Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
Statistical Physics Approach to Post-Transcriptional Regulation
Candidate: Araks Martirosyan
Advisors: Andrea De Martino, Enzo Marinari
Collaborator: Matteo Figliuzzi
Rome, 2015
8
ncRNA
RNA mRNA
ncRNA Regulatory RNA
Constituent RNA
tRNArRNA
...piRNA
siRNA
…
TranscriptionTranslation
DNA
Protein
miRNA
[3,4]
9
miRNA
RNA mRNA
ncRNA Regulatory RNA
Constituent RNA
tRNArRNA
...piRNA
siRNA
…
TranscriptionTranslation
DNA
Protein
miRNA
[5]
10
miRNA binding
3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR
5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR}
}
seed
canonical sitesmRNA
miRNA28
11
mRNA cleavage
3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR
5’UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3’ UTR
5'UTR AAUCGCGAAGAUCUACUAGAGUAGGUCACCAGGA 3'UTR
cleavage
3’UTR ACGACGUUCUCAUUCAGUGGUU 5’ UTR
12
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
18
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]
19
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.
24
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
25
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
26
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
27
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
28
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
31
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
37
Channels
TF1
miRNAceRNA ceRNA2 ceRNA2
TF2
ITF
miRNAceRNA
TF1 TFμTF2TFμ ImiRNA
miRNA-channel TF-channel
40
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
41
miRNA-mediated regulation may represent the sole control mechanism in case of differential complex processing
TF1
miRNAceRNA ceRNA2 ceRNA2
TF2
miRNAceRNA
ITF - ImiRNAImiRNA ITF
44
The limit of weakly interacting high miRNA population
miRNAceRNA2miRNA
ω
ceRNA2ceRNA2ceRNA2k i+→k i
+ ω ,δ→δω .
46
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
47
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.
49
References
[1] Alberts B, Johnson A, Lewis J, Morgan D, Raff M, Roberts K, Walter P. Molecular Biology of the Cell. Garland Science, 2015.
[2] Cech TR, Steitz JA. The noncoding RNA revolution-trashing old rules to forge new ones. Cell 2014; 157(1): 77–94.
[3] Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE, Mello CC. Potent and spe- cific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998; 391(6669): 806–811.
[4] Mello CC, Darryl C Jr. Revealing the world of RNA interference. Nature 2004; 431(7006): 338–342.
[5] Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116(2): 281–297.
[6] Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden, RNA language? Cell 2011; 146(3): 353–358.
[7] Denzler R, Agarwal V, Stefano J, Bartel DP, Stoffel M. Assessing the ceRNA Hypothesis with Quantitative Measurements of miRNA and Target Abundance. Molecular Cell 2014; 54(5): 766–776.
[8] Bosson AD, Zamudio JR, Sharp PA. Endogenous miRNA and target concentrations determine susceptibility to potential ceRNA competition. Molecular Cell 2015; 56(3): 347–359.
[9] Figliuzzi M, De Martino A, Marinari E. RNA-based regulation: dynamics and response to perturbations of competing RNAs. Biophysical journal 2014; 107(4): 1011–1022.
[10] Bosia C, Pagnani A, Zecchina R. Modelling Competing Endogenous RNA Networks. PLoS ONE 2013; 8(6): e66609.
[11] Alon U. An Introduction to Systems Biology: Design Principles of Biological Cir- cuits. CRC Press; 2006.
[12] Shannon CE. A Mathematical Theory of Communication. The Bell System Technical Journal 1948; 27(3): 379–423.
[13] Tkačik G, Walczak AM, Bialek W. Optimizing information flow in small genetic networks. Physical Review E 2009; 80(3): 031920.
[14] Bialek W. Biophysics: Searching for Principles. Princeton University Press, 2012.
[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.