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Regulation of transcript stability and post-transcriptional
processes
– from yeast to human
Reut ShalgiWeizmann Institute of Science, Israel
RSMD workshopUppsala11/2006
The central Dogma
Transcription
mRNA
Translation
ProteinDNA
The central Dogma
Transcription
mRNA
Translation
ProteinDNA
Transcrip
tion
miRNA (ncRNAs)
De
gra
da
tion
De
gra
da
tion
Post transcriptional control
• Functional sequence motifs in 3’ UTRs stability associated motifs
(Shalgi et al. Genome Biology 2005)
• miRNA regulation (Xi et al. Clin Cancer Res. 2006)
Transcription Translation
ProteinDNA
Transcrip
tion
miRNA
Deg
rada
tion
Deg
radatio
n
mRNA
A catalog of stability-associated sequence
elements in 3' UTRs ofyeast mRNAs
Shalgi R, Lapidot M, Shamir R and Pilpel Y. Genome Biology 2005
The cell transcriptome
gene expression profile
AAATCGGAATTGGAGGTATCGGATCTTGTTGAATATCCACCAATGTCTTACCCCTGTATTTTA…
promoter 5’ UTR Protein coding region 3’ UTR
Balance between transcription activation and transcript degradation
AAAAAAAAAAA…TGTATAAT
time
Expre
ssio
n level
mRNA transcription and degradation –
both determine the cell transcriptome
genes
conditionsPromoter sequence
AAATCGGAATTGGAGGTATCGGATCTTGTTGAATATCCACCAATGTCTTACCCCTGTATTTTAACAAGAGTTTACGGAATACTGTTATATGGTTAAAGGTGTGGACGCCTTGAAGGTTTACCTTACCGAATGACACCTGAATATTACAATAGTCAGATCGAATAACGTTCTGGAATATGGCGTTATCCAAAGTTAGCGCAGTTTTCCGATGGTCCAATGTAATCATTAGAAATAGTAAAAACTGTGTAATGGTAAAGATTGTGTCACTGGAAAAAAACTGCTACAAATAATAAATAAATAAAAAAATACGAAAGCACAGTACTACGGGTGCCTCCACAAATAGATAAGAAACCAAGCGGAGACATGCGTTTAGACTACGGTGAGGATATAAATTATTTATACAACCAGACCTACGGTATATAAAAGAGCATCTAGTTTACCTGTTATGATGAATGGACATTCGCTACATCTACGGATCTTACTCTCTATTTGTTAAAAAAAATTACAAAGAGAACTACTGCATATATAAATAACATACCTACGGAATAACAT
ACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGTATTGGTTGTTGTCTCACTGGTAGCACTGTTCATTTTAGAGCTGACAGACTCTTCATTCGTAGTCTGTGGCCTCCATGTTGGATAGACCGTAACAACATCATTCACAGTAGCCGTGGCCGTCGAAACAATGGCAGGTGAAGCAGTTTCGGAACACACACCAGATTCGCAGGAAGTAACAGTAACTAGCGTAGTTTGTTGCCTCGATTCTGTGGTGGAAATAGGACACCATGTCGTGTATTCTGTGGTAACGCCGTTAATAGTAGCAGTGCTTATAGATACAATGACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGT
3’ UTR sequence
?
Functional sequence motifs in 3’ UTRs
Finding sequence elements associated with transcript stability derived from 3’ UTRs of
yeast mRNAs
3’ UTRs were previously inferred to be involved in controlling:Transcript StabilitySub-cellular localization
(Keursten & Goodwin, Nature Reviews Gen. 2003)
Why 3’ UTRs ?
Discovery of stability-associated motifs in yeast 3’ UTRs
The data
ACCAATCACATCGGTCGCGGAAGCCGTCTGTGTTTCAGCATGATTGAATCTTGAAATTGAAGAGGTGACTACTGTTTTCGTCTCAGCAGCTCCAGTACTGGTAGTTGTCTCAGCAGCTCCAGTATTGGTTGTTGTCTCACTGGTAGCACTGTTCATTTTAGAGCTGACAGACTCTTCATTCGTAGTCTGTGGCCTCCATGTTGGATAGACCGTAACAACATCATTCACAGTAGCCGTGGCCGTCGAAACAATGGCAGGTGAAGCAGTTTCGGAACACACACCAGATTCGCAGGAAGTAACAGTAACTAGCGTAGTTTGTTGCCTCGATTCTGTGGTGGAAATAGGACACCATGTCGTGTATTCTGTGGGGAGTTGTCTCAGCAGCTCCAGT
3’ UTR sequence
the “virtual northern”Data by Hurowitz & Brown, Genome Biol., 2003
Yeast mRNA half lives
(Calculated from mRNA Decay profiles)
Taken from Wang , PNAS 2002
Time (min)
Expre
ssio
n level
Discovery of stability-associated motifs in yeast 3’ UTRs
TCATTGAAAGCTTCCCTTATCCCTTCCA…TCTCCTACAACGCCTGAGGAGGACCAGA…GCACCATCCCTCCTACAACTAACTACCAG…TGAGCTCATTAAGCTTCCCAGCACAACT…
AAGCTTCC
CCTACAAC
1. Exhaustive kmer enumeration (8<=k<=12)
A List of all kmers in the 3’ UTRs for each kmer, a list of the gene that contain it in their 3’ UTR:
AAGCTTCC gene1 AAGCTTCC gene2 AAGCTTCC gene22AAGCTTCC … AAGCTTCC … AAGCTTCC … AAGCTTCC … #CCTACAAC gene5 CCTACAAC gene9 CCTACAAC … CCTACAAC … CCTACAAC … CCTACAAC … #
Functional sequence motifs in 3’ UTRs
Finding sequence elements associated with transcript stability derived from 3’ UTRs of
yeast mRNAs
AAGCTTCCCCTACAAC
Genome average half life = 26 min
Time (min)
Expre
ssio
n level
Average half life = 9 min
Average half life = 38 min
Discovery of stability-associated motifs in yeast 3’ UTRs
2. Kmer Stability p-value calculation: Mean transcript half-life is 26.3 minutes.
Do the genes that contain the kmer in their 3’ UTR have a significantly lower/higher mean half-life?
3. Controlling for multiple hypotheses: using the FDR - False Discovery Rate
A list of significant kmers
Motif mean ½ life #genes p-value
AAGCTTCC
26 min 0.3
CCTACAAC 8 min 10-6
…
20030
AAAAAAAA
46 min 80 10-11
Discovery of stability-associated motifs in yeast 3’ UTRs
4. Creating motifs from kmers by clustering:
AAGGGCTT
AAGGCCTC
AGGGCTT
AAGGGCTC
AAGGGCTAAGGCCTT
AAGGCCT
GGCGCCTT
GCACCTT
GGCGCCT
GGCCCCTT
GGCACCTT
GCCCCTT
TTCCTTCC
TTCCATC
TTCCATCT
TTCCATCC
TTCCTTC
TCCTTCC
A catalog of stability-associated motifs
Motif M1
Mean half life: 16 min. Number of genes: 640 P-value: 1.2*10-50
Motif M11
Mean half life: 46.5 min. Number of genes: 89 P-value: 1*10-300
time
Exp
ressio
n level
A catalog of stability-associated motifs
Motif M1
Mean half life: 16 min. Number of genes: 640 P-value: 1.2*10-50
Motif M11
Mean half life: 46.5 min. Number of genes: 89 P-value: 1*10-300
A catalog of stability-associated motifs
For the first time, a catalog of stability-associated motifs was assembled
53 motifs: 40 de-stabilizing 13 stabilizing
For comparison, the current promoter motif catalog (Harbison et al.) contains 102 motifs.
~1700 genes contain a stability-associated motif
Out of those, 850 contain both a stability motif, and a promoter motif
Many stability motifs are evolutionary conserved
In other yeasts
16 were found to be significantly conserved
S. kudriavzeviiS. kudriavzevii
S. paradoxusS. paradoxus
S. S. cerevisiaecerevisiae
M24220 genes
56
105
47Highly
ConservedP-value=0.009
Evolutionary conservation remains all the way to
human
Comparing to mammalian 3’ UTR motifs(Xie et al. Nature, 2005)
11 were significantly similar to a mammalian conserved motif
YEAST
HUMAN
Functional enrichment
cell growth and/or maintenance7.32*10-5
cell organization and biogenesis 2.52*10-5
protein biosynthesis 4.22*10-5
nucleic acid metabolism 7.27*10-6
transcription from Pol II promoter 6.47*10-4
protein modification 4.69*10-4
Process p-valueM1
M24ribosome biogenesis and assembly 3.87*10-7
rRNA processing 3.88*10-6
protein biosynthesis 3.03*10-5
nucleic acid metabolism 1.42*10-4
RNA processing 1.32*10-6
transcription from Pol I promoter 1.86*10-7
Type Transcript regulation
Number of genes
Enriched biological processes (GO category)
I Transcription initiation level regulation
2297(~35%( transport
II Degradation level regulation
793(~12%( RNA modification, protein modification, nucleic-acid metabolism
III Transcription initiation and degradation level regulation
846(~13%) cell growth and maintenance, cell wall organization and biogenesis, protein biosynthesis
Stability affecting Motifs are Stability affecting Motifs are complementary to promoter complementary to promoter
motifs motifs
Integrating Harbison et al.’s data on promoter motifsThree potential modes of regulation:
stop
stop
stop
M24
Stability affecting Motifs are Stability affecting Motifs are complementary to promoter complementary to promoter
motifsmotifs
M24ribosome biogenesis and assembly 3.87*10-7
rRNA processing 3.88*10-6
protein biosynthesis 3.03*10-5
nucleic acid metabolism 1.42*10-4
RNA processing 1.32*10-6
transcription from Pol I promoter 1.86*10-7
Process p-value
Rap1
2 4 62 4 62 4 62 4 6
Stability affecting Motifs are Stability affecting Motifs are complementary to promoter complementary to promoter
motifsmotifs
-1
0
1
Time points
No
rmal
ized
exp
ress
ion
All protein biosynthesis related genesChecked their steady-state expression profiles in a set of 40
conditions
M24
Rap1
10 82 282 21#genes
√
√
√√X X
X X
3’ UTR motifs associated 3’ UTR motifs associated with sub-cellular with sub-cellular
localizationlocalization
Sub cellular clustering score & p-value Uses GO annotation (cellular component) And a similarity measure by (Lord, Bioinformatics,
2003) The SCC (Sub-Cellular Clustering) The SCC (Sub-Cellular Clustering) score score
Cellular component Cellular componentSCC=0.5 SCC=0.05
mitochondria
Mitochondria inner membrane
3’ UTR motifs associated 3’ UTR motifs associated with sub-cellular with sub-cellular
localizationlocalization
The 3’ UTR yeast motif
SCC score: 0.289SCC p-value: < 10-6
Associated with 610 genesOut of which 260 genes are
known to be Localized to the Mitochondria
Example – a putative mitochondrial zipcodeExample – a putative mitochondrial zipcode
Sub Cellular Clustering score:
endomembrane system
NO83.9E-050.43M13
endoplasmic reticulumYES
(p-val<1E-3)721E-060.11M22
enriched termsconservation#targetsSCC p-value
SCC score
logoname
mitochondrial inner & outer membrane translocase complex
mitochondrial inner & outer membrane
mitochondrial membrane
mitochondrial ribosome
mitochondrial matrix
mitochondrial intermembrane space
mitochondrion
YES(p-val<1E-3)
610<1E-60.289M1
A catalog of 23 motifs A catalog of 23 motifs associated with sub-cellular associated with sub-cellular
localizationlocalization
Very few experimentally verified motifs:
M1: CYC1 (Russo, Mol Cell Biol, 93)
M24: was suggested to be thebinding site for Puf4p (Gerber,
PLOS, 2004)
Foat B, PNAS, Dec. 2005 Mitochondrial Motif:
was suggested to be the binding site for Puf3p (Gerber, PLOS, 2004, Gerber, PNAS, 2006)
the co-translational import model of mitochondrial genes (Kaltimbacher V, RNA, Jul. 2006)
Support from the literature
Summary – part 1
A first large scale catalog of 3’ UTR motifs that are directly associated with effects on transcript stability (and sub-cellular localization) in yeast.
53 motifs: 40 de-stabilizers, 13 stabilizers many of them are conserved in other yeasts 11 are significantly similar to recently
published mammalian conserved 3’ UTR motifs intricate relationship with promoter motifs http://longitude.weizmann.ac.il/3UTRMotifs/
A first step towards filling the gap of transcript level regulation
Post transcriptional control
• Functional sequence motifs in 3’ UTRs stability associated motifs
(Shalgi et al. Genome Biology 2005)
• miRNA regulation (Xi et al. Clin Cancer Res. 2006)
Transcription Translation
ProteinDNA
Transcrip
tion
miRNA
Deg
rada
tion
Deg
radatio
n
mRNA
Differentially Regulated Micro-RNAs by Tumor Suppressor p53
in Colon Cancer
Xi Y, Shalgi R, Fodstad O, Pilpel Y, Ju J.
Clin Cancer Res. 2006
Background – p53 p53 is a tumor suppressor. regulates DNA repair, cell
senescence, appoptosis, and more. Is a critical inhibitor of tumour
development is the most frequently mutated gene
in human cancers p53 is a TF.
Background – microRNAs (miRs)
Small (~21 nt) RNAs Post-transcriptional
silencing: Regulate mRNA
degradation and translation
inhibition Through RISC (RNA
induced silencing complex)
Identification of miRNAs regulated by p53:Cancer cells: HCT-116 +/+ and p53- cells show differential miRNA expression using miRNA microarray:43 miRs were downregulated 11 were upregulated in the wt (vs. the mutant)
miRNAs that are transcriptionally regulated
by p53
p53+p53-
Looking for p53 binding sites in miRNA promotersp53 binding site search:
0 -3
miRNAs that are transcriptionally regulated by
p53
(Wei CL, Cell, Jan 2006)
List of p53 binding sites in promoters of the 10 most highly variable miRNAs
9 out of the 10 had a site in their promoter
miRNAs that are transcriptionally regulated by
p53
miRNA pos. gap len. SiteScorehsa-let-7b 828 0 AGCCATGTCT..CTTCTTGTCT87.56
Looking at all known miRNAs in the database (326): 130 (~40%) have a putative p53 binding site in their promoter control: 1000 sets of reshuffled promoters: p-value < 0.001
Global analysis of p53 sites in miRNAs
promoters
A highly significant enrichment for p53 binding sites in miRNA upstream regions
Is there a specific tendency for p53 to regulate miRNAs?
p53 as a hub in the signaling network another mechanism of p53 global control of
cellular processes under stress
p53 regulation of miRNAs
Summary – Summary – transcript stability transcript stability mechanisms mechanisms
S. cereviciae de-adenylation
dependant degradation Stabilizing/de-
stabilizing RNA binding proteins.
No Dicer & RISC 3’ UTR motifs (perhaps also
5’ UTR)
Higher organisms Both de-
adenylation dependant deg.
And miRNA
Dicer & RISC 3’ UTR motifs
ProteinDNA
miRNA
mRNA
Thanks !
Tzachi Pilpel Moshe Oren Ron Shamir
Michal Lapidot Ophir Shalem and all the other Pilpel lab
membersCollaborators:
P53 miR project:Ju group
Cancer Research Institute,Mobile, Alabama
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