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

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

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

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)

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

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

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

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

ERIK’S SLIDE Poster # siRNA transfection experiments AU-rich constructs support turnover as important mechanism AREs Efficacy

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

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

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

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

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)

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

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]

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 nM 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

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

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

3’ UTR Length is correlated with expression level

Individual target abundance is correlated with downregulation

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

Methods: Target Prediction

Methods: Target Abundance

Methods: DownregulationTime Course

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

Past Evidence - Toxicity Anderson et al (2008)

Past Evidence - Dilution In Cells Ebert et al 2007

Normalization