Title: Nikolaus Rajewsky
1 Metazoan microRNAs
- Nikolaus Rajewsky
- Biology Mathematics
- Center for Comparative Functional Genomics
- Department of Biology, New York University
- Max Delbruck Centrum for Molecular Medicine,
Berlin
2Acknowledgements
Collaborators Markus Stoffel (Rockefeller) Kris
Gunsalus Fabio Piano (NYU) Pino Macino
(Rome) Lior Pachter (Berkeley) T. Tuschl
(Rockefeller) Nick Socci (Sloan-Kettering)
Lab Azra Krek (Dominic Grün) Sabbi Lall Kevin
Chen Marc Friedlander Pranidhi Sood Thadeous
Kacmarczyk Teresa Colombo Yi-Lu Wang D.
Langenberger, R.Wolf, L. Rosenberg, I. da
Piedade
Administration Alexandra Tschernycheff Funding
NIH.
Thanks to The Genomics Initiative and all the
wonderful people at NYU.
3We have heard a lot about a certain class of
trans acting regulatory factors Transcription
Factors
4But they do not tell us anything about what
happens after mRNA synthesis.
5Trans-acting regulatory genes which act on mRNA
stability and mRNA translation RNA binding
proteins and microRNAs and
6Metazoan genomes encode a large class of
non-coding, small RNAs that bind to target mRNAs
to repress mRNA levels/protein production
microRNAs.mature microRNA UCGAGGACUUACGUAAGUC
7 hairy 3 UTR
mir-7
xxxxx
Rajewsky and Socci 2004
8But what is their biological function?
- A happy case where experiments and
- computation are arguably equally
- important..
9My two lectures
- background in mRNAs and history of small RNAs
- microRNA biology and function
- microRNA gene finding
- microRNA target finding and experimental
validation - microRNA knockdown techniques
- insights into microRNA function from
characterization of targets - population genetics applied to human gentotyped
SNPs to get - insights into function and evolution of
cis-regulatory sites - microRNA Literature 2002 5 papers in
pubmed - 2005 100
papers - 2006 more
than I can read - Reviews Bartel Cell 2004, Ambros Nature 2004,
Rajewsky Nature Genetics 2006
10Abundance of RNAs in cells
Hershey et al, 40 years ago
11Ancient RNA
- RNA may be older than DNA (RNA world) dark
matter (?)
12Pervasiveness of RNA genesis
- 50 of the human genome is transcribed
- (Affymetrix Science 2004)
- 90 of the yeast genome is transcribed (W. Huber,
personal communication)
13Some basic facts about mRNAs
14CAP structure at 5 end of mRNAs m7G
7-methylguanylate, p phosphate group, N any
base Poly A Tail at 3 end of mRNAs
homopolymeric stretch of 25-200 adenine
nucleotides Secondary structures (hairpins) can
block translation. Internal Ribosomal Entry Sites
can induce CAP independent transl initiation.
upstream Open Reading Frames normally function as
negative regulators. In green binding sites for
proteins/RNAs There are thousands of human genes
with RNA binding domains!
15Evolution and Complexity of UTR lengths
- Compare C. elegans, fruitfly, humans 5 UTR
length roughly constant - But their average 3 UTR length is (in nt)
- 250, 500, 1000
- TFs have often very long and complicated 3
UTRs. - Housekeeping genes 3 UTRs typically short.
- Note Human 50-70 of all genes have at
least 2 different 3 UTR - isoforms, typically not annotated in current
databases -
163UTR length of highly specifically expressed
mRNAs is tissue specific
Average 3 UTR length
17- Eukaryotic mRNAs are thought to be
- circularized upon translation PABP binds
- directly poly A, eIF4E binds directly cap,
- hold together by eIF4G
18Circularization of mRNA by translation initiation
factors
From Wells et al, Mol. Cell (1998)
19Translational control by 3 UTRs ?
From Mazumdar et al, Trends in Biochemical
Sciences (2003)
20Some basic facts about RNAsecondary structures
- Basepairing Watson-Crick (AU, GC) but also GU
wobbles. GC free energy 2.5 AU free energy - Popular programs to fold or hybridize RNA MFold
(Zucker), Vienna package, RNAhybrid (Rehmsmeier)
21RNA secondary structure predictions
- Golden rule Never trust a computationally
predicted - secondary structure of a sequence gt300 basepairs.
- Terminology
- Hairpin loop
- Internal loop
- Bulge loop
- Multibranched loop
- Stem
- Pseudoknot
22Nussinov algorithm
- Simplest approach find the configuration with
the greatest number of paired bases - Impossible to score all possible structures
- Dynamic programming (Ruth Nussinov et al 1978)
look at all ways to extend an optimal
substructure.
23- Note
- (a) Hairpin loops are treated as if they could
have any length. In reality, RNA need to have a
minimum of 3 nucleotides to form a hairpin - (b) Any number of bulges/internal loops/stems of
any size are possible (for example a stem of
length 1- but nature demands a minimal stem
length) - (c ) no parameters for loops, stacking energies,
no temperature, - A better algorithm MFOLD (M. Zucker, see his
- review in Nucleic Acids Res. 2003)
24An excerpt out of the 110 pages of MFOLD
parameters
Y Y
Y Y Y
Y ------------------
------------------ ------------------
------------------ ------------------
------------------ A C G U A
C G U A C G U A
C G U A C G U A
C G U ------------------
------------------ ------------------
------------------ ------------------
------------------ 5' --gt 3'
5' --gt 3' 5' --gt 3'
5' --gt 3' 5' --gt 3'
5' --gt 3' X X
X X
X X
A A A C
A G A U
A G A U U U
U G U C
U A U U
U G YA
YA YA
YA YA
YA 3' lt-- 5' 3' lt-- 5'
3' lt-- 5' 3' lt-- 5'
3' lt-- 5' 3' lt-- 5' 3.90
3.70 3.10 5.50 3.20 3.00 2.40 4.80 3.20
3.00 2.40 4.80 3.90 3.70 3.10 5.50 3.90
3.70 3.10 5.50 3.90 3.70 3.10 5.50 3.80
3.70 5.50 3.70 3.10 3.00 4.80 3.00 3.10
3.00 4.80 3.00 3.80 3.70 5.50 3.70 3.80
3.70 5.50 3.70 3.80 3.70 5.50 3.70 3.20
5.50 2.30 5.50 2.50 4.80 1.60 4.80 2.50
4.80 1.60 4.80 3.20 5.50 2.30 5.50 3.20
5.50 2.30 5.50 3.20 5.50 2.30 5.50 5.50
5.50 5.50 5.50 4.80 4.80 4.80 4.80 4.80
4.80 4.80 4.80 5.50 5.50 5.50 5.50 5.50
5.50 5.50 5.50 5.50 5.50 5.50 5.50
Y Y
Y Y
Y Y ------------------
------------------ ------------------
------------------ ------------------
------------------ A C G U A
C G U A C G U A
C G U A C G U A
C G U ------------------
------------------ ------------------
------------------ ------------------
------------------ 5' --gt 3'
5' --gt 3' 5' --gt 3'
5' --gt 3' 5' --gt 3'
5' --gt 3' X X
X X
X X
A A A C
A G A U
A G A U U U
U G U C
U A U U
U G YC
YC YC
YC YC
YC 3' lt-- 5' 3' lt-- 5'
3' lt-- 5' 3' lt-- 5'
3' lt-- 5' 3' lt-- 5' 3.60
3.20 3.10 5.50 2.90 2.50 2.40 4.80 2.90
2.50 2.40 4.80 3.60 3.20 3.10 5.50 3.60
3.20 3.10 5.50 3.60 3.20 3.10 5.50 3.70
4.00 5.50 3.70 3.00 3.30 4.80 3.00 3.00
3.30 4.80 3.00 3.70 4.00 5.50 3.70 3.70
4.00 5.50 3.70 3.70 4.00 5.50 3.70 5.50
5.50 5.50 5.50 4.80 4.80 4.80 4.80 4.80
4.80 4.80 4.80 5.50 5.50 5.50 5.50 5.50
5.50 5.50 5.50 5.50 5.50 5.50 5.50 3.70
5.50 2.80 4.80 3.00 4.80 2.10 4.80 3.00
4.80 2.10 5.50 3.70 5.50 2.80 5.50 3.70
5.50 2.80 5.50 3.70 5.50 2.80
25- mfold (m multiple) server www.bioinfo.rpi.edu
/applications/mfold - Original version (Zucker Science 1989)
- Computes minimum free energy and optimal fold,
suboptimal folds, melting temperatures - Handles temperature, and has a set of ionic
conditions - Can also hybridize 2 strands of nucleic acids
- Pseudoknots are not allowed
- Uses dynamic programming. Still written in
fortran (!) - Although Mfold uses a plethora of experimental
binding parameters, many parameters are guessed
by fitting secondary structures deduced from
phylogenetic evidence - Varying the mfold parameters in their error range
significantly changes mfold output(R. Bundschuh,
unpublished)
26- Note the availability of 17 vertebrate genomes,
12 fly genomes, etc gives very interesting
opportunities to detect conserved RNA secondary
structure (check Sean Eddys lab) - Tools are being developed to compare RNA
secondary structure (check Giegerich lab), for
example to identify structural motifs
27History of regulatory RNAs
- 1960s Jacob Monod
- Idea intermol. RNA-RNA interactions involved in
gene regulation Britten Davidson 1969 - Small translation control RNAs bind to inactive
mRNA Heywood and Kennedy 1976 - lin-4 supresses lin-14 translation phenotypes,
expression, first target sites in 3 UTRs
Ambros, Lee, Feinbaum, Ruvkun, etc early 90s - First big surprise microRNAs are abundant
Tuschl, Bartel, Ambros labs early 2000 - Second big surprise 2005 microRNA are likely to
regulate at least 30 of all human genes (three
groups Bartel, Lander, Rajewsky)
28The classic phenotype heterochronic microRNAs in
C. elegans
In-vivo knockdown of microRNAs by
anti-oligonucleotides
Let-7 knock down phenotype (injection)
Let-7 knock down phenotype (soaking)
29microRNAs
- Large class of regulatory
- genes (gt200 in humans)
- Initial phenotypic,
- expression, and
- evolutionary analysis
- suggest important function
- Number of microRNAs
- with known function small
Fig. from Bartel CELL 2004
30microRNAs interesting for computational people
because
- likely to regulate the posttranscriptional
expression of at least 30 of all (human) genes. - microRNA gene finding and microRNA target finding
are at least in part computable. - A rare case where we can computationally look at
evolution of cis-regulatory sites and
trans-acting factors.
Note Many other RNAs siRNAs, rasis, sncRNAs,
tcRNAs, .
31microRNA genomic organization
- Typically transcribed by pol II
- Can reside in introns, can come in operons
- Many seem to have regular promoters
- Transcription start easily hundreds of bases
upstream of the hairpin - Many come in several slightly different copies
32Processing transcripts the making of mature
microRNAs
Note mature microRNAs presumably freely diffuse
between cytoplasm/nucleus.
33 hairy 3 UTR
mir-7
xxxxx
Rajewsky and Socci Dev. Biol. 2004
34Mechanistic function of mature microRNAs
From Bartel, CELL 2004
(already outdated).
35(No Transcript)
36microRNA expression detection
- Cloning and sequencing 454 sequencing
- Northerns
- In-situs (Exiqon)
- Microarrays (bead based oligo based)
- PCR (Applied Biosystems)
- Reporter gene assays
- By bioinformatic analysis of microarray data for
regular genes - (later)
37microRNA microarrays
Baskerville and Bartel, RNA 2005
38- Most miRNA genes within 50 kb of each other have
highly correlated expression patterns, consistent
with the idea that they are processed from
polycistronic primary transcripts.
39Classifying cancer (Golub lab, Nature 2005)
40microRNA in situs (Exiqon)
Plasterk group, Science 2005
41Reporter gene activity driven by microRNA
upstream sequences( see also in situs by Eric
Lai PNAS 2005 )
miR-7 reporter
miR-1 reporter
42- A puzzle microRNAs seemingly not needed and not
expressed during vertebrate body patterning, but
microRNAs frequently and specifically expressed
during fly body patterning.
Giraldez et al Science 2005 Eric Lai lab PNAS
2005
43Known microRNA functions
44microRNA gene finding
- Experimental cloning, sequencing (Tuschl/Bartel
labs), bioinformatics, Northerns. - no false positives, many false negatives.
Also implicit expression analysis. - Computational
- miRscan (Burge lab), miRseeker (Rubin lab,
fly) score conservation structure of hairpins. -
- 50 false positives, less false negatives,
methods rely on training data. - New methods (machine learning) Mihaela Zavolan
(Biozentrum Basel, - Nature methods 2005) Rosetta Informatics
(Nature Genetics 2005), - do not rely on cross-species conservation.
45Vertebrate microRNA precursor evolution
46Vertebrate mature microRNA evolution
47miRscan
From Lim et al Genes Development 2003
48Zavolan lab (from Pfeffer et al , Nature Methods
2005 viral microRNAs) No cross-species
comparisons. 71 true positives, 3 false
positives.
49- Rosetta Informatics (Nature genetics 2005)
roughly 800 human microRNAs, many of them primate
specific (?)
50 Metazoan microRNA targets
- Nikolaus Rajewsky
- Biology Mathematics
- Center for Comparative Functional Genomics
- Department of Biology, New York University
- Max Delbruck Centrum for Molecular Medicine,
Berlin
Review/perspective Rajewsky, Nature Genetics 2006
51microRNAs
- Large class of regulatory
- genes (gt200 in humans)
- Initial phenotypic,
- expression, and
- evolutionary analysis
- suggest important function
- Number of microRNAs
- with known function small
Fig. from Bartel CELL 2004
52- animal microRNAs bind target mRNAs and
- can degrade mRNAs/inhibit translation.
- to understand function of microRNAs
- find and characterize their targets.
- Note also a beautiful system to computationally
- study function and evolution of trans-acting
factors - AND cis-regulatory sites.
53Known microRNA functions
54mature microRNA
55 hairy 3 UTR
mir-7
From Rajewsky and Socci, 2004
xxxxx
56A Meeting at the New York Academy of Sciences
2005
The Rules
57The participants
Algorithm (alphabetical order)
Representative Speaker
Hatzigeorgiou Sander Rajewsky Rigoutsos
Rehmsmeier Bartel
- DIANA_MicroT.1
- miRanda-2.0
- PicTar1.0
- rna22
- RNAhybrid_helix_2_7TE
- targetscanS
58(We removed all identifiers not starting with NM)
59The first round of target predictions (2003/2004)
- Brennecke et al (Cohen lab)
- Enright et al (Sander/Marks labs)
- Kirikadiou et al (Hatzigeorgiou lab)
- Lewis et al (Bartel/Burge labs)
- Rajewsky and Socci
Important Eric Lai Nature Genetics 2002
60miRanda
- Enright et al, Genome Biology 2003
- Phase 1 Smith-Waterman type.
- Complementarity parameters 5 for GC, 5 for
AU, 2 for GU and -3 for others. Affine gap model
in addition scaling factor of 2.0 for first 11
positions. Finally no mismatches at positions
2-4 fewer than 5 mismatches at 3-12 at least 1
mismatch between 9-(L-5) fewer than 2 mismatches
in the last five positions of the alignment. - Phase 2 free energy (Vienna) minimium -14
kcal/mol. - Phase 3 conservation 80 identity
- No notion of significance. No experimental
validation.
61TargetScan, Embl algorithm (2003)Lewis et al
Cell 2003. Brennecke et al 2003.
- Perfect W/C complementarity in the 5 end of the
microRNA (exception Brennecke et al), free energy
cutoff of the entire site - Both have experimental validation (Brennecke in
vivo, Lewis et al in cell lines) - Lewis et al introduce the concept of shuffled
microRNAs as a control
62 Rajewsky and Socci
- Training set 25 known target sites in C. elegans
- Screened a number of alignment algorithms to best
recover the sites versus random sites - Discovery of a nucleus of consecutive W/C
basepairings, length 6-8 -
- Remaining microRNA bases partially basepaired in
predicted in silico secondary structure of
miRNAmRNA duplex
63Nucleus based approach specifically recovers
known target sites
counts
nucleus score
64Model for target site recognition
- kinetic step the nucleus (consecutive W/C
basepairings) allows rapid zip up of
microRNA/target to overcome thermal diffusion - thermodynamics further basepairings stabilize
the duplex - also free energy is a bad predictor for finding
targets - nucleus often in 5 end of microRNA
- ran algorithm on fly genes and predicted
conserved microRNA targets.
Rajewsky and Socci 2004
65Next generation of target finding algorithms
- TargetscanS (Lewis et al 2005)
- PicTar (Krek et al 2005)
- http//pictar.bio.nyu.edu
- Essentially much more power due to massive
- cross-species comparisons.
- Both algorithms similar. PicTar is a HMM and
- can naturally score combinations of sites also
- detects imperfect nuclei sites.
66Single target site recognition 2005
- A stretch of perfect consecutive W-C basepairings
between miRNAmRNA, termed nucleus is crucial to
target recognition - Nuclei are 7 bps long and start at the 1st or 2nd
position from the 5 end of the microRNA - Imperfect nuclei are allowed if compensated
(therefore, at least 2 - classes of target sites class 1 seems much
larger) - However, this model still cannot explain
specificity of target - recognition too many false positives. Need
cross-species - comparisons.
67Stark et al, PLoS Biology 2005
68PicTar algorithm
- PicTar
- Probabilistic
- Identification of
- Combinatorial
- Target sites
- http//pictar.bio.nyu.edu
69Using genome wide alignments of 8 vertebrate
genomes (UCSC database)
Multiple alignments of 3 UTRs (Number of
nucleotides in megabases)
- 90 of all 20.255 human RefSeq transcripts
are aligned between - human/chimp/mouse/rat/dog. Low error rate
(human/mouse 5). - 20 of all 20.255 human RefSeq transcripts
aligned between all 8 species
70Identification of anchor sites
12345678------------
21 microRNA 5 TGATACAggcgtaggttaaac 3
microRNA 5 tGATACAGgcgtaggttaaac 3
- hs AAAAAATTATT----GTATCATTTTAA
- pt AATAAATTATT----GTATCATTATGA
- mm ------TTACT----GTATCTGAAAAA
- rn ------TTATT----GTATCA---AAA
- cf -----ATAATT----GTATCA---AAA
- gg -----ATAATTTTGCCCCTCAGTTAAA
71- Controls randomize bases 1-8 of each microRNA
- Pick 8mers such that abundance of 7mer 1-7, and
2-8 is comparable in abundance to real microRNA
(lt15) - Experimented with many other ways of randomizing,
with comparable results
72Predicting single microRNA target sites
(anchors)
Total number of conserved target sites for real
and randomized microRNAs
1.8
2.3
3.3
4.0
73Experimental validation of 7/13 predictions (not
picked by score!)
74- Thus, thousands of genes are predicted to be
targeted by microRNAs. - Some microRNAs target hundreds of genes.
75T is a tiling of sequence S with binding sites w
Probability for this tiling is
Likelihood to observe S given pwi
Z is optimized using standard minimization
procedures. PicTar score -log(Z/Zbackground).
76Combinatorial scoring across species
- hs -AAAAAATTATT----GTATCATTTTAACCCAAAAATTA
TCAAACGATTTTA - pt -AATAAATTATT----GTATCATTATAACCCATAAATTA
TCAAACGATTATG - mm ------ATTACT----GTATCTTATAACA-------TTA
TTAACCCAGAAAA - rn -------TTATT----GTATCA--TAACA-------TTA
TTAACCCA---AA - cf ------ATAATT----GTATCA--TAACA------ATAA
TTAACCCA---AA - gg ------ATAATTTTGCCCCAATTTTAAAC------ATAA
TTAACCCACAAAA - Score phylo average (scores) primates
rodents dog
77An early application
- -- the novel microRNA miR-375 is specifically
- expressed in pancreatic islet cells
-
- -- miR-375 down-regulates insulin secretion.
- Targets??
-
(Poy et al Nature 2004)
78Testing target predictions with Westerns in cell
lines
79- Further experimental results in pancreatic beta
cell lines - Knock-down of miR-375 increases insulin secretion
- Overexpression of miR-375 reduces insulin
secretion - Knock-down of myotrophin reduces insulin
secretion - Point mutation of the putative target site in
myotrophin abolishes knock-down effect of miR-375
80Krek et al. 2005 Grun et al. 2005 Lall et al. 2006
81Combinatorial control by microRNAs?
- Synergism between target sites for single
microRNAs has been experimentally shown - Some experimental evidence for coordinate control
by different miRNAs targeting the same mRNA (Krek
et al, 2005) - and
82Figure 1. GY-boxes (green circles), Brd-boxes
(blue squares), and K-boxes (red triangles) in
the 3'-UTRs of Notch target genes of the
Brd-Complex and the E(spl)-Complex
Eric C. Lai et al. Genes Dev. 2005 19 1067-1080
83Promoter/enhancer
Gene
DNA
Transcription
cis-elements
3 UTR
a
mRNA
Nnnnnn
Translation
miRNAs, RBPs
Transcription factors
Protein
Cell-type specific TF and miRNAs modulates
cellular gene expression
Cell type 2
Cell type 1
TF code
TF code
Gene Battery 2
Gene Battery 1
miRNA code
miRNA code
(from Hobert 2004, modified)
84PicTar predictions and experimental validation in
nematodes
Lall et al Curr Biology 2006
C. elegans C.
briggsae C. remanei
85- results
- improved algorithm
- at least 10 of all elegans genes are
- regulated by microRNAs
- new experimental in vivo platform for target
- testing 2 out of 3 new let-7 predictions
- validated by site mutagenesis
- system for embedding microRNA targets
- into regulatory networks
86N-Browse Integration of target predictions with
C. elegans functional genomics data
87Experimental system for testing 3 UTR mediated
gene regulation in vivo
88Time
Time
let-7
let-7
89Advantages of this in vivo system
- assays 3 UTR mediated, endogenous target
- expression within endogenous miRNA
- expression
- promoterome already available, 3 UTRome
- possible
- cloning and transgenics scalable
90Body Wall Muscle
Somatic Gonad
Hypodermis
Vulval Muscle
Ant. Neurons
Seam Cells
Germ Line
Intestine
VNC
T14B1.1 3UTR
91Body Wall Muscle
Somatic Gonad
Hypodermis
Vulval Muscle
Ant. Neurons
Seam Cells
Germ Line
Intestine
VNC
T14B1.1 3UTR
unc-54 3UTR
92Expression of predicted let-7 regulated 3UTRs
relative to unc-54 3UTR
93(incomplete) summary of experiments
- most of the 12 tested let-7 targets show 3
UTR dependent - expression
- 4 of these show temporal expression
consistent with - textbook let-7 expression
- 2/3 targets validated by site mutagenesis
94Broad rewiring of posttranscriptional control by
microRNAs during metazoan evolution
Grun et al PLoS Comp Bio 2005 Chen Rajewsky,
submitted
95 All 16,000
heptamers microRNA
families
Significance of
conservation across flies
Significance of conservation
across mammals
96- many microRNAs and recognition motifs deeply
conserved - but regulatory relationships only weakly
conserved (for example 100 between flies and
vertebrates). - lots of microRNA target rewiring during
organismal evolution - microRNAs maybe part of genes that drive the
diversity of life (?)
97Knockdown of mouse miR-122 in vivo
- experiment
- standard affymetrix analysis before and after
- knockdown with antagomirs (3 animals each)
- result
- mRNAs for roughly 300 genes go up, 300 down.
- Krutzfeldt et al, Nature 2005
98miR-122 nucleus abundance
99Significance of enrichment of miR-122 nuclei
compare no change UTRs to upregulated UTRs.
actcca
cactcc
- log ( p value )
100The number of miR-122 nuclei correlates with
log(foldchange) cooperativity (?)
genes sorted
by foldchange
101Significance of depletion of miR-122 nuclei
compare downregulated genes versus no change
genes
-log(pvalue)
- log ( p value )
102miR-122 function?
- Analyzed all up regulated genes (target set)
No significant - over-representation of functional categories.
-
- Analyzed all down regulated (anti-targets)
genes -
- Top scoring GO category (plt10-11 ) cholesterol
biosynthesis. - Experiments cholesterol levels are indeed
down by 40. -
103 1. miR-122 regulates cholesterol levels2.
genes activated by miR-122 execute regulation
of cholesterol levels 3. these genes are
depleted in miR-122 recognition motifs4.
many likely targets (although coserved!)
currently not predicted because sites present
but not aligned in orthologous 3 UTRs.
104- Cell type specific signatures of microRNAs on
steady state mRNA levels
Sood et al PNAS 2006
105(No Transcript)
106(No Transcript)
107- tissue specific microRNAs leave broad signatures
on steady state mRNA levels of hundreds of genes - can predict where some specific microRNAs are
expressed - microRNAs likely to be involved in tissue identity
108Detecting functional 3 UTR motifs by
correlatingmotifs with mRNA changes in
microarray experiments
-
- Fit all 3 UTR motifs in all 3 UTRs
simultaneously to all mRNA - expression data (linear regression model,
REDUCE Bussemaker et - al 2001)
- Both absence and presence of each motif modeled
- No cross-species comparisons necessary
- The model explains 50-70 of of the expression
data and predicts - direct targets as well as functional 3 UTR
motifs (and thus active - microRNAs)
-
Sood et al, 2006
109(No Transcript)
110Acknowledgements
Collaborators Markus Stoffel (Rockefeller) Kris
Gunsalus Fabio Piano (NYU) Pino Macino
(Rome) Lior Pachter (Berkeley) T. Tuschl
(Rockefeller) Nick Socci (Sloan-Kettering)
Lab Azra Krek (Dominic Grün) Sabbi Lall Kevin
Chen Marc Friedlander Pranidhi Sood Thadeous
Kacmarczyk Teresa Colombo Yi-Lu Wang D.
Langenberger, R.Wolf, L. Rosenberg, I. da
Piedade
Administration Alexandra Tschernycheff Funding
NIH.
Thanks to The Genomics Initiative and all the
wonderful people at NYU.
111Lineage specific target predictions
7,200 conserved sites in all 9 flies
2,392 sites only in Sophophora
3,446 sites only in Drosophila
Phylo-Pictar, unpublished
112micro-evolution primate evolution (unpublished)
25 MYA
6 MYA
0.2 MYA
113- This part of the talk will be re-posted as soon
as the paper is (hopefully) out
114Acknowledgements 2
Kevin Chen
Thanks to M. Zavolan (Biozentrum Basel), R.
Borowsky (NYU), E. Halperin (Berkeley), P.
Andolfatto (UCSD), M. Siegal (NYU), R. Nielsen
(Copenhagen)