Genome Evolution. Amos Tanay 2009 Genome evolution Lecture 12: epistasis and the evolution of gene...

Genome Evolution. Amos Tanay 2009

Genome evolution

Lecture 12: epistasis and the evolution of gene regulation


Britten and Davidson, July 1969

The view of cells as complex networks of genes that interact and regulate each other became a central part of the modern central dogma of molecular biology

Cells are complex gene networks


Many networks in today’s biology – most are not directly interpretable in evolutionary/genomics term, beware!

Metabolic networks: representing metabolic reactions and enzymes catalyzing them.

State of the art: characterized in many species. Enzymes identified. Dynamics modeled using linear approximation (Flux balance analysis)

Protein networks: representing different types of (usually physical) interaction among proteins.

State of the art: Methods in development (mass spec and more). Large surveys in yeast providing reasonable coverage. In mammals work in progress. Dubious quality for some of the data. Structure-based prediction still minimal.

Genetic interaction networks: representing fitness interaction among genes

State of the art: Available for large fractions of the pairs in yeast. Flies/Mammals technique in development using RNAi – but not easy.


Transcriptional regulation generate a network that is more directly encoded by the genome

Maps of interaction between TFs and genomic loci.

State of the art: Almost complete for specific conditions in yeast. Data on larger genomes rapidly acumulating

Transcriptional regulation is encoded into several levels of the genome:

- The transcription factor sequence (trans- effect)- The binding site (cis- effects)

- (The binding site neigborhood – co-factors, epigenetics)- (Sequence of co-factors and their own regulation…)

Transcription regulation (our phenotype) can therefore be:Conserved due to conservation of the genotypeDiverge due to divergence of any of multiple loci in the genomeConserved due to coordinated divergence of multiple loci in the genome


After S. Carroll

Phenotypic innovation through regulatory adaptation


After S. Carroll


Ancient and Recent Positive Selection Transformed Opioid cis-Regulation in Humans (Rockman, Plos Biol, 2005)

Sequence evidence for positive selectionTry to remember what can help us establish this? (e.g. divergence and polymorphisms)

The human variant is indeed responding differently


Big questions in evolution of regulation

• How does the network structure affect genome evolution (conservation and divergence)? Can we enhance our understanding of these effects at the population genetics level?

• Which levels in the genome drives regulatory innovation? (cis- or trans-)

• What are the major drivers of phenotypic innovation – regulation or proteins?

Big challenges in comparative genomics of regulation:

• Can we infer regulatory mechanisms from patterns of conservation and divergence?

• Can we combine functional experiments on the regulatory phenotype into our models?

• Would extensive comparative genomic ultimately breaks regulatory codes that are currently not understood?


Comparative genomics:

• Obtain a set of sequenced genome• Collect some functional data on them (expression, TF interaction,

epigenomics)• Describe the conservation and divergence of the sequence and functional

data• Build models that describe genome evolution given some regulatory potential

and fit it to the data – then infer function from the sequence

Interventions• Work with two or more species

• Introduce some genomic alteration, emulating some evolutionary scenario (possibly and absurdic one)

• Examine the behavior of the altered genomic fragment

Evolutionary experiment• Evolve strains given some controlled conditions

• Follow phenotypic and genomic changes

• (why isn’t it actually possible?) (think about s and


EpistasisAssume we have two loci, each bearing two alleles (Aa and Bb)Assume that the basal state of the population is homogenous with alleles ab

f(A) - The relative fitness of A is defined using the growth rate of the genome Abf(B) - The relative fitness of B is defined using the growth rate of the genome aB

What is the fitness of AB?

If the two loci are unrelated, we can expect it to be: f(Ab)*f(aB)

When f(A)=1+s, f(B)=1+s’, and s,s’ are small, f(A)*f(B)~(1+s+s’)

Epistasis is defined as the deviation from such linearity/independence:

f(AB) > f(Ab)*f(aB): synergistic locif(AB) < f(Ab)*f(aB): antagonistic loci

A B

AB

+ A B

AB

-

How widespread is epistasis? Is it positive or negative in general? and how it affect evolution in general?


Testing epistasis in viruses: directed mutagenesis

Sanjuan, PNAS 2004

47 genotypes of vesicular stomatitis virus carrying pairs of nucleotide substitution mutations (filled)15 genotypes carrying pairs of beneficial mutations (empty circles)

Epistasis is generally negative here


Testing epistasis in viruses: HIV-1 isolated drug resistant strains

Comparing growth in drug-free media (extracting viral sequence and reintegrating it in a virus model)Sequencing strains, comparing to some standardPlotting fitness relative to the number of mutations:

Bonhoeffer et al, science 2004

For each pair of loci, compute average fitness for aa,aB,Aa and BB, then estimate epistasis. To assess significance, recompute the same after shuffling the sequences

Mean is significantly higher than randomized meansEffect is stronger when analysis is restricted to 59 loci with significant effect on fitness

Results suggesting that epistasis tends to be positive (at least in these viruses and in this condition)


Functional sources for epistasis:

• Protein structure (interacting residues)

• Different positions in the same TFBS

• Two interacting TFBSs

• TF DNA binding domain and its target site

• Two competing enzymes

• Two competing TFBS

• RNA paired bases

• Groups of TFBSs at co-regulated promoters


RNA folds and the function of RNA moelcules

•RNA molecular perform a wide variety of functions in the cell

•They differ in length and class, from very short miRNA to much longer rRNA or other structural RNAs.

•They are all affected strongly by base-pairing – which make their structural mostly planar (with many exceptions!!) and relatively easy to model

Simple RNA folding energy: number of matching basepairs or sum over basepairing weights

More complex energy (following Zucker): each feature have an empirically determined parametersstem stacking energy (adding a pair to a stem)bulge loop lengthinterior loop lengthhairpin loop lengthdangling nucleotides and so on.

Pseudoknots (breaking of the basepairing hierarchy) are typically forbidden:


Predicting fold structure

Due to the hierarchical nature of the structure (assuming no pseudoknots), the situation can be analyzed efficiently using dynamic programming.

We usually cannot be certain that there is a single, optimal fold, especially if we are not at all sure we are looking at a functional RNA.

It would be better to have posterior probabilities for basepairing given the data and an energy model…

This can be achieved using a generalization of HMM called Stochastic Context Free Grammar (SCFG)


EvoFold: considering base-pairing as part of the evolutionary model

Once base-pairing is predicted, the evolutionary model works with pairs instead of single nucleotides.By neglecting genomic context effects, this give rise to a simple-tree model and is easy to solve.If we want to simultaneously consider many possible base pairings, things are becoming more complicated.

An exact algorithm that find the best alignment given the fold structure is very expensive (n^5) even when using base pairing scores and two sequences. Pedersen PloS CB 2006


EvoFold: considering base-pairing as part of the evolutionary model

Whenever we discover compensatory mutations, the prediction of a functional RNA becomes much stronger.


Compensatory mutations in proteins?

PDB structuresHomology modelling

3-Alignments

Pairs of interacting residues

Rat Mouse Human

Choi et al, Nat Genet 2005

Find pairs of mutations in interacting residues (DRIP)Coupled: occurring in the same lineageUncoupled: occurring in different lineages


Ludwig, Kreitmen 2000

eve stripe 2 in D. melanogaster and D. pseudoobscura – conserved phenotype by a compensatory substitution pattern in two parts of the enhancer

mel pseudo

While the two enhancers drive a conserved expression pattern, we cannot mix and match them between species!Evolution therefore continuously compensate for changes in one part with changes in the other.


D. Melanogaster

D. Yakuba

D. Erecta

D. Pseudoobscura

Across a larger phylogeny, the phenotype can diverge

Ludwig,..,Kreitmen 2005

The D. Erecta S2E is forming much weaker stripe in D. Mel.

Eve staining in 4 speciesOrthologous stripe 2 enhancer reporters in a melanogaster embryo


D. Melanogaster

D. Yakuba

D. Erecta

D. Pseudoobscura

The conservation of the enhancer sequence itself cannot predict the conservation of the phenotype

Enhancer functional in mel.

Enhancer functional in mel.

Enhancer not functional in mel.

Sequence conserved

Sequence conserved

Sequence not conserved

May reflect compensation

May reflect trans- diverg

All conserved


Species-Specific Transcription in Mice Carrying Human Chromosome 21 (Wilson et al. 2008)

Duncan Odom and co-workers introduced human chromosome 21 into mouse cellsUsing ChIP they showed that most binding sites (of enhancer mostly) were remain active as in human cells – suggesting they are determined in cis.


Coregulation: epistasis of transcriptional modules

• Transcriptional modules are crucial for the organization and function of biological system

• Gene co-regulation give rise to major epistatic relations among regulatory loci

• epistasis reduces evolvability

Co-regulationIs advantageous

Disruption of regulationIs deleterious

RegulationScheme 1 Regulation

Scheme 2

Rugged evolutionarylandscape


Cis-elements underlying conserved TMs

32 genesP<10-29

S. c

erev

isia

e

S. P

ombe

114genes

P<10-151

S. c

erev

isia

e

S. P

ombe

45genesP<10-56

Ribosome biogenesis

S. P

ombe

S. c

erev

isia

e

S phase

S. pom

be

7 genesP<10-9

S. c

erev

isia

e

Amino acid met. Ribosomal Proteins


Phylogenetic cis-profiling with 17 yeast species

A.

nidu

lan

s

S.

baya

nus

S.

cere

visi

ae

K.

wal

tii

A.

goss

ypii

S.

cast

ellii

N.

cras

sa

S.

pom

be

C.a

lbic

ans

S.

klu

yver

ii

Y.

lyp

oliti

ca

D.

han

sen

ii

K.

lact

is

C.

glab

rata

Putative Orthologous

Module (POM)


Conserved cis-elements

S. cerevisiae

S. castellii

S. kluyveri

K. waltii

A. gossypii

C. albicans

N. crassa

A. nidulans

S. bayanus

S. kudriavzevii

S. mikatae

S. paradoxus

S. pombe

MCB HAP2345 GCN4

S phase Respiration Amino acid metabolism

C. galbrata

K. lactis

D. hansenii

Y. lipolytica

•Conserved FM are sometime regulated by remarkably conserved cis elements

•Conserved cis elements are bounded by conserved TFs

Tanay et al. PNAS, 2005


RAP1 Homol-D IFHL

S. cerevisiae (133)

S. castellii (89)

S. kluyveri (61)

K. waltii (54)

A. gossypii (73)

C. albicans (41)

N. crassa (67)

A. nidulans (72)

S. bayanus (118)

S. kudriavz .(94)

S. mikatae (88)

S. parad. (75)

S. pombe (74)

C. glabrata (69)

K. lactis (75)

D. hansenii (73)

Y. lipolytica (70)

73 44

49

46

30

51 53

41 46

29 3230

31 3034

1735

52 3264

21 4529

45 4053

4054

4048

4657

3146

38112

Rap1 emergence

Homol-Dloss

Ribosomal Protein Module:Evolutionary change viaredundancy

Redundantmechanism

Homol-Dbased


Rap1 evolution in trans

BCRT Myb Silencing TA

S. cerevisiae

S. castelii

K. waltii

A. gossypii

C. albicans

N. crassa

A. nidulans

S. pombe

H. sapiens

New TA domainCo-emerged withRap1 role in RP regulation


Redundant cis-elements are spatially clustered: RP genes in A. gossypii

3’6bp

Homol-D RAP1

5’


Evolution of the IFHL element

pombe

nidulans

crassa

lypolityca

albicans

hansenii

sacc. et al.

Tandem duplication

Conservation

Reverse complement duplication

Drift…


Evolution of the Ribosomal biogenesis module

S. cerevisiae (225)

S. castellii (204)

S. kluyveri (178)

K. Waltii (230)

A. gossypii (226)

C. albicans (214)

N. crassa (193)

A. Nidulans (187)

S. bayanus (195)

S. kudri. (196)

S. mikatae (187)

S. parad. (215)

S. pombe (196)

C. glabrata (214)

K. lactis (225)

D. hansenii (219)

Y. lipolytica (208)

RRPE PAC TC?

83

99

132 79

154

51 159

126 152

122 171 163

122 200 145

163 181 59

137 157 110

180 166

152 167

151 159

152 163

136 151

175 159

157 187


a, S. cerevisiae and C. albicans transcribe their genes according to one of three programs, which produce the a-, - and a/ -cells.

The particular cell type produced is determined by the MAT locus, which encodes a sequence-specific DNA-binding protein.

In S. cerevisiae, a-type mating is repressed in -cells by 2.

In C. albicans, a-type mating is activated in a-cells by a2.

In both species, a-cells mate with -cells to form a/ -cells, which cannot mate.

a2 is an activator of a-type mating over a broad phylogenetic range of yeasts.

In S. cerevisiae and close relatives, a2 is missing and 2 has taken over regulation of the type.

Tsong et al. 2006Mating genes

a2

2

Albicans

Cerevisiae


A transition of motifs is observed between Cerevisiae and albicans


Innovation in 2 is observed along with the emergence of possible mcm2 interaction

A redundant intermediate may have enable the switch


Ihmels Science, 2005

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