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![Page 1: Target mRNA abundance dilutes microRNA and siRNA activity Aaron Arvey ISMB 2010 MicroRNA Mike needs help to degrade all the mRNA transcripts!](https://reader036.fdocuments.in/reader036/viewer/2022062309/56649ebb5503460f94bc3f73/html5/thumbnails/1.jpg)
Target mRNA abundance dilutes microRNA and siRNA activity
Aaron ArveyISMB 2010
MicroRNAMike needshelp todegrade all the mRNAtranscripts!
![Page 2: Target mRNA abundance dilutes microRNA and siRNA activity Aaron Arvey ISMB 2010 MicroRNA Mike needs help to degrade all the mRNA transcripts!](https://reader036.fdocuments.in/reader036/viewer/2022062309/56649ebb5503460f94bc3f73/html5/thumbnails/2.jpg)
Target mRNA abundance dilutes microRNA and siRNA activity
Erik Larsson Chris Sander Christina Leslie Debbie Marks
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![Page 3: Target mRNA abundance dilutes microRNA and siRNA activity Aaron Arvey ISMB 2010 MicroRNA Mike needs help to degrade all the mRNA transcripts!](https://reader036.fdocuments.in/reader036/viewer/2022062309/56649ebb5503460f94bc3f73/html5/thumbnails/3.jpg)
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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
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microRNA pathway
siRNA pathway
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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
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microRNAs and siRNAs
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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
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microRNA targets and siRNA off-targets
siRNA primary targets
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microRNAs induce different amounts of downregulation
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BigShift
LittleShift
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Concept: Small RNAs with
many targets downregulate each individual target to a
lesser extent
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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)
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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
Examples of differential regulation on shared targets
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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
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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.
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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
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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)
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Functional Examples
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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:
• Can also consider the total number of molecules degraded v
€
logxTx0
⎛
⎝ ⎜
⎞
⎠ ⎟= log
x0 − v
x0
⎛
⎝ ⎜
⎞
⎠ ⎟
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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
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Velocity is correlated with target abundance and follows Michaelis-Menten kinetics
Velocity can be estimated by
€
− x0 −xTx0
x0∑
x0 Assayed by RNA - Seq (or microarray)
xT / x0 Assayed by microarray
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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.)
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Questions
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ErikLarsson
ChrisSander
ChristinaLeslie
DebbieMarks
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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
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Individual target abundance is correlated with downregulation
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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
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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
Franco-Zorrilla et al (2007)
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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
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Past Evidence: Dilution In Solution
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Haley & Zamore (2004)
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Past Evidence - Toxicity
Anderson et al (2008)
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Past Evidence - Dilution In Cells
Ebert et al 2007
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Normalization
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