Genome evolution: a sequence-centric approach

1. Genome evolution: a sequence-centric approach Lecture 13: epistasis: RNA, enhancers, networks

2. (Probability, Calculus/Matrix theory, some graph theory, some statistics) Tree of life Genome Size Elements of genome structure Elements of genomic information Simple Tree Models HMMs and variants PhyloHMM,DBN Context-aware MM Factor Graphs Probabilistic models Genome structure Inference Mutations DP Sampling Variational apx. LBP Models for populations Drift Selection and fixation Draft Parameter estimation Population EM Generalized EM (optimize free energy) Protein coding genes Inferring Selection TFBSs Today refs: Papers cited

3. 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 + - A B A B AB AB How widespread is epistasis? Is it positive or negative in general? and how it affect evolution in general?

4. Testing epistasis in viruses: directed mutagenesis 47 genotypes of vesicular stomatitis virus carrying pairs of nucleotide substitution mutations (filled) 15 genotypes carrying pairs of beneficial mutations (empty circles) Sanjuan, PNAS 2004

5. 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: 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 Effect is stronger when analysis is restricted to 59 loci with significant effect on fitness Mean is significantly higher than randomized means Results suggesting that epistasis tends to be positive (at least in these viruses and in this condition) Bonhoeffer et al, science 2004

6. 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

7. 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:

8. 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)

9. 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

10. EvoFold: considering base-pairing as part of the evolutionary model Whenever we discover compensatory mutations, the prediction of a functional RNA becomes much stronger.

11. Evolution of a regulatory module: eve stripe 2 in D. melanogaster and D. pseudoobscura mel pseudo While the two enhancers drive a conserved expression patter, we cannot mix and match them between species!Evolution therefore continuously compensate for changes in one part with changes in the other. Ludwig, Kreitmen 2000

12. Evolution of a regulatory module Orthologous stripe 2 enhancer reporters in a melanogaster embryo Eve staining in 4 species D. Melanogaster D. Yakuba The D. Erecta S2E is forming much weaker stripe in D. Mel. D. Erecta D. Pseudoobscura Ludwig,..,Kreitmen 2005

13. Sequence conservation and divergence in eve stripe 2 and around it D. Melanogaster Enhancer functional in mel. D. Yakuba Enhancer not functional in mel. D. Erecta Enhancer functional in mel. D. Pseudoobscura

14. Co-regulation Is advantageous Disruption of regulation Is deleterious Rugged evolutionary landscape Regulation Scheme 1 Regulation Scheme 2 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

15. Cis-elements underlying conserved TMs 114 genes P<10-151 32 genes P<10-29 S. Pombe S. Pombe S. cerevisiae S. cerevisiae Amino acid met. Ribosomal Proteins 45 genes P<10-56 S. Pombe S. pombe 7 genes P<10-9 S. cerevisiae S. cerevisiae S phase Ribosome biogenesis

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

17. Amino acid metabolism S phase Respiration Conserved cis-elements MCB HAP2345 GCN4 S. cerevisiae S. paradoxus S. mikatae • Conserved FM are sometime regulated by remarkably conserved cis elements • Conserved cis elements are bounded by conserved TFs S. kudriavzevii S. bayanus S. castellii C. galbrata S. kluyveri K. waltii K. lactis A. gossypii D. hansenii C. albicans Y. lipolytica N. crassa A. nidulans S. pombe Tanay et al. PNAS, 2005

18. 112 38 S. cerevisiae (133) 46 31 S. parad. (75) Homol-D based Homol-D loss 46 57 S. mikatae (88) 40 48 S. kudriavz .(94) 40 54 S. bayanus (118) 45 40 53 S. castellii (89) Redundant mechanism 21 45 29 C. glabrata (69) 29 32 30 S. kluyveri (61) Rap1 emergence 31 30 34 K. waltii (54) 17 35 K. lactis (75) 52 32 64 A. gossypii (73) 46 41 D. hansenii (73) 30 C. albicans (41) 51 53 Y. lipolytica (70) 46 N. crassa (67) 49 A. nidulans (72) 73 44 S. pombe (74) RAP1 Homol-D IFHL Ribosomal Protein Module: Evolutionary change via redundancy

19. S. cerevisiae S. castelii K. waltii A. gossypii C. albicans N. crassa A. nidulans S. pombe H. sapiens BCRT Myb Silencing TA Rap1 evolution in trans New TA domain Co-emerged with Rap1 role in RP regulation

20. Redundant cis-elements are spatially clustered: RP genes in A. gossypii 5’ 3’ 6bp Homol-D RAP1

21. Evolution of the IFHL element sacc. et al. Drift… hansenii Reverse complement duplication albicans lypolityca crassa Conservation nidulans pombe Tandem duplication

22. 187 157 ? S. cerevisiae (225) Evolution of the Ribosomal biogenesis module 159 175 S. parad. (215) 151 136 S. mikatae (187) 163 152 S. kudri. (196) 159 151 S. bayanus (195) 167 152 S. castellii (204) 166 180 C. glabrata (214) 157 110 137 S. kluyveri (178) 181 59 163 K. Waltii (230) 200 145 122 K. lactis (225) 171 163 122 A. gossypii (226) 152 126 D. hansenii (219) 159 51 C. albicans (214) 154 Y. lipolytica (208) PAC TC RRPE 79 132 N. crassa (193) 99 A. Nidulans (187) 83 S. pombe (196)

23. 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 sequence-specific DNA-binding proteins. In S. cerevisiae, a-type mating is repressed in a-cells by a2. In C. albicans, a-type mating is activated in a-cells by a2. In both species, a-cells mate with a-cells to form a/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 a2 has taken over regulation of thetype. a2 Albicans a2 Cerevisiae Mating genes Tsong et al. 2006

24. A transition of motifs is observed between Cerevisiae and albicans

25. Innovation in a2 is observed along with the emergence of possible mcm2 interaction A redundant intermediate may have enable the switch

26. Phenotypic innovation through regulatory adaptation After S. Carroll

27. After S. Carroll

28. Rockman, Plos Biol, 2005

29. Ihmels Science, 2005

30. 481 segment longer than 200bp that are absolutely conserved between human, mouse and rat (Bejerano et al 2005) What are these elements doing? Why they are completely conserved? 4 Knockouts are not revealing significant phenotypes.. Ahituv et al. PloS Biolg 2007

31. Population genetics do suggest ultraconserved elements are under selection Separating mutational effects from selective effect is still a challenge… Katzman et al., Science 2007