P ng the Limits to MicroRNA Mediated ontrol of Gene …Probing the Limits to MicroRNA-Mediated...

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Pr obing the Limits to Micr oRNA-Mediated Contr ol of Gene Expression Araks Martirosyan Matteo Figliuzzi, Enzo Marinari, Andrea De Martino Greenwich, 2015

Transcript of P ng the Limits to MicroRNA Mediated ontrol of Gene …Probing the Limits to MicroRNA-Mediated...

Page 1: P ng the Limits to MicroRNA Mediated ontrol of Gene …Probing the Limits to MicroRNA-Mediated Control of Gene Expression Araks Martirosyan Matteo Figliuzzi, Enzo Marinari, Andrea

Probing the Limits to MicroRNA-Mediated Control of Gene Expression

Araks Martirosyan

Matteo Figliuzzi, Enzo Marinari, Andrea De Martino

Greenwich, 2015

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Introduction

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Gene Expression DNA

Protein [1]

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Gene Expression

RNA

Transcription

DNA

Protein

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Gene Expression

RNA mRNA

ncRNA

Transcription

DNA

Protein

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Gene Expression

RNA mRNA

ncRNA

TranscriptionTranslation

DNA

Protein

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ncRNA

RNA mRNA

ncRNA

Constituent RNA

tRNArRNA

...

TranscriptionTranslation

DNA

Protein [2]

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ncRNA

RNA mRNA

ncRNA Regulatory RNA

Constituent RNA

tRNArRNA

...piRNA

siRNA

TranscriptionTranslation

DNA

Protein

miRNA

[3,4]

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miRNA

RNA mRNA

ncRNA Regulatory RNA

Constituent RNA

tRNArRNA

...piRNA

siRNA

TranscriptionTranslation

DNA

Protein

miRNA

[5]

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miRNA binding

3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR

5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR}

}

seed

canonical sitesmRNA

miRNA28

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mRNA cleavage

3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR

5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR

5'UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3'UTR

cleavage

3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR

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Translational repression

3’UTR ACGACGUUCUCAUUCAAUGUUU 5’ UTR

5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR

Translational repression

3’UTR ACGACGUUCUCAUUCAAUGUUU 5’ UTR

5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR

Ribosome

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miRNA-Target interaction networks

* TargetScan* miRanda

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Competing endogenous RNAs (ceRNAs)

ceRNA2

ceRNA1

miRNA

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ceRNA effect

ceRNA2

ceRNA1

miRNA

[6]

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DebateDenzler et al. (2014)

Mouse hepatocytes

Modulation of miRNA target abundance is unlikely to cause significant effects on gene expression through a ceRNA effect.

Bosson et al. (2014)

mouse embryonic stem cell

miRNA-target pool ratios and an affinity partitioned target pool accurately predict miRNA susceptibility to target competition.

[7, 8]

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The goal

1. Quantify the maximal post-transcriptional regulatory power achievable by miRNA-mediated cross-talk,

2. Explore how heterogeneities in binding affinities influence the latter,

3. Compare the effectiveness miRNA-mediated control with other regulatory elements.

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The model

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ceRNA Network

miRNAceRNA1 ceRNA2

[9, 10]

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ceRNA Network: target binding/unbinding

miRNAceRNA1 ceRNA2

C1 C2

+/- +/-k1

+ k1− k2

−k2

+

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ceRNA Network: ceRNA cleavage

miRNAceRNA1 ceRNA2

C1 C2

+/- +/-k1

+ k1− k2

−k2

+

κ1 κ2

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ceRNA Network: transcription

TF1

n1 miRNAceRNA1

TF2

nµ ceRNA2 n2

TFµ

C1 C2

+/- +/-

k ink ink in kout kout kout

b1 b2β

k1+ k1

− k2−

k2+

κ1 κ2

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ceRNA Network: degradation

TF1

n1 miRNAceRNA1

TF2

nµ ceRNA2 n2

TFµ

C1 C2

+/- +/-

Ø

Ø

Ø

Ø

Øk ink ink in kout kout kout

b1 b2β

σ1 σ2

d2d1k1

+ k1− k2

−k2

+

κ1 κ2

δ

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Dynamics ∂mi∂ t

=bini−d imi−k i+miμ+k i

− c i+ξmi−ξ++ξ−

∂μ∂ t

=βnμ−δμ−k i+miμ+(k i

−+κi)ci+ξμ−ξ++ξ−+ξκ

∂ ci∂ t

=−σimi+∑ik i

+miμ−∑i(k i

−+κi)c i+ξci+ξ+−ξ−−ξκ

∂ni ,μ∂ t

=k in f i ,μh (1−ni ,μ)−kout ni ,μ+ξni ,μ

ceRNA

miRNA

complex

TF binding site occupancy

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Dynamics ∂mi∂ t

=bini−d imi−k i+miμ+k i

− c i+ξmi−ξ++ξ−

∂μ∂ t

=βnμ−δμ−k i+miμ+(k i

−+κi)ci+ξμ−ξ++ξ−+ξκ

∂ ci∂ t

=−σimi+∑ik i

+miμ−∑i(k i

−+κi)c i+ξci+ξ+−ξ−−ξκ

∂ni ,μ∂ t

=k in f i ,μh (1−ni ,μ)−kout ni ,μ+ξni ,μ

n̄i ,μ=k in f i ,μ

h

k in f i ,μh +kout

n̄i ,μ

f i ,μ

fast[11]

1

o

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White noise

<ξ+ (t )ξ+ (t ' )>=k i+ m̄iμ̄ δ(t−t ' ) ,

<ξ−(t )ξ−(t ' )>=k i− c̄iδ(t−t ' ) ,

<ξκ(t )ξκ(t ')>=κi c̄ iδ(t−t ') ,<ξμ (t )ξμ(t ' )>=(β n̄μ+δμ̄)δ(t−t ' ),<ξmi(t )ξmi(t ' )>=(bi n̄i+d i m̄i)δ(t−t ' ) ,

m̄i=bi n̄i+k i

− c̄id i+ki

+ μ̄, μ̄=

β n̄μ+∑i(k i

−+κi) c̄iδ+∑i

k i+ m̄i

, c̄i=k i

+ μ̄ m̄iσi+k i

−+κi.

where

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The method

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Mutual Information

Channel

I ( f j ,m2)=∫df jdm2 p(f j ,m2) log2p(f j ,m2)p(f j) p(m2)

I opt=max p(f j) I (f j ,m2)

[12]

Channel Capacity

f jm2

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Noise and information transmission

I opt=0

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Noise and information transmission

I opt∼0

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Noise and information transmission

I opt=log21

√2π e ∫ df j 1/σ f jPopt (f j)=

1Z1σ f j

[13, 14]

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Channels

TF1

miRNAceRNA ceRNA2 ceRNA2

TF2

ITF

miRNAceRNA

TF1 TFμTF2TFμ ImiRNA

miRNA-channel TF-channel

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Results

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The capacity of the miRNA-channel is maximal in a specific range of miRNA-ceRNA binding rates

ITF - ImiRNATF1

miRNAceRNA ceRNA2 ceRNA2

TF2

miRNAceRNA

ImiRNA ITF

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miRNA-mediated regulation may represent the sole control mechanism in case of differential complex processing

TF1

miRNAceRNA ceRNA2 ceRNA2

TF2

miRNAceRNA

ITF - ImiRNAImiRNA ITF

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Dependence on ∆

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Dependence on ∆

∆σm2

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Conclusions1. miRNA-mediated ceRNA effect may act as a master regulator of gene expression in the presence of the heterogeneity in target binding affinities, that is the case “in vivo” (Breda et al, 2015 [15]).

miR

NA

The density of target sites

energy of interaction between the miRNA and the target

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Conclusions2. Target derepression may be significant even if the competitor is in low copy numbers, provided a certain heterogeneity in kinetic parameters (e.g. for a catalytically degraded target and a stoichiometrically degraded competitor) is present.

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Thank you!

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References

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[4] Mello CC, Darryl C Jr. Revealing the world of RNA interference. Nature 2004; 431(7006): 338–342.

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[15] Breda J, Rzepiela AJ, Gumienny R, van Nimwegen E, Zavolan M. Quantifying the strength of miRNA-target interactions. Methods 2015; 85(1): 90–99.