1. Genome Evolution. Amos Tanay 2012 Genome evolution: Lecture 12: Evolution of regulatory sequences

2. Genome Evolution. Amos Tanay 2012 Beyond Protein Coding Sequences Non coding fraction of the genome: E. coli : 12% Yeast : 27% Fly : 76% Human : 97.6% How biological functions of non-coding sequence can be defined?

3. Genome Evolution. Amos Tanay 2012 Sequence specific transcription factors Sequence specific transcription factors (TFs) are a critical part of any gene activation or gene repression machinery TFs include a DNA binding domain that recognize specifically “regulatory elements” in the genome. The TF-DNA duplex is then used to target larger transcriptional structure to the genomic locus. Lactose Repressor

4. Genome Evolution. Amos Tanay 2012 Sequence specificity is represented using consensus sequences or weight matrices The specificity of the TF binding is central to the understanding of the regulatory relations it can form. We are therefore interested in defining the DNA motifs that can be recognize by each TF. A simple representation of the binding motif is the consensus site, usually derived by studying a set of confirmed TF targets and identifying a (partial) consensus. Degeneracy can be introduced into the consensus by using N letters (matching any nucleotide) or IUPAC characters (representing pairs of nucleotides, for examlpe W=[A|T], S=[C|G] A more flexible representation is using weight matrices (PWM/PSSM): PWMs are frequently plotted using motif logos, in which the height of the character correspond to its probability, scaled by the position entropy ACGCGT ACGCGA ACGCAT TCGCGA TAGCGT 123456 A60%20%00 40% C080%0100%00 G00 080%0 T40%000060%

5. Genome Evolution. Amos Tanay 2012 In vitro TF binding energy is approximated by weight matrices Yeast Leu3 data (Liu and Clarke, JMB 2002) We can interpret weight matrices as energy functions: This linear approximation is reasonable for most TFs.

6. Genome Evolution. Amos Tanay 2012 s In-vivo TF binding affinity is approximated by weight matrices s Ume6 ChIP ranges 11.5 5.5 Average PWM energy Stronger binding Stronger prediction Tanay. Genome Res 2006 Cross-link and sheer ImmunoPrecipitation Chromatin ImmunoPrecipitation (ChIP)

7. Genome Evolution. Amos Tanay 2012 TF binding affinity is kinetically important, with possible functional implications Kalir et al. Science 2001

8. Genome Evolution. Amos Tanay 2012 TFs are present at only a fraction of their optimal sequence targets. Binding is regulated by co-factors, nucleosomes and histone modifications Heinzman et al. Nature Genetics, 2007)

9. Genome Evolution. Amos Tanay 2012 TFs are present at only a fraction of their optimal sequence targets. Binding is regulated by co-factors, nucleosomes and histone modifications Heinzman et al. Nature Genetics, 2007)

10. Genome Evolution. Amos Tanay 2012 Specific proteins are identifying enhancers Here are studies of p300 binding in the developing mouse brain (visel et al. Nature 2009)

11. Genome Evolution. Amos Tanay 2012 TFBSs are clustered in promoters or in “sequence modules” The distribution of binding sites in the genome is non uniform In small genomes, most sites are in promoters, and there is a bias toward nucleosome free region near the TSS In larger genomes (fly) we observe CRM (cis-regulatory-modules) which are frequently away from the TSS. These represent enhancers. A single binding site, without the context of other co-sites, is unlikely to represent a functional loci

12. Genome Evolution. Amos Tanay 2012 Discriminative scores for motifs So far we used a generative probabilistic model to learn PWMs The model was designed to generate the data from parameters We assumed that TFBSs are distributed differently than some fixed background model If our background model is wrong, we will get the wrong motifs.. A different scoring approach try to maximize the discriminative power of the motif model. We will not go here into the details of discriminative vs. generative models, but we shall exemplify the discriminative approach for PWMs. Lousy discriminator High specificity discriminatorHigh sensitivity discriminator

13. Genome Evolution. Amos Tanay 2012 Hypergeometric scores and thresholding PWMs PWM score threshold Number of sequences Positive True positive For a discriminative score, we need to decide on both the PWM model and the threshold. Hyper geometric probability (sum for j>=k is the hg p-value)

14. Genome Evolution. Amos Tanay 2012 Constructing a weight matrix from aligned TFBSs is trivial This is done by counting (or “voting”) Several databases (e.g., TRANSFAC, JASPAR) contain matrices that were constructed from a set of curated and validated binding site Validated site: usually using “promoter bashing” – testing reported constructs with and without the putative site Transfac 7.0/11.3 have 400/830 different PWMs, based on more than 11,000 papers However, there are no real different 830 matrices out there – the real binding repertoire in nature is still somewhat unclear

15. Genome Evolution. Amos Tanay 2012 High density arrays quantify TF binding preferences and identify binding sites in high throughput Using microarrays (high resolution tiling arrays) we can now map binding sites in a genome-wide fashion for any genome The problem is shifting from identifying binding sites to understanding their function and determining how sequences define them Harbison et al., Nature 2004

16. Genome Evolution. Amos Tanay 2012 Direct measurements of the in-vitro binding affinity of 8-mers and DNA binding domains (here just a library of homeodomains, from Berger et al. 2008)

17. Genome Evolution. Amos Tanay 2012 Profiling binding affinity to the entire k-mer spectrum provide direct quantification of in-vitro affinity (Badis et al., 2009) Heatmap of 2D hierarchical agglomerative clustering analysis of 4740 ungapped 8- mers over 104 nonredundant TFs, with both 8- mers and proteins clustered using averaged E-score from the two different array designs. 8-mers 104 TFs

18. Genome Evolution. Amos Tanay 2012 What kind of biological function is naturally selected? Discrete and deterministic “binding sites” in yeast as identified by Young, Fraenkel and colleuges In fact, binding is rarely deterministic and discrete, and simple wiring is something you should treat with extreme caution.

19. Genome Evolution. Amos Tanay 2012 The Halpern-Bruno model for selection on affinity According to Kimura’s theory, an allele with fitness s and a homogeneous population would fixate with probability: Assuming slow mutation rate (which allow us to assume a homogenous population) and motifs a and b with relative fitness s the fixation probabilities (chance of fixation given that mutation occurred!) are: If p represent the mutation probability, and  the stationary distribution, and if we assume the process as a whole is reversible then: We work on deriving the substitution rate at each position of the binding site, given its observed stationary frequency. We are assuming that the fitness of the site is defined by multiplying the fitness values of all loci. This means fitness is generally linear in the binding energy! (Halpern and Bruno, MBE 1998)

20. Genome Evolution. Amos Tanay 2012 The Halpern-Bruno model for selection on affinity Moses et al., 2003 The HB model is limited for the study of general sequences. When restricting the analysis to relatively specific sites, HB is not completely off

21. Genome Evolution. Amos Tanay 2012 The entire genome should behave like a mixture of background sequance and functional loci: So we can try and recover Q(E) and therefore F(E) from the maximum likelihood parameters fitting an empirical W(E) Testing the general binding energy – fitness correspondence While E(S) is approximated by a PWM, F(E) is unlikely to be linear Assume that the background probability of a motif a is P 0 (a). In detailed balance, and assuming the fitness of a at functional sites is F(a), the stationary distribution at sites can be shown to be: Mustonen and Lassig, PNAS 2005 If we collapse all sites with binding energy E (and hence the same F(a)=F(E(a)) Inferred F(E), is shown in Orange Expected and observed energy distribution in E.Coli CRP sites (left) and background (right) Comparison of CRP energies in E.coli and S. typhimurium (Hwa and Gerland, 2000-)

22. Genome Evolution. Amos Tanay 2012 TFBS evolution: purifying selection and conservation Similar function Neutral evolution Disrupted function Low rate purifying selection TF1 TF2 Altered function Low rate purifying selection TF1 CACGCGTACACGCGTT TF1 CACGAGTTCACGCGTT CACACGTTCACGCGTT Altered affinity Rate? Selection? TF1 CACACGTTCACGCGTT

23. Genome Evolution. Amos Tanay 2012 Kellis et al., 2003 Binding sites conservation

24. Genome Evolution. Amos Tanay 2012 Binding sites conservation: heuristic motif identification Kellis et al., 2003

25. Genome Evolution. Amos Tanay 2012 Analyzing k-mer evolutionary dynamics Instead of trying to identify conserved motifs try to infer the evolutionary rate of substitution between pairs of k-mers Start from a multiple alignment and reconstruct ancestral sequences (assuming site independence, or even max parsimony) Now estimate the number of substitution between pairs of 8-mers, compare this number to the number expected by the background model Do it for a lot of sequence, so that statistics on the difference between observed and expected substitutions can be derived

26. Genome Evolution. Amos Tanay 2012 Saccharomyces TFBS Selection Network Arcs: 1nt substitution RateSelection Normal Low neutral negative arc not enough stat Nodes: octamers conserved @ 2SD conserved @ 3SD node otherwise conservation Inter-island organization in the Reb1 cluster: selection hints toward multi modality of Reb1 Tanay et al., 2004

27. Genome Evolution. Amos Tanay 2012 Leu3 selection network log delta affinity 0.3 0.2 0.1 0 3210-2-3-4-5 High Affinity (K d < 60) Meidum Affinity (400 > K d > 60) High rate subs. Substitution changing high affinity to high affinity motifs Substitution changing high affinity to low affinity motifs Substitution rate

28. Genome Evolution. Amos Tanay 2012 A simple transcriptional code and its evolutionary implications AAATTT AATTTT AAAATT GATGAG GATGCG GATGAT CACGTG CACTTG ACGCGT TCGCGT ACGCGT All the rest TGACTG TGAGTG TGACTT TF 1 TF 2 TF 3 TF 4 TF 5

29. Genome Evolution. Amos Tanay 2012 The Halpren-Bruno model for selection on affinity The basic notion here is of the relations between sequence, binding and function/fitness Sequence Binding energy Function We argued that E(S) can be approximated by a PWM F(E) is a completely different story, for example: Is there any function at all to low affinity binding sites? Is there a difference between very high affinity and plain strong binding sites? Are all appearances of the site subject to the same fitness landscape?

30. Genome Evolution. Amos Tanay 2012 S. cerevisiae S. mikitae Simulation (Neutral, context aware) High affinity Low affinity ΔE.. ΔE.. KS statistics More tests for possible conservation of low binding energy sites

31. Genome Evolution. Amos Tanay 2012 More tests for possible conservation of low binding energy sites Tanay, GR 2006   Binding site conservation Conservation of total energy Reb1 Ume6 binding energy percentile Conservation score Cbf1 Gcn4 Mbp1 binding energy percentile Conservation score binding energy percentile

32. Genome Evolution. Amos Tanay 2012 Evolutionary dynamics of transcription factor binding (mammals) Schimdt et al. Science 2010 Shared binding loci: 4%

33. Genome Evolution. Amos Tanay 2012 Evolutionary dynamics of CTCF binding (mammals) Schimdt et al. Cell 2012 Shared binding loci: 24%

34. Genome Evolution. Amos Tanay 2012 Bradley et al. PLoS biology 2010 Evolutionary dynamics of transcription factor binding (flies) – correlates with the sequence