: 142 –miR-106: 315 Differences –Downregulation: 1.19 –Abundance: 173

14. Shared targets are more downregulated by microRNAs with fewer targets

15. Conclusion: Small RNAs with more targets downregulate each target to a lesser extent.

16. Consequences: Endogenous regulation by microRNAs Each microRNA is quantitatively unique –Definition of target should perhaps be different for different microRNAs, targets are likely to be quantitatively different The cell as a very finely tuned system of regulation –Increase in one target mRNA detracts from downregulation of another target mRNA –microRNA regulation is always using all available degradation machinery (Khan et al2009), but is still stretched thin Evolutionary constraints –Possibility 1: anti-targets (mRNA transcripts that ‘avoid’ being co-expressed with microRNA) enable the cell to avoid high target concentration –Possibility 2: microRNA expression increases when target mRNAs increase, dosage compensation

17. Consequences: Target abundance limits siRNA activity Limits knockdown of primary target –May limit drug efficacy, especially in small concentration –May limit functional genomic screens Limits the knockdown of off-targets –Increase in off-targets may actually decrease toxicity (Anderson et al, 2008)

18. Functional Examples Cancer: PTEN pseudogene 1 (PTENP1) regulates cell cycle by way of PTEN (Poliseno et al) Environmental Response: Non-coding RNA regulates phosphate starvation response (Franco Zorrilla et al)

19. Kinetics Were we guaranteed to find this result? –No: Depends on dynamic range of kinetic relationship Degradation is a function of speed, time, and concentration –So far, we have only considered downregulation with respect to concentration Downregulation has been defined as the ratio: Can also consider the total number of molecules degraded v

20. Background: RISC Kinetics Multi-turnover enzyme –Single loaded RISC is able to degrade many mRNA transcripts (Hutvágner & Zamore, 2002) RISC is saturated with small RNA upon transfection (Khan et al, 2009) Degradation in lysate is very fast (Haley & Zamore, 2004) [RISC] + [target]  [RISC+target]  [RISC] + [product]

21. Haley & Zamore (2004) Kinetics in drosophila lysate Product (nM) Background: RISC Kinetics Degradation kinetics depend on target concentration 1nM RISC in lysate –Slope of line is velocity –Transcripts degraded at rate of 72-300nM transcript/day Target concentration in cell is likely to be in the range 1-60nM 72nM > 60nM –Ignores transcriptional rate –Ignores cellular context –Ignores localization Target Concentration (nM) Change in molecules (velocity nM/min) 1nM 5nM 20nM 60nM

22. Velocity is correlated with target abundance and follows Michaelis-Menten kinetics Velocity can be estimated by

23. Velocity is correlated with target abundance and follows Michaelis-Menten kinetics Concentration of Predicted Targets (RPN) Velocity (a.u.)

24. Questions Erik Larsson Chris Sander Christina Leslie Debbie Marks

25. We control for several alternative explanations A+U content – Not correlated 3’ UTR length –Correlated, controllable by shared targets Expression of individual targets –Correlated, controllable by shared targets

26. Individual target abundance is correlated with downregulation

27. Caveats of shared-target analysis False positive rate may increase sub-linearly –If false positive rate increases with number of predicted targets, becomes harder to control –The siRNA analysis completely controls for this (since there is only a single primary target!) Length of UTR is 2x normal length in shared targets –Normal: 1167nt – Shared target: 2041nt –Longer 3’ UTR may lead to increased downregulation, though this would not give preference for a specific microRNA

28. Methods: Target Prediction

29. Methods: Target Abundance

30. Methods: Downregulation

31. Time Course

32. Past Evidence - In Vivo Franco-Zorrilla et al (2007)

33. Correlation between siRNA off-target abundance and primary target downregulation Off Target Abundance Log2 Expression Ratio of primary target

34. Past Evidence: Dilution In Solution Haley & Zamore (2004)

35. Past Evidence - Toxicity Anderson et al (2008)

36. Past Evidence - Dilution In Cells Ebert et al 2007

37. Normalization