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Genome evolution 2012

Genome evolution 2012. Lecture 1: evolutionary ideas. Amos Tanay, Ziskind 204, ext 3579 עמוס תנאי amos.tanay@weizmann.ac.il http :// www . wisdom . weizmann . ac . il / ~atanay / GenomeEvo /. Linnaeus - Species. Swedish (1708-1777)

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Genome evolution 2012

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  1. Genome evolution 2012 Lecture 1: evolutionary ideas Amos Tanay, Ziskind 204, ext 3579 עמוס תנאי amos.tanay@weizmann.ac.il http://www.wisdom.weizmann.ac.il/~atanay/GenomeEvo/

  2. 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

  3. 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

  4. 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

  5. Fischer,Haldane,Wright – Population genetics Ronald Fisher: (English 1890-1962) Start by studying crop variation Invented ANOVA, Max likelihood, non parametric statistics, Fisher information Quantitative genetics, diffusion approximation Fischer J.B.S Haldane: (English 1892-1964) Aristocrat family Briggs-haldane kinetics (Michaelis-Mentel Alternative) Gene frequencies Popular author and communicator Haldane Sewall Wright: (American 1889-1988) Geneticist (Guinea pigs) Genetic drift, inbreeding.. Wright

  6. AA AA Generations/time Aa Aa aa aa Modeling the dynamics of allele frequencies Models of population genetics Blue allele A A Generations/time a Yellow allele a Modeling the dynamics of allele frequencies

  7. t+1 Modeling evolution Blue allele A A Generations/time a Yellow allele a Modeling the dynamics of allele frequencies t

  8. Mayr,Dobzhansky – Synthesis Frequency of recessive allele (blue flower color) in “desert snow” flowers (Lynanthus parruae) 0.717 0.005 0.000 0.000 0.032 Dobzhansky 0.573 0.657 0.000 Mayr 0.009 Ernst Mayr: German/American (1904-2005) Tropical explorations: birds Speciation Biogeography Philosophy of Science: rejected reductionism 0.000 Theodosius Dobzhansky (Ukrainan/American 1900-1975) Genetics and the origin of species Flies/plants field studies 0.002 0.302 0.007 0.004 0.000 0.000 0.126 0.504 0.005 0.106 0.008 0.000 0.339 0.000 0.224 0.010 0.068 0.000 0.014 0.411 The modern synthesis Mendel Darwin

  9. Watson,Crick - Code The code – Genomic sequences …ACGAATAGCAAATGGGCAGATGGCAGTCTAGATCGAAAGCATGAAACTAGATAGCAT… Monod Jacob Crick The machine – Protein networks in cells

  10. 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…

  11. 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 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 Coalescent time t=n t=1

  12. Felsenstein (and many others): Phylogenetics, probability Computational methods for sequence analysis Construct phylogenies from genomes Tree of life? Origin of early forms? Joe Felsenstein Gould-Eldrege:Punctuated equilibrium Better and better fossil record Evolution/speciation rate: bursts

  13. Ohno: duplication Genome evolution is facilitated by duplications Underlying concept: modularity Based on protein families at start (Can you think of the challenges in explaining protein duplication?) Susumo Ohno – (1928-2000)

  14. 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

  15. How can an organism tolerate genome duplication and massive gene loss? • Is this critical in evolving new functionality?

  16. Jacob/Monod-> Evolving programs F. Jacob (b 1920) J. Monod (1910-1976) Regulation Davidson.. Gould.. Lewis.. Development Evo-Devo

  17. Maynard-Smith: interaction Interaction between individuals inside a species: different strategies Introducing game theoretic ideas to evolution What is the basic unit of evolution? Genes may compete and interact in a population 1920-2004

  18. The Genomics revolution Mouse chromosomes colors overlaid on the human chromosomes – From the mouse genome paper

  19. From hundreds to billions loci…. Genome = many independent nucleotides x2 x5 x1 x4 x3 x6 Universal Q 1960 Multiple copies of the same Markov process 1970 1980 Protein analysis Phylogenetic reconstruction 1990 2000 2010

  20. From hundreds to billions loci…. Genome = many independent nucleotides x2 x5 x1 x4 x3 x6 Universal Q 1960 Multiple copies of the same Markov process 1970 1980 1990 2000 2010

  21. 3X109 {ACGT} 3X109 {ACGT} Genome alignment Humans and Chimps ~5-7 million years • Where are the “important” differences? • How did they happen?

  22. 9% 1.2% 0.8% 3% 1.5% 0.5% 0.5% Where are the “important” differences? How did new features were gained? Gorilla Chimp Gibbon Baboon Human Macaque Marmoset Orangutan

  23. “Junk” and ultraconservation Baker’s yeast 12MB ~6000 genes 1 cell The worm c.elegans 100MB ~20,000 genes ~1000 cells Humans 3GB ~27,000 genes ~50 trillions cells

  24. Archeological genomics reveal sequences of extinct species!

  25. From: Lynch 2007

  26. ENCODE Data intergenic exon intron exon intron exon intron exon intergenic

  27. 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?

  28. Ultimate experiment: sequence the entire genome of the evolving S. aureus Mutations Resistance to Antibiotics 8 9 10 11 12 13 14 15…18 1 2 3 4-6 7 S. Aureus found just few “right” mutations and survived multi-antibiotics

  29. Evolving E.coli in the lab given antibiotic pressure Independent lines develop resistance by playing with one critical protein in multiple ways Toprak et al. 2012

  30. Course duties • Exercises – 70% of the grade – (Evolutionary simulations/Theory) • Project Topics: Population genetics: models, drift, selection Species, phylogenies Probabilistic models for sequence evolution Comparative genomics: inferring selection Quantitative traits evolution Evolution of transcription regulation Cancer evolution Mathematics: Markov processes, algorithms for probabilistic inference, some statistics Introduced without assuming much prior knowledge, buy may require work to understand..

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