CSLS Retreat 2007
Matan Hofree & Assaf Weiner
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Outline
A brief introduction to microRNA
Project motivation and goal
Selecting the data sets
Features and data analysis
Results and conclusions
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What are microRNAs?
Non-coding RNAs that regulate gene expression post-transcriptionaly
22~ nucleotide long
Evolutionary conserved
Located in intergenic regions or introns
Often specific to a particular tissue or a developmentalstage
Human genomes are predicted to encode as many as1000 unique miRs, that together possibly regulate onethird of all genes1
1 Bartel et. al. Cell (2005)
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Possible mechanisms of miR-mediatedinhibition of gene expression
Two Types of mechanisms have been observed:
1.Direct effect on translation - Inhibition of initiation,elongation or termination.
2.Indirect effects – Affects the number of availabletranscripts by degradation or sequestration.
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Timothy W. Nilsen (2007) Mechanisms of microRNA-mediated gene regulation in animal cells, Trends in Gen. 23, 5,243-249.
miR-target interaction principles
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Target site in mRNA 3’UTR
Incomplete base pairing
G:U base-pairing
7 fully complementary bases in5’ of miRNA – ‘Seed’
He-L& Hannon-GJ (2004)
miR-target interaction principles
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Evolutionary conservation of target site
Repeating binding site in many of the known targetgenes
Reinhart-BJ et. al. (2000) Nature 403: 901
Motivation for our work
“Perfect seed pairing is not generally a reliable predictor formiRNA-target interactions” Didiano D. & Hobert O. (2006) Nat. Struct. Mol.
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Experiment
(modification in target)
Functional
Non-functional
wt target site(control)
13 putative targets 3’ UTR withseed match and phylogeneticconservation
Binding site in random 3’UTR
Original target site in other miRregulated 3’UTR
Motivation for our work
Conclusions from Hobert’s work:
The presence of a well matched miR target site in 3’UTR alone, is not a reliable predictor for a miR-target functional interaction.
Proposed miR-target interactions possibly occur invery specific contexts.
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The seed is not enough…
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Discerning the true targets
Possible factors contributing to miR-targetfunctionality:
1.Unique miR-target duplex secondary structure
2.3’ UTR accessibility and target site accessibility
3.RNA-binding protein cofactors
4.Sequence and structural motifs
5.Presence or absence of neighboring binding sites ofthe same or different miRs
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The long and tedious tale of preparingthe data sets
... AATTCACATTCCGACTTT ...
... GGTAGACATTCCCTTAGA ...
... GGGCTACATTCCTTACGT ...
... CCGATACATTCCTACGGA ...
... AGATTACATTCCTGACCT ...
...
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Functional miR-target interaction
Non-functional miR-target interaction
... GCCTCACATTCCCCTTGT ...
... CAGTGACATTCCCAGAGA ...
... CATTCACATTCCTCAGTT ...
... TGTCTACATTCCTTATCA ...
... GACCTACATTCCTCATTG ...
...
The long and tedious tale of preparingthe data sets
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Over expression of miR
miR silencing (antisense)
treated wt
wt treated
Search for down-regulated genes
Search for down-regulated genes
Search for up-regulated genes
Search for up-regulated genes
The long and tedious tale of preparingthe data sets
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Over expression of miR
miR silencing (antisense)
treated wt
wt treated
Search for down-regulated genes
Search for down-regulated genes
Search for up-regulated genes
Search for up-regulated genes
Lim et al. (2005) overexpression of miR-1, miR-124 and miR-373 in humanHela cells
Esau et al. (2006) &Krutzfeldt et al. (2005)silenced miR-122 in mouseliver cells
Lim et al. (2005)
Fold change downregulation >= 1.4
Expression change P-Val <0.001
Get equally expressed genes( P-Val > 0.85)
Expression above median
Checked fold change ~1
miR-regulated group
miR-non-regulated group
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•Screen out detection anomalies as defined by array
•Consistency between 12H and 24H sets
•Full 2-8(7 nt) seed match
•Screen out detection anomalies as defined by array
•Consistency between 12H and 24H sets
•Full 2-8(7 nt) seed match
Lim et al. (2005)
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* Taking into account overlaps between groups
Results so far…
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CLDN12 AAGAAAACTTCTTGTAGCCTCACATTCCCCTTGTGCAAAGAGCTC
TAGLN2 TAGATATATATTTTAGCAGTGACATTCCCAGAGAGCCCCAGAGCT
ARCN1 GCCACAGTCTGTAATCCATTCACATTCCTCAGTTTCACCACCTCC
TIP120A TTTCATTCCGTTTGGATGTCTACATTCCTTATCAAAGGATATAAA
PGM2 TTATGTGTTTTACAAAGACCTACATTCCTCATTGTTTCATGTTTG
...
Target conservation
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(a)Targets alignment around seed
Target conservation
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CLDN12 C A C A T T C C C
0.5 0.9 0.6 0.8 0.2 0.4 0.6 0.8 0.8
(a)Targets alignment around seed
(b)Extracting conservation values for each target (UCSC hg17)
CLDN12 C A C A T T C C C
0.5 0.9 0.8 0.4 0.6 0.6 0.6 0.8 0.8
TAGLN2 G A C A T T C C C
0.5
ARCN1 C A C A T T C C T
0.5
TIP120A T A C A T T C C T
0.6
PGM2 T A C A T T C C T
0.7
========================================================
Conservation: 0.5
Target conservation
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(a)Targets alignment around seed
(b)Extracting conservation values for each target (UCSC hg17)
(c)Selecting the median value for each position
Target conservation
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Corrected α = 0.0005
Target conservation
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Corrected α = 0.0005
Target conservation
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Corrected α = 0.0005
Seed repeats in 3’ UTR
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The positive group hasstatistically significantly moresequences with repeating seeds(p=0.00027)
Neighboring seeds density
CLDN12 AAGAAAACTTCTTGTAGCCTCACATTCCCCTTGTGCAAAGAGCTC
TAGLN2 TAGATATATATTTTAGCAGTGACATTCCCAGAGAGCCCCAGAGCT
ARCN1 GCCACAGTCTGTAATCCATTCACATTCCTCAGTTTCACCACCTCC
TIP120A TTTCATTCCGTTTGGATGTCTACATTCCTTATCAAAGGATATAAA
PGM2 TTATGTGTTTTACAAAGACCTACATTCCTCATTGTTTCATGTTTG
-20 -10 0 10 20
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hsa-miR-302b
hsa-miR-507
0.2
0.2
0
0.1
0.1
Seed density ofwindow
Seeds density = mean of targets density
0.12
Neighboring seeds density
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Corrected α=0.00125
Overlapping seeds (miR bindingsites) may interfere with miRfunction
Neighboring seeds contribute tofunctionality
Possibly favored periodicaldistances between seeds ?
miR-mRNA binding pattern
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We need a way to determine what is the preferablesecondary structure of the miR-target complex
RNAcofold from the Vienna package
target 5' UUGGCUCACUUGCCUUAGTCATCGCTA 3'
miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5'
target 5' UUGGUCAUUUUUUUUAUAUUGCCUUAU 3'
miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5'
target 5' AUCAUAUUAAUUGUGCCUUAUAGTAAT 3'
miRNA 3' ACCGUAAGUGGCGCACGGAAUU 5'
(a) Using Vienna package RNAcofold we estimated theinteraction between the miR and putative targets
target 5' U G 3'
UGGC UCACU UGCCUUA
ACCG AGUGG ACGGAAU
miRNA 3‘ UA CGC U 5‘
target 5' U U UUUUUAUAU U 3‘
UGG CAUUU UGCCUUA
ACC GUAAG ACGGAAU
miRNA 3‘ UGGCGC U 5‘
target 5' A AUAUUAAU U 3‘
UCA UGUGCCUUA
AGU GCACGGAAU
miRNA 3' ACCGUA GGC U 5'
miR-mRNA binding pattern
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Bound
Notbound
target 5' U G 3'
UGGC UCACU UGCCUUA
ACCG AGUGG ACGGAAU
miRNA 3‘ UA CGC U 5‘
target 5' U U UUUUUAUAU U 3‘
UGG CAUUU UGCCUUA
ACC GUAAG ACGGAAU
miRNA 3‘ UGGCGC U 5‘
target 5' A AUAUUAAU U 3‘
UCA UGUGCCUUA
AGU GCACGGAAU
miRNA 3' ACCGUA GGC U 5'
(b) For each miR position we calculated the fraction of times theposition was found bound in each set.
miR-mRNA binding pattern
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2/3
3/3
miR-mRNA binding pattern
miR-1
miR-124
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Corrected p-value α= 0.0025
Corrected p-value α= 0.0025
miR-mRNA binding pattern
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miR-target bound positions
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miR-target bound positions
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•Seed extension contributes tofunctionality
• miR-373 - another example ofdifference in its operation mechanism
•Seed extension contributes tofunctionality
• miR-373 - another example ofdifference in its operation mechanism
Target accessibility
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Corrected α = 0.0003
Target accessibility
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Corrected α = 0.0003
Conclusions
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miRspecificconclusions
Different binding patterns characterizeeach miR
miR-1 position 13
miR-373 low seed affinity
The function of certain miRs is perhapsmore dependent on multiple bindingsites (miR-373).
Conclusions
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Generalizingproperties
Positive group targets show evolutionaryconservation
Positive group targets are moreaccessible
Positive group is enriched in repeatingbinding sites of the same miR
Indication for miR cooperation orinterference (periodic binding sites?)
What’s next?
Apply all the tests on additional data sets
Redefine the data sets with imperfect seeds
Further search for structural motifs enriched in ourpositive set
Explore 5’ UTR of the sets we defined for sequence orstructural motifs
Suggest a biological model to mechanism for miR-targetspecificity
Possibly try to generalize the features we’ve found inan SVM based classification program
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Acknowledgements
Dr. Yael Altuvia
Prof. Hanah Margalit
All the wonderful people in Hanah’s lab.
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