Genome Evolution. Amos Tanay 2012 Genome evolution: Lecture 12: Evolution of regulatory sequences

Genome Evolution. Amos Tanay 2012

Genome evolution:

Lecture 12: Evolution of regulatory sequences


Beyond Protein Coding Sequences

Non coding fraction of the genome:• E. coli : 12%• Yeast : 27%• Fly : 76%• Human : 97.6%

How biological functions of non-coding sequence can be defined?


Sequence specific transcription factors• Sequence specific transcription factors (TFs) are a critical part of any gene activation or gene

repression machinery• TFs include a DNA binding domain that recognize specifically “regulatory elements” in the

genome.• The TF-DNA duplex is then used to target larger transcriptional structure to the genomic

locus.

Lactose Repressor


Sequence specificity is represented using consensus sequences or weight matrices

• The specificity of the TF binding is central to the understanding of the regulatory relations it can form.

• We are therefore interested in defining the DNA motifs that can be recognize by each TF.• A simple representation of the binding motif is the consensus site, usually derived by

studying a set of confirmed TF targets and identifying a (partial) consensus. Degeneracy can be introduced into the consensus by using N letters (matching any nucleotide) or IUPAC characters (representing pairs of nucleotides, for examlpe W=[A|T], S=[C|G]

• A more flexible representation is using weight matrices (PWM/PSSM):

• PWMs are frequently plotted using motif logos, in which the height of the character correspond to its probability, scaled by the position entropy

ACGCGTACGCGAACGCATTCGCGATAGCGT

1 2 3 4 5 6

A 60% 20% 0 0 20% 40%

C 0 80% 0 100% 0 0

G 0 0 100% 0 80% 0

T 40% 0 0 0 0 60%


In vitro TF binding energy is approximated by weight matrices

Yeast Leu3 data (Liu and Clarke, JMB 2002)

We can interpret weight matrices as energy functions:

])[log(][

][)(

iiii

iii

spsw

swsE

This linear approximation is reasonable for most TFs.


• s

In-vivo TF binding affinity is approximated by weight matrices

• s

Ume6

ChIP ranges

11.5

5.5

Av

era

ge

PW

M e

ne

rgy

Stronger bindingS

tronger p

redictionTanay. Genome Res 2006

Cross-link and sheer

ImmunoPrecipitation

Chromatin ImmunoPrecipitation (ChIP)


TF binding affinity is kinetically important, with possible functional implications

Kalir et al. Science 2001


TFs are present at only a fraction of their optimal sequence targets. Binding is regulated by co-factors, nucleosomes and histone modifications

Heinzman et al. Nature Genetics, 2007)


TFs are present at only a fraction of their optimal sequence targets. Binding is regulated by co-factors, nucleosomes and histone modifications

Heinzman et al. Nature Genetics, 2007)


Specific proteins are identifying enhancersHere are studies of p300 binding in the developing mouse brain

(visel et al. Nature 2009)


TFBSs are clustered in promoters or in “sequence modules”

• The distribution of binding sites in the genome is non uniform• In small genomes, most sites are in promoters, and there is a bias toward nucleosome free

region near the TSS• In larger genomes (fly) we observe CRM (cis-regulatory-modules) which are frequently away

from the TSS. These represent enhancers.• A single binding site, without the context of other co-sites, is unlikely to represent a

functional loci


Discriminative scores for motifs

• So far we used a generative probabilistic model to learn PWMs• The model was designed to generate the data from parameters• We assumed that TFBSs are distributed differently than some fixed background model

• If our background model is wrong, we will get the wrong motifs..

• A different scoring approach try to maximize the discriminative power of the motif model.• We will not go here into the details of discriminative vs. generative models, but we shall

exemplify the discriminative approach for PWMs.

Lousy discriminator High specificity discriminator High sensitivity discriminator


Hypergeometric scores and thresholding PWMs

||

||

||||

)|(|

B

n

kB

An

k

A

kBAP

PWM score threshold

Nu

mb

er

of

seq

ue

nce

s

Positive

True positive

For a discriminative score, we need to decide on both the PWM model and the threshold.

Hyper geometric probability

(sum for j>=k is the hg p-value)


Constructing a weight matrix from aligned TFBSs is trivial

• This is done by counting (or “voting”)• Several databases (e.g., TRANSFAC, JASPAR) contain matrices that were

constructed from a set of curated and validated binding site• Validated site: usually using “promoter bashing” – testing reported

constructs with and without the putative site

Transfac 7.0/11.3 have 400/830 different PWMs, based on more than 11,000 papers

However, there are no real different 830 matrices out there – the real binding repertoire in nature is still somewhat unclear


High density arrays quantify TF binding preferences and identify binding sites in high throughput

• Using microarrays (high resolution tiling arrays) we can now map binding sites in a genome-wide fashion for any genome

• The problem is shifting from identifying binding sites to understanding their function and determining how sequences define them

Harbison et al., Nature 2004


Direct measurements of the in-vitro binding affinity of 8-mers and DNA binding domains (here just a library of homeodomains, from Berger et al. 2008)


Profiling binding affinity to the entire k-mer spectrum provide direct quantification of in-vitro affinity (Badis et al., 2009)

Heatmap of 2D hierarchical agglomerative clustering analysis of 4740 ungapped 8-mers over 104 nonredundant TFs, with both 8- mers and proteins clustered using averaged E-score from thetwo different array designs.

8-mers

104 TFs


What kind of biological function is naturally selected?

Discrete and deterministic “binding sites” in yeast as identified by Young, Fraenkel and colleuges

In fact, binding is rarely deterministic and discrete, and simple wiring is something you should treat with extreme caution.


The Halpern-Bruno model for selection on affinity

Ns

s

e

e2

2

1

1

According to Kimura’s theory, an allele with

fitness s and a homogeneous population would fixate with probability:

NsNs

s

ba

NsNs

s

ab

e

s

e

ef

e

s

e

ef

22

2

22

2

1

2

1

1

1

2

1

1

Assuming slow mutation rate (which allow us to assume a homogenous population) and motifs a and b with relative fitness s the fixation probabilities (chance of fixation given that mutation occurred!) are:

NsNs

NsNs

Nsbaab ee

e

s

e

e

sff 2

2

22

2 1

1

2

1

1

2/

If p represent the mutation probability, and the stationary distribution, and if we assume the process as a whole is reversible then:

Ns

ba

ab

aba

bab

ababa

babab ef

f

p

p

fp

fp 21

bab

aba

aba

bab

ab

pppp

f

1

ln

We work on deriving the substitution rate at each position of the binding site, given its observed stationary frequency. We are assuming that the fitness of the site is defined by multiplying the fitness values of all loci. This means fitness is generally linear in the binding energy!

bab

aba

aba

bab

abab

pppp

pcr

1

ln

(Halpern and Bruno, MBE 1998)

1,1 ssfitness


The Halpern-Bruno model for selection on affinity

Moses et al., 2003

The HB model is limited for the study of general sequences.When restricting the analysis to relatively specific sites, HB is not completely off


• The entire genome should behave like a mixture of background sequance and functional loci:

• So we can try and recover Q(E) and therefore F(E) from the maximum likelihood parameters fitting an empirical W(E)

Testing the general binding energy – fitness correspondence

• While E(S) is approximated by a PWM, F(E) is unlikely to be linear

• Assume that the background probability of a motif a is P0(a). In detailed balance, and assuming the fitness of a at functional sites is F(a), the stationary distribution at sites can be shown to be:

Mustonen and Lassig, PNAS 2005

)(2)()( aNFo eaPaQ

• If we collapse all sites with binding energy E (and hence the same F(a)=F(E(a))

)(2)()( ENFo eEPEQ

)()()1()( EQEPEW o Inferred F(E), is shown in Orange

Expected and observed energy distribution in E.Coli CRP sites (left) and background (right)

Comparison of CRP energies in E.coli and S. typhimurium

(Hwa and Gerland, 2000-)


TFBS evolution: purifying selection and conservation

Similar function

Neutral evolution

Disrupted function

Low ratepurifying selection

TF1

TF2

Altered function

Low ratepurifying selection

TF1

CACGCGTACACGCGTT

TF1

CACGAGTTCACGCGTT

CACACGTTCACGCGTT Altered affinity

Rate?Selection?

TF1

CACACGTTCACGCGTT


Kellis et al., 2003

Binding sites conservation


Binding sites conservation: heuristic motif identification

Kellis et al., 2003


Analyzing k-mer evolutionary dynamics

• Instead of trying to identify conserved motifs try to infer the evolutionary rate of substitution between pairs of k-mers

• Start from a multiple alignment and reconstruct ancestral sequences (assuming site independence, or even max parsimony)

• Now estimate the number of substitution between pairs of 8-mers, compare this number to the number expected by the background model

• Do it for a lot of sequence, so that statistics on the difference between observed and expected substitutions can be derived


Saccharomyces TFBS Selection Network

Arcs: 1nt substitutionRate Selectio

nNormal

Low

neutral

negative

arc

not enough stat

Nodes: octamers

conserved @ 2SD

conserved @ 3SD

node

otherwise

conservation

Inter-island organization in the Reb1 cluster: selection hints toward multi modality of Reb1

Tanay et al., 2004


Leu3 selection network

log delta affinity

0.3

0.2

0.1

03210-1-2-3-4-5

High Affinity (Kd < 60)

Meidum Affinity (400 > Kd > 60)

High rate subs.

Substitution changing high affinity to high

affinity motifs

Substitution changing high affinity to low affinity motifs

Sub

stitu

tion

rate


A simple transcriptional code and its evolutionary implications

AAATTTAATTTTAAAATT

GATGAGGATGCGGATGAT

CACGTGCACTTG

ACGCGTTCGCGTACGCGT

All th

e re

st

TGACTGTGAGTGTGACTT

TF1

TF2

TF3

TF4TF5


The Halpren-Bruno model for selection on affinity

The basic notion here is of the relations between sequence, binding and function/fitness

Sequence

Binding energy

Function )(

)(

EF

SE

We argued that E(S) can be approximated by a PWM

F(E) is a completely different story, for example:Is there any function at all to low affinity binding sites?Is there a difference between very high affinity and plain strong binding sites?Are all appearances of the site subject to the same fitness landscape?


S. cerevisiae S. mikitaeSimulation(Neutral, context aware)

High affinity

Low affinity

ΔEΔE....

ΔEΔE....

0

0.2

0.4

0.6

0.8

1

0 0.25 0.5

KS statistics

More tests for possible conservation of low binding energy sites


More tests for possible conservation of low binding energy sites

Tanay, GR 2006

Binding site conservation

Conservation of totalenergy

0

5

10

15

20

0 50 1000

5

10

15

20

0 50 100

0

5

10

15

20

0 50 100

Reb1

Ume6

binding energy percentile

Co

nse

rvat

ion

sco

re

Cbf1 Gcn4Mbp1

binding energy percentile binding energy percentile

0

10

20

30

40

50

60

0 50 100

Co

nse

rvat

ion

sco

re

0

5

10

15

20

0 50 100 binding energy percentile

binding energy percentile


Evolutionary dynamics of transcription factor binding (mammals)

Schimdt et al. Science 2010

Shared binding loci: 4%


Evolutionary dynamics of CTCF binding (mammals)

Schimdt et al. Cell 2012

Shared binding loci: 24%


Bradley et al. PLoS biology 2010

Evolutionary dynamics of transcription factor binding (flies) – correlates with the sequence

Documents

Genome Evolution. Amos Tanay 2012 Genome evolution: Lecture 12: Evolution of regulatory sequences