Target mRNA abundance dilutes microRNA and siRNA activity

Aaron ArveyMemorial Sloan Kettering Cancer Center

MicroRNAs and Human DiseaseFebruary 12th 2011

Subtitle:All Target Genes Are Sponges

Concept: Small RNAs with

many targets downregulate each individual target to a

lesser extent

Target Concentration

Dow

nreg

ulat

ion

microRNAs induce different amounts of downregulation

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BigShift

LittleShift

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)

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Off-Target Concentration

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Target ConcentrationM

ean

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Average downregulation is correlated with

target concentration

A single target is effected

by all other targets

Sod1Mapk14GapdhPpib

Pairwise examples Examples of

differential regulation on shared targets

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Target Concentration

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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

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Velocity is correlated with target abundance and follows Michaelis-Menten kinetics

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Concentration of Predicted Targets (RPN)

Vel

ocity

(a.

u.)

Target Concentration

Previous work by Haley & Zamore (2004) suggested similar kinetics in extract usingsingle competitor target

ERIK’S SLIDEPoster #245

1778 siRNA transfection experiments

AU-rich constructssupport turnover

as important mechanism

AREsE

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acy

Recent Literature

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Cancer:PTEN pseudogene 1 (PTENP1) regulates cell cycle by way of PTEN

Poliseno et al, 2010

Cazalla et al, 2010Virus & Host:Herpesvirus transcriptsdownregulate host microRNAs

Questions

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Erik LarssonPoster #245

Chris Sander

Christina Leslie

Debbie Marks

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QuickTime™ and a decompressor

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Anders JacobsenPoster #232

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Consequences

• Each microRNA is unique in its ability to downregulate targets

• Each cellular context presents different ‘sponges’

• siRNA design criterion

• Evolutionary constraints

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Pairwise examplesQuickTime™ and a decompressor

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• Smad5 downregulation– miR-155: -1.29– 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)

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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]

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Haley & Zamore (2004)

Kinetics in drosophila lysate

Pro

duct

(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)

Cha

nge

in m

olec

ules

(vel

ocity

nM

/min

)

1nM

5nM

20nM

60nM

Past Evidence: Dilution In Solution

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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

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Methods: Target Abundance

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Methods: DownregulationTime Course

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Correlation between siRNA off-target abundance and primary target downregulation

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Off Target Abundance

Log2

Exp

ress

ion

Rat

ioof

prim

ary

targ

et

Past Evidence - Toxicity

Anderson et al (2008)

Past Evidence - Dilution In Cells

Ebert et al 2007

Normalization

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