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

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Presentation on theme: "Genome Evolution. Amos Tanay 2009 Genome evolution Lecture 12: epistasis and the evolution of gene regulation."— Presentation transcript:

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

2 Genome Evolution. Amos Tanay 2009 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

3 Genome Evolution. Amos Tanay 2009 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.

4 Genome Evolution. Amos Tanay 2009 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 genotype Diverge due to divergence of any of multiple loci in the genome Conserved due to coordinated divergence of multiple loci in the genome

5 Genome Evolution. Amos Tanay 2009 After S. Carroll Phenotypic innovation through regulatory adaptation

6 Genome Evolution. Amos Tanay 2009 After S. Carroll

7 Genome Evolution. Amos Tanay 2009 Ancient and Recent Positive Selection Transformed Opioid cis-Regulation in Humans (Rockman, Plos Biol, 2005) Sequence evidence for positive selection Try to remember what can help us establish this? (e.g. divergence and polymorphisms) The human variant is indeed responding differently

8 Genome Evolution. Amos Tanay 2009 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?

9 Genome Evolution. Amos Tanay 2009 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 

10 Genome Evolution. Amos Tanay 2009 Epistasis Assume 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 Ab f(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 loci f(AB) < f(Ab)*f(aB): antagonistic loci AB AB + AB - How widespread is epistasis? Is it positive or negative in general? and how it affect evolution in general?

11 Genome Evolution. Amos Tanay 2009 Testing epistasis in viruses: directed mutagenesis Sanjuan, PNAS 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

12 Genome Evolution. Amos Tanay 2009 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 standard Plotting 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 means Effect 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)

13 Genome Evolution. Amos Tanay 2009 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

14 Genome Evolution. Amos Tanay 2009 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 parameters stem stacking energy (adding a pair to a stem) bulge loop length interior loop length hairpin loop length dangling nucleotides and so on. Pseudoknots (breaking of the basepairing hierarchy) are typically forbidden:

15 Genome Evolution. Amos Tanay 2009 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)

16 Genome Evolution. Amos Tanay 2009 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

17 Genome Evolution. Amos Tanay 2009 EvoFold: considering base-pairing as part of the evolutionary model Whenever we discover compensatory mutations, the prediction of a functional RNA becomes much stronger.

18 Genome Evolution. Amos Tanay 2009 Compensatory mutations in proteins? PDB structures Homology modelling 3-Alignments Pairs of interacting residues RatMouseHuman Choi et al, Nat Genet 2005 Find pairs of mutations in interacting residues (DRIP) Coupled: occurring in the same lineage Uncoupled: occurring in different lineages

19 Genome Evolution. Amos Tanay 2009 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.

20 Genome Evolution. Amos Tanay 2009 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 species Orthologous stripe 2 enhancer reporters in a melanogaster embryo

21 Genome Evolution. Amos Tanay 2009 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 not functional in mel. Sequence conserved Sequence not conserved May reflect compensation May reflect trans- diverg All conserved

22 Genome Evolution. Amos Tanay 2009 Species-Specific Transcription in Mice Carrying Human Chromosome 21 (Wilson et al. 2008) Duncan Odom and co-workers introduced human chromosome 21 into mouse cells Using ChIP they showed that most binding sites (of enhancer mostly) were remain active as in human cells – suggesting they are determined in cis.

23 Genome Evolution. Amos Tanay 2009 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-regulation Is advantageous Disruption of regulation Is deleterious Regulation Scheme 1 Regulation Scheme 2 Rugged evolutionary landscape

24 Genome Evolution. Amos Tanay 2009 Cis-elements underlying conserved TMs 32 genes P< S. cerevisiae S. Pombe 114 genes P< S. cerevisiae S. Pombe 45 genes P< Ribosome biogenesis S. Pombe S. cerevisiae S phase S. pombe 7 genes P<10 -9 S. cerevisiae Amino acid met.Ribosomal Proteins

25 Genome Evolution. Amos Tanay 2009 Phylogenetic cis-profiling with 17 yeast species A. nidulans S. bayanus S. cerevisiae K. waltii A. gossypii S. castellii N. crassa S. pombe C.albicans S. kluyverii Y. lypolitica D. hansenii K. lactis C. glabrata Putative Orthologous Module (POM)

26 Genome Evolution. Amos Tanay 2009 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 MCBHAP2345GCN4 S phaseRespiration 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

27 Genome Evolution. Amos Tanay 2009 RAP1Homol-DIFHL Rap1 emergence Homol-D loss Ribosomal Protein Module: Evolutionary change via redundancy Redundant mechanism Homol-D based

28 Genome Evolution. Amos Tanay 2009 Rap1 evolution in trans BCRTMybSilencingTA S. cerevisiae S. castelii K. waltii A. gossypii C. albicans N. crassa A. nidulans S. pombe H. sapiens New TA domain Co-emerged with Rap1 role in RP regulation

29 Genome Evolution. Amos Tanay 2009 Redundant cis-elements are spatially clustered: RP genes in A. gossypii 3’ 6bp Homol-D RAP1 5’

30 Genome Evolution. Amos Tanay 2009 Evolution of the IFHL element pombe nidulans crassa lypolityca albicans hansenii sacc. et al. Tandem duplication Conservation Reverse complement duplication Drift…

31 Genome Evolution. Amos Tanay 2009 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 PACTC ?

32 Genome Evolution. Amos Tanay 2009 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 Mating genes a2 22 Albicans Cerevisiae

33 Genome Evolution. Amos Tanay 2009 A transition of motifs is observed between Cerevisiae and albicans

34 Genome Evolution. Amos Tanay 2009 Innovation in  2 is observed along with the emergence of possible mcm2 interaction A redundant intermediate may have enable the switch

35 Genome Evolution. Amos Tanay 2009 Ihmels Science, 2005


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