Transcript Genome evolution - The Faculty of Mathematics and Computer

Genome Evolution © Amos Tanay, The Weizmann Institute
Genome evolution 2010
Lecture 1: evolutionary ideas
Amos Tanay, Ziskind 204, ext 3579
‫עמוס תנאי‬
[email protected]
http://www.wisdom.weizmann.ac.il/~atanay/GenomeEvo/
Genome Evolution © Amos Tanay, The Weizmann Institute
Linnaeus - Species
Swedish (1708-1777)
Developed hierarchical
taxonomy (and
pioneered scientific
classification)
Even though his
classification scheme
included mythic
monsters, Goethe said
he is comparable only
to Shakespeare and
Spinoza
Genome Evolution © Amos Tanay, The Weizmann Institute
Lemarck - adaptation
Jean Baptiste Lamarck
French (1744-1829)
First specializing in
invertebrate zoology, collecting
samples for museums-gardens
1 paper in first 6 years as
professor
Controversial (geophysics,
chemistry..)
The “first” evolutionary theorist
“Forming order”
Complexification force
Adaptive force
Genome Evolution © Amos Tanay, The Weizmann Institute
Darwin – natural selection
Darwin
English (1809-1882)
Dislike surgeon studies
Famous Beagle trip
Maltussian growth
Survival of the fittest
Wallace
“Origin of species” (1859)
First print: 1250 copies
The Descent of Man, and
Selection in Relation to
Sex
Genome Evolution © Amos Tanay, The Weizmann Institute
Fischer,Haldane,Wright – Population genetics
Ronald Fischer: (English 1890-1962)
Start by studying crop variation
Invented ANOVA, Max likelihood, non parameteric statistics, Fischer
information
Qunatitative genetics, diffussion approximation
J.B.S Haldane: (English 1892-1964)
Aristocrat family
Briggs-haldane kinetics (Michaelis-Mentel Alternative)
Gene frequencies
Popular author and communicator
Fischer
Haldane
Sewall Wright: (American 1889-1988)
Geneticist (Guinea pigs)
Genetic drift, inbreeding..
Wright
Genome Evolution © Amos Tanay, The Weizmann Institute
Models of population genetics
Blue allele
A
A
Generations/time
a
Yellow allele
a
Modeling the dynamics of allele frequencies
AA
Generations/time
AA
Aa
aa
Aa
aa
Modeling the dynamics of allele frequencies
Genome Evolution © Amos Tanay, The Weizmann Institute
Modeling evolution
Blue allele
A
Generations/time
a
Yellow allele
A
a
Modeling the dynamics of allele frequencies
t
t+1
Genome Evolution © Amos Tanay, The Weizmann Institute
Mayr,Dobzhansky – Synthesis
Frequency of recessive allele (blue flower color) in “desert snow” flowers (Lynanthus parruae)
FSR  0.1589
0.717
0.005
Mayr
Ernst Mayr:
German/American
(1904-2005)
FRT  0.3299
0.000 0.000
0.032
0.573
0.657
0.009
0.302
0.000
0.007
0.000
0.000
0.504
0.005
0.008
0.000
H  0.4995
Theodosius Dobzhansky
(Ukrainan/American 19001975)
0.002
0.004
0.126
0.000
0.339
Tropical explorations:
birds
Dobzhansky
0.000
0.010
0.106
0.224
0.068
0.000
H  0.0272
0.014
0.411
H  0.3062
Speciation
Biogeography
Philosophy of Science:
rejected reductionism
The modern synthesis
Mendel
Darwin
Genetics and the origin of
species
Flies/plants field studies
Genome Evolution © Amos Tanay, The Weizmann Institute
Watson,Crick - Code
The code – Genomic sequences
…ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT…
Monod
Jacob
Crick
The machine – Protein networks in cells
Genome Evolution © Amos Tanay, The Weizmann Institute
Kimura: Stochasticity, Neutrality
Selectionists: Mutations are occurring by chance - some
get selected and these are the changes we see between
genomes
Kimura et al.: Most of the changes between
genomes are neutral - not a result of selection
…ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT…
…ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT…
…ACGAATAGCAAAAGGGCAGATGGCATTCTAGATCGAAAGCATGAAACTAGATAGCAT…
…ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT…
Kimura
…ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT…
Genome Evolution © Amos Tanay, The Weizmann Institute
Neutral Evolution
Kimura’s analytic achievement was the solution of a certain class of Partial
Differential Equations that describe the dynamic of allele frequencies under
neutral evolution


1 
V ( x) ( x, t )
 ( x, t )   M ( x) ( x, t ) 
t
x
2 x 2
But we can try and understand the essence of neutral evolution even without
fancy mathematics:
Neutral changes
Along the path are fixated
Last common ancestor
t=1
Coalescent
time
t=n
Genome Evolution © Amos Tanay, The Weizmann Institute
Felsenstein (and many others):
Phylogenetics, probability
Computational methods for sequence analysis
Construct phylogenies from genomes
Tree of live? Origin of early forms?
Gould-Eldrege:
Punctuated equilibrium
Better and better fossil record
Evolution/speciation rate: bursts
Joe Felsenstein
Genome Evolution © Amos Tanay, The Weizmann Institute
Ohno: duplication
Genome evolution is facilitated by duplications
Underlying concept: modularity
Susumo Ohno – (1928-2000)
Based on protein families at start
(Can you think of the challenges in explaining protein duplication?)
Genome Evolution © Amos Tanay, The Weizmann Institute
Yeast Genome duplication
• The budding yeast S. cerevisiae genome have extensive duplicates
• We can trace a whole genome duplication by looking at yeast
species that lack the duplicates (K. waltii, A. gosypii)
• Only a small fraction (5%) of the yeast genome remain duplicated
Genome Evolution © Amos Tanay, The Weizmann Institute
•
How can an organism tolerate genome duplication and massive gene loss?
•
Is this critical in evolving new functionality?
Genome Evolution © Amos Tanay, The Weizmann Institute
Jacob/Monod-> Evolving programs
F. Jacob
(b 1920)
J. Monod
(1910-1976)
Regulation
Development
Davidson..
Gould..
Lewis..
Evo-Devo
Genome Evolution © Amos Tanay, The Weizmann Institute
Maynard-Smith: interaction
Interaction between individuals inside a species: different
strategies
Introducing game theoretic ideas to evolution
What is the basic unit of evolution?
1920-2004
Genes may compete and interact in a population
Genome Evolution © Amos Tanay, The Weizmann Institute
The Genomics revolution
Genome Evolution © Amos Tanay, The Weizmann Institute
From hundreds to billions loci….
Genome = many
independent
nucleotides
x1
Universal
x2
x3
x4
x5
x6
Q
1960
Multiple copies of the same Markov process
1970
1980
Protein analysis
Phylogenetic reconstruction
1990
2000
2010
Genome Evolution © Amos Tanay, The Weizmann Institute
From hundreds to billions loci….
Genome = many
independent
nucleotides
x1
Universal
x2
x3
x4
x5
x6
Q
Multiple copies of the same Markov process
1960
1970
1980
1990
2000
2010
Genome Evolution © Amos Tanay, The Weizmann Institute
Humans and Chimps
~5-7 million years
3X109
{ACGT}
3X109
{ACGT}
Genome alignment
• Where are the “important” differences?
• How did they happen?
Genome Evolution © Amos Tanay, The Weizmann Institute
9%
1.2%
0.8%
3%
1.5%
0.5%
Human
Chimp
Gorilla
Orangutan
Gibbon
Baboon
Macaque
Where are the
“important”
differences?
How did new
features were
gained?
Marmoset
0.5%
Genome Evolution © Amos Tanay, The Weizmann Institute
Antibiotic resistance: Staphylococcus aureus
Timeline for the evolution of bacterial resistance in an S. aureus
patient (Mwangi et al., PNAS 2007)
•Skin based
•killed 19,000 people in the US during 2005 (more than AIDS)
•Resistance to Penicillin: 50% in 1950, 80% in 1960, ~98% today
•2.9MB genome, 30K plasmid
How do bacteria become resistant to antibiotics?
Can we eliminate resistance by better treatment protocols, given understanding of the evolutionary process?
Genome Evolution © Amos Tanay, The Weizmann Institute
Ultimate experiment: sequence the entire genome of the evolving S. aureus
Mutations
Resistance to Antibiotics
Vanco.
Rifampi
Oxacili
Dapto.
20/7
1
0.012
0.75
0.01
20/9
4
16
25
0.05
1/10
6
16
0.75
0.05
6/10
8
16
1.5
1.0
13/10
8
16
0.75
1.0
1 2 3 4-6 7 8 9 10 11 12 13 14 15…18
S. Aureus got found just few “right” mutations and survived multi-antibiotics
Genome Evolution © Amos Tanay, The Weizmann Institute
“Junk” and ultraconservation
Baker’s yeast
12MB
~6000 genes
The worm
c.elegans
100MB ~20,000 genes
Humans
3GB ~27,000 genes
1 cell
~1000 cells
~50 trillions cells
Genome Evolution © Amos Tanay, The Weizmann Institute
Archeological genomics reveal
sequences of extinct species!
Genome Evolution © Amos Tanay, The Weizmann Institute
From: Lynch 2007
Genome Evolution © Amos Tanay, The Weizmann Institute
intergenic
exon
intron exon intron exon
intron
ENCODE Data
exon
intergenic
Genome Evolution © Amos Tanay, The Weizmann Institute
Course duties
•Exercises – 70% of the grade
•Submit on time
•Present a paper
Topics:
Population genetics: models, drift, selection
Species, phylogenies
Probabilistic models for sequence evolution
Comparative genomics: inferring selection
Quantitative traits evolution
Evolution of transcription regulation
Mathematics: Markov processes, algorithms for probabilistic inference, some
statistics
Introduced without assuming much prior knowledge, buy may require work to
understand..