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Regulatory Genomics Lecture 2 November 2012 Yitzhak (Tzachi) Pilpel 1.
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Transcript of Regulatory Genomics Lecture 2 November 2012 Yitzhak (Tzachi) Pilpel 1.
Regulatory Genomics
Lecture 2 November 2012
Yitzhak (Tzachi) Pilpel
Lecture 2 November 2012
Yitzhak (Tzachi) Pilpel1
Course requirements
• Attendance and participation
• Two reading assignments
• A final take home papers reading-based exam
• website
No meeting next week on Nov 15th
2
Expression regulation of genes determines complex spatio-temporal patterns
3
Monitor expression during
cell cycle
0 5 10 15-2
-1
0
1
2
3
4
Time
mR
NA
exp
ress
ion
leve
l
G1 S G2 M G1 S G2 M 4
Time-point 1
Tim
e-po
int 3
Tim
e-po
int 2
-1.8
-1.3
-0.8
-0.3
0.2
0.7
1.2
1 2 3
-2
-1.5
-1
-0.5
0
0.5
1
1.5
1 2 3
-1.5
-1
-0.5
0
0.5
1
1.5
1 2 3
Time -pointTime -point
Time -point
Normalized
Expression
Normalized
Expression
Normalized
Expression
Genes can be clustered based on time-dependent expression profilesGenes can be clustered based on time-dependent expression profiles
5
The K-means algorithm
• Start with random positions of centroids.
Iteration = 0
6
K-means
• Start with random positions of centroids.
• Assign data points to centroids
Iteration = 1
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K-means
• Start with random positions of centroids.
• Assign data points to centroids.
• Move centroids to center of assigned points.
Iteration = 1
8
K-means
• Start with random positions of centroids.
• Assign data points to centroids.
• Move centroids to center of assigned points.
• Iterate till minimal cost. Iteration = 3
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An expression cluster
1D and 2D clustering of gene expression data
Hierarchical clustering
How to join sets?
f
e dc
b
a
How to measure a distance between expression profiles?
14
Gene x
Gen
e y
t1
t2t3
Gene x
Gen
e y
t4t5
Clustering the data
http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletH.html
http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html
Try these two applets at home (needs java)
The common distance matrices
16
Promoter Motifs and expression
profilesCGGCCCCGCGGA
CTCCTCCCCCCCTTC TGGCCAATCA
ATGTACGGGTG
17
Formaldehyde crosslinks living yeast cells
Binding site
TFBinding site Binding site
Inside the yeast nucleus:
ChIP - chromatin immunoprecipitation
Reversal of the crosslinks to separate DNA segments from proteins,and fluorescence labeling of each pool separately
(enriched DNA)
hybridization to DNA array of all yeast intergenic sequences
(unenriched DNA)
TF
= epitope tag
= TF of interest
Harvest and sonicate; results in DNA fragments(some of which are bound to proteins)
18
P-value 0.0535,365 interactions
P-value 0.0112,040 interactions
P-value 0.0058,190 interactions
P-value 0.0013,985 interactions
P-value, or confidence level, for each spot in array
The total number of protein-DNA interactions in the location analysis data set, using a range of P value thresholds:
A P-value was selected which minimizes false positives, at the expense of gaining false negatives. P-value 0.001
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Genome-wide Distribution of Transcriptional Regulators
• Promoter regions of 2343 of 6270 yeast genes (37%) were bound by 1 or more of the 106 transcriptional regulators (P=.001)
Avg.: regulator binds 38 promoter regions
At P= 0.001, significantly more intergenic regions bind 4 or more regulators than expected by chance
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Network Motifs
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Network Motifs
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Network Motifs in the Yeast Regulatory Network
-Based on algorithmic analyses performed in Matlab; http://jura.wi.mit.edu/cgi-bin/young_public/navframe.cgi?s=17&f=networkmotif
103
49
90 81
188
23
Protein
Gene
The Cell Cycle Transcriptional Regulatory Network:
Various stages of cell cycle
Blue boxes represent sets of genes bound by a common set of regulators.
Each box is positioned according to the time of peak expression levels for the genes represented by the box.
Ovals represent regulators, connected to genes they regulate
Length of arc defines the period of activity of that regulator24
Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators
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Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators
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Network of Transcriptional Regulators Binding to Genes Encoding Other Transcriptional Regulators
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DNA mRNA Protein
Inactive DNA
The Central Dogma of Molecular BiologyExpressing the genome
RNA
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Translation consists of initiation, elongation and termination
5’5’ 3’3’STOPSTOP
Codon
Anti-codon
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The ribosome attachment site determines initiation rate
E. coli
Yeast
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A consensus for S. cerevisiae ribosome attachment sites?
position relative to ATG
100%
0%
sequenceHow good is it as a
“ribosomal attachment site” ?
ribosomal attachmentsite score
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5’ 3’
CTGCGC
GCG
GCGGCG
GCG
GCG
GCGGCG
CAGGCG
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Rank
ribosomal attachmentsite score
The sequence adaptation score of proteins in yeast
CRP
good score
bad score
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Multiple codons for the same amino acid
C1 C2 C3 C4 C5 C6Serine: UCU UCC UCA UCG AGC AGUCysteine: UGU UGCMethionine: UGG
STOP: UAA, UAG UGA
C1 C2 C3 C4 C5 C6Serine: UCU UCC UCA UCG AGC AGUCysteine: UGU UGCMethionine: UGG
STOP: UAA, UAG UGA
34
G T R Y E C Q A S F D
C1C1C1C1C1C1C1C1C1C1C1C2C2C2C2C2C2C2C2C2C2C2C1C1C2C1C1C2C1C1C2C1C1C2C2C2C2C1C1C1C1C1C1C1C1C1C1C1C1C1C1C2C2C2C2
For a hypothetical protein of 300 amino acids with two-codon each, There are 2^300 possible nucleotide sequences
These variants will code for the same protein, and are thus considered “synonymous”.
Indeed evolution would easily exchange between them
These variants will code for the same protein, and are thus considered “synonymous”.
Indeed evolution would easily exchange between themBut are they all really equivalent??
35
Selection of codons might affect:AccuracyThroughput
CostsFolding
RNA-structure
36
in
jijiji tRNAsW
1
)1(
Wi/Wmax if Wi0wi = wmean else{
tAIg wikk1
g
1/g
dos Reis et al. NAR 2004
The tRNA Adaptation Index (tAI)
ATC CCA AAA TCG AAT … ……
A simple model for translation efficiency
Wobble InteractionWobble Interaction
37
Supply demand and charging
38
How the RNA structure influences translation?
?
39
No correlation between CAI and protein expression
Positive correlation between structure’s energy and expression
The 5’ window needs to be un-folded for high expression
Pro
tein
ab
unda
nce
Pro
tein
ab
unda
nce
Conclusions from synthetic library
40
Formaldehyde crosslinks living yeast cells
Binding site
TFBinding site Binding site
Inside the yeast nucleus:
ChIP - chromatin immunoprecipitation
Reversal of the crosslinks to separate DNA segments from proteins,and fluorescence labeling of each pool separately
(enriched DNA)
hybridization to DNA array of all yeast intergenic sequences
(unenriched DNA)
TF
= epitope tag
= TF of interest
Harvest and sonicate; results in DNA fragments(some of which are bound to proteins)
41
A genome-wide method to measure translation efficiency
(Ingolia Science 2009)
42
Translational response to starvation
43
DNA mRNA Protein
Inactive DNA
The Central Dogma of Molecular BiologyExpressing the genome
RNA
44
mRNA abundance
Option 1 Option 2 Option 3 Option 4
Production
degradation
45
Relationship between gene expression levels and mRNA decay rates across genes.
A study in human population examined decay and steady-state mRNA level variation across people.Found strong negative or positive correlations between mRNA level and decay rates.Fast responding genes show “discordant” relation suggesting that increased expression is often accompanied by increased decay rate
The various phases are coupled
47
At the hardware level (post-transcription: RNA binding proteins)
G1 1 1 1 0
G2 1 0 0 1
G3 0 1 1 1 48
At the hardware level (post-transcription: microRNA)
G1 1 1 1 0
G2 1 0 0 1
G3 0 1 1 1
RISC RISC RISC RISC
49
Yang CGFR 16:397, 2005
50
Computational approaches to find microRNA genes
• MiRscan (Lim, et al. 2003)– Scan to find conserved
hairpin structures in both C. elegans and C. briggsae.
– Using known microRNA genes (50) as training set.
51
What is the effect of over expression of a miR?
52
53
None-Coding RNAs are often co- targeted with their own targets for various cellular needs
miR-124 decreases similarly the abundance and translation of mRNA targets
54
microRNA expression profiles classify microRNA expression profiles classify human cancershuman cancers
Lu et al. Nature 435: 834, 2005Samples (patients)
miR
s
55
Gene expression is noisy
56
Fluorescence distribution shapes
57
The cell intrinsic and extrinsic contributions to noise
58
DNA
RNA
Protein
Regulationby transcription
factors
RNA Polymerase
RibosomeExtrinsic
IntrinsicChromatin
remodeling
Transcription process
Translation process
Φ
Protein degradation
The actual intrinsic and extrinsic sources of noise:Extrinsic – variation in copy numbers of molecules
among cells; Intrinsic: stochastic events
59
A theoretical approach
60
DNA mRNA Protein
The ratio of transcription to translation should affect noise
61
Transcription bursts should affect noise
62
Can noise be useful?
The native net shows longer and more duration-diverse competence periods
Native networks does better on a wider range of extracellular [DNA]
The trade-off:High competence allows finding solutions, but reduces growth rate
Questions about noise
• What are the sources of noise?
• How is noise regulated in cells
• How is it tolerated by the biological systems that need to be noise free?
• When is noise advantageous /deleterious/ neutral?
66