Target mRNA abundance dilutes
microRNA and siRNA activity
MicroRNA
Mike needs
help to
degrade all
the mRNA
transcripts!
Aaron Arvey
ISMB 2010
Target mRNA abundance dilutes
microRNA and siRNA activity
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Erik Larsson
Chris Sander
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Christina Leslie
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Debbie Marks
Background: Small RNAs mediate mRNA
degradation
microRNA pathway
– Protein complex that uses small
RNA to guide degradation
• microRNAs
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• Small RNAs are 19-25nt
• RNA Induced Silencing
Complex (RISC)
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– Processed from non-coding genes
– Transfected into the cell
– Knockdown specific gene
– Unintended “off-targets” are also
downregulated
siRNA pathway
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• siRNAs (for our purposes)
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Background: Transfected small RNAs induce
target mRNA degradation
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kT im
are n
eede dec ompe™ and
d to s ress
a
ee th or
is pi c
ture.
microRNAs and siRNAs
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• Double stranded RNA
molecules are transfected
into cell lines
• Concentrations of small
RNA are very high,
~100nM
• Target mRNAs are
degraded
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Background: Target Prediction
• microRNAs
– Transcript 3’ UTR is most likely to
be targeted
– microRNA 5’ “seed” region guides
targeting
microRNA targets and
siRNA off-targets
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• siRNAs
– Off-targets have similar targeting
rules as microRNAs
– Primary targets have nearly exact
complementarity
siRNA primary targets
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microRNAs induce different
amounts of downregulation
Big
Shift
Little
Shift
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Concept:
Small RNAs with
many targets
downregulate each
individual target to a
lesser extent
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)
Downregulation is significantly correlated
with target concentration
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Downregulation of siRNA primary target and off-targets
is significantly correlated with off-target concentration
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Shared targets show that downregulation
is determined by target abundance
Measure target abundance on all targets
Measure downregulation on shared targets
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Pairwise examples
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Examples of
differential
regulation on
shared targets
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Pairwise examples
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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
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Shared targets are more downregulated
by microRNAs with fewer targets
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Conclusion: Small RNAs with more targets
downregulate each target to a lesser extent.
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
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)
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)
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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:
xT 
x 0  v 
log  log

x 0  of molecules
 x 0 degraded v
• Can also consider the total number

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]
Background: RISC Kinetics
– 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
Haley &
Zamore (2004)
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60nM
20nM
5nM
1nM
Change in molecules
(velocity nM/min)
• Degradation kinetics
depend on target
concentration
• 1nM RISC in lysate
Product (nM)
Kinetics in drosophila lysate
Target Concentration (nM)

Velocity is correlated with target abundance
and follows Michaelis-Menten kinetics
Velocity can be estimated by
xT
 x 0 
x0
x0
x0
Assayed by RNA- Seq (or microarray)
xT / x 0 Assayed by microarray
Velocity (a.u.)
Velocity is correlated with target abundance
and follows Michaelis-Menten kinetics
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Concentration of Predicted Targets (RPN)
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Questions
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Debbie
Marks
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Christina
Leslie
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Erik
Larsson
Chris
Sander
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
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: Downregulation
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Time Course
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Past Evidence - In Vivo
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Franco-Zorrilla et al (2007)
Log2 Expression Ratio
of primary target
Correlation between siRNA off-target abundance
and primary target downregulation
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Off Target Abundance
Past Evidence:
Dilution In Solution
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Haley & Zamore (2004)
Past Evidence - Toxicity
Anderson et al (2008)
Past Evidence - Dilution In Cells
Ebert et al 2007
Normalization
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