Target mRNA abundance dilutes microRNA and siRNA activity

1. Target mRNA abundance dilutes microRNA and siRNA activity Subtitle:All Target Genes Are Sponges Aaron Arvey Memorial Sloan Kettering Cancer Center MicroRNAs and Human Disease February 12th 2011

2. Target Concentration Downregulation Concept: Small RNAs with many targets downregulate each individual target to a lesser extent

3. microRNAs induce different amounts of downregulation Big Shift Little Shift Data from Grimson et. al., 2007

4. Meta-analysis of transfection studies • 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, 2005, Anderson 2008) • Microarray assay post-transfection • RNA-Seq to quantify mRNA target abundance (Morin 2008)

5. Average downregulation is correlated with target concentration Primary Target Downregulation Mean Target Downregulation Off-Target Concentration Target Concentration A single target is effected by all other targets Sod1 Mapk14 Gapdh Ppib

6. Mean Target Downregulation Target Concentration Pairwise examples Examples of differential regulation on shared targets

7. Kinetics Questions • Were we guaranteed to find this result? • Depends on dynamic range of kinetic relationship • Degradation is a function of speed, time, and concentration • We have only considered downregulation and concentration • Downregulation is defined as the ratio: • Result depends on the velocity of degradation v

8. Velocity (a.u.) Target Concentration Concentration of Predicted Targets (RPN) Velocity is correlated with target abundance and follows Michaelis-Menten kinetics Previous work by Haley & Zamore (2004) suggested similar kinetics in extract usingsingle competitor target

9. ERIK’S SLIDE Poster #245 1778 siRNA transfection experiments AU-rich constructssupport turnoveras important mechanism Efficacy AREs

10. Recent Literature Poliseno et al, 2010 Cancer: PTEN pseudogene 1 (PTENP1) regulates cell cycle by way of PTEN Cazalla et al, 2010 Virus & Host: Herpesvirus transcriptsdownregulate host microRNAs

11. Poster #105 Questions Consequences • Each microRNA is unique in its ability to downregulate targets • Each cellular context presents different ‘sponges’ • siRNA design criterion • Evolutionary constraints Debbie Marks Christina Leslie Chris Sander Anders JacobsenPoster #232 Erik LarssonPoster #245

12. 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

13. Consequences • Each microRNA is quantitatively unique • Definition of target should perhaps be different for different microRNAs • Improve target prediction methods • 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

14. Consequences • 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)

16. Recent Literature Environmental Response: Non-coding RNA regulates phosphate starvation response (Franco Zorrilla et al, 2007)

17. 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]

18. Kinetics in drosophila lysate Haley & Zamore (2004) Product (nM) Background: RISC Kinetics 60nM • 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 20nM 5nM 1nM Target Concentration (nM) Change in molecules(velocity nM/min)

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

20. 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

22. 3’ UTR Length is correlated with expression level

23. Individual target abundance is correlated with downregulation

24. 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

25. Methods: Target Prediction

26. Methods: Target Abundance

27. Methods: DownregulationTime Course

28. Correlation between siRNA off-target abundance and primary target downregulation Log2 Expression Ratioof primary target Off Target Abundance

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

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

31. Normalization