Genome evolution

Genome evolution Lecture 11: Selection in protein coding genes

Protein genes: codes and structure Degenerate code 1 2 3 codons Recombination easier? 3’ utr 5’ utr Introns/exons Conformation Epistasis: fitness correlation between two remote loci Domains

Identifying protein coding genes From mRNAs Spliced ESTs : short low quality fragments that are easier to get Using computational methods. Limited accuracy Using conservation or mapping from other genomes

Questions on protein function and evolution Identification: • Identify protein coding genes • Not completely resolved for new species, but with new technology this question is becoming technical (ChIP + RNA-seq = genes) Structure/Function: • Define functional domains • Highly important for understanding protein function • Which parts of the proteins are “important” (e.g., catalytic?) • Difficult since structural modeling is hard and context dependent Evolution • Identify places and times where a new protein feature emerged • Positive selection • Understand mutation/selection through codon degeneracy • Understanding processes of duplication and diversitification

The classical analysis paradigm Target sequence BLAT/BLAST Genbank CLUSTALW Matching sequences ACGTACAGA ACGT--CAGA ACGTTCAGA ACGTACGGA Alignment Phylogenetic Modeling Analysis: rate, Ka/Ks…

Basics: rates of substitution We observe two sequences The divergence D is the fraction of different amino acids We want to find the rate of replacement (or substitution) l l D So if we computed the divergence D of two sequence, we can estimate the rate And: Where L is the sequence length. Note that for small D’s, K~D, but for larger values, K takes into account multiple substitutions

Basics: rates of substitution - nucleotides a a G G A A With nucleotides, we cannot ignore mutations that eliminate divergence a a b b a b C T C T a a Jukes-Kantor (JK) Kimura Probability to have the same value after two branches of length t: So we can estimate the rate given the observed divergence d (note that k is 3 times the rate of any specific substitution):

Using universal matrices: PAM/BLOSSOM62 Given a multiple alignment (of protein coding DNA) we can convert the DNA to proteins. We can then try to model the phylogenetic relations between the proteins using a fixed rate matrix Q, some phylogeney T and branch lengths ti When modeling hundreds/thousands amino acid sequences, we cannot learn from the data the rate matrix (20x20 parameters!) AND the branch lengths AND the phylogeny. Based on surveys of high quality aligned proteins, Margaret Dayhoff and colleuges generated the famous PAM (Point Accepted mutations): PAM1 is for 1% substitution probability. Using conserved aligned blocks, Henikoff and Henikoff generated the BLOSUM family of matrices. Henikoff approach improved analysis of distantly related proteins, and is based on more sequence (lots of conserved blocks), but filtering away highly conserved positions (BLOSUM62 filter anything that is more than 62% conserved) S. Henikoff

Universal amino-acid substitution rates? “We compared sets of orthologous proteins encoded by triplets of closely related genomes from 15 taxa representing all three domains of life (Bacteria, Archaea and Eukaryota), and used phylogenies to polarize amino acid substitutions. Cys, Met, His, Ser and Phe accrue in at least 14 taxa, whereas Pro, Ala, Glu and Gly are consistently lost. The same nine amino acids are currently accrued or lost in human proteins, as shown by analysis of non-synonymous single-nucleotide polymorphisms. All amino acids with declining frequencies are thought to be among the first incorporated into the genetic code; conversely, all amino acids with increasing frequencies, except Ser, were probably recruited late. Thus, expansion of initially under-represented amino acids, which began over 3,400 million years ago, apparently continues to this day. “ Ultra-deep evolutionary inference should be treated carefully…… Jordan et al., Nature 2005

Molecular clocks and lineage acceleration • How universal is the rate of the evolutionary process? • Mutations may depend on the number of cell division and thus in the length of generation • Mutations depends on the genomic machinery to prevent them ( • Mutations may also depend on the environment • The molecular clock (MC) hypothesis state that evolution is working in a similar rate for all lineages Relative rate test: KOA – KOB = 0 ? Test: KCA – KCB O A B C

Different molecular clocks Cytochrom C: 5 substiutions per 100 residues per 100 million years Kim et al., 2006 PLoS genet Hemoglobin: 20 substiutions per 100 residues per 100 million years Fibrinopeptiedes: 80 substiutions per 100 residues per 100 million years in apes and primates

Analysis: rate variation • If our ML model include rate variation, we can use the inferred rates to annotate the protein • Same can be done by constructing a conservation profile, even if the model is simplistic. • Shown here are example from Tal Pupko’s work on the Rate4Site and ConSurf programs

Synonymous vs. non synonymous mutations • Degenerate positions of codon are evolving more rapidly – free from selection on the coding sequence • This provide us with a powerful “internal control” – we are comparing two different types of evolutionary events at the same loci, so all sources of variation in the mutational process are not affecting us. Given aligned proteins, we can count: MA – number of non-synonymous changes Ms – number of synonymous changes We then want to estimate: • Ka – rate of non-synonymous mutations (per syn site) • Ks – rate of synonymous mutations (per syn site) • Estimate V(Ka), V(Ks) • Comparing Ka and Ks can provide evolutionary insights: • Ka/Ks<<1: negative selection may be purging protein modifying mutations • Ks/Ka>>1: positive selection may help acquiring a new function • (statistics using, e.g., T-test) Chi-square test Average number of sites

From dN/dS to Ka and Ks Consider the divergence of synonymous and non synonymous sites separately. As discussed before, we can estimate the rates: A more realistic approach should consider the genetic code and other effects A codon model is defining a rate matrix over nucleotide triplets We can use various parameterizations, for example: We learn the ML parameters. Small w indicate selection For transitions For non-synonymous

Codon bias • Different codons appears in significantly different frequencies, which is not expected assuming neutrality • Bias is measured in several ways, most popular is the codon adaptation index: • Possible sources of bias: • Selection for translational efficiency given different tRNA abudnances • Highly expressed genes tend to have stronger codon adaptation indices • Sequence context mutational effects • E.g. CpGs are highly mutable • Selection for low insertion/deletion potential • Weak selection for codon bias should be stronger for genomes with larger effective population size. In some cases this is true Codon frequency divided by the frequency of the synonymous codon with maximal frequency

Positive selection in humans vs chimp Looking at trends for families of genes Kn vs Ks Significantly enriched functions/tissues Example Testis genes: P<0.0001 Immunity genes, Gematogenesis, Olfaction P<1e-5 Inhibition of apoptosis P<0.005 Sensory perception P<0.02 Nielsen et al., 2005 PloS Biol

Mcdonald-Kreitman test Outgroup Tb Possible neutral replacement mutations Possible neutral synonymous mutations Tw Deleterious mutations Expected ratio of replacement to synonymous fixed mutations Fixed Poly Replacement Expected ratio of replacement to synonymous polymorphic mutations Synonymous M. Kreitman

Mcdonald-Krietman test - example • Works by comparing Ka/Ks divergence between species and Ka/Ks diversity among species populations • Negative selection should make the divergence Ka/Ks smaller than the diversity Ka/Ks • Positive selection should drive the opposite effect chimp human Busstamente et al, Nature 2005

Reminder: the coalescent Past Theorem: The amount of time during which there are k lineages, tk has approximately an exponential distribution with mean 2N * (2/(k(k-1))) Present 1 2 3 4 5

Infinite sites model Theorem: Let u be the mutation rate for a locus under consideration, and set q=4Nu. Under the infinite sites model, the expected number of segregating sites is: Proof: Let tj be the amount of time in the coalescent during which there are j lineages. We showed earlier that tj has approximately an exponential distribution with mean 2/(j(j-1)). The total amount of time in the tree for a sample size n is: Mutations occur at rate 2Nu:

Infinite sites model Theorem: q=4Nu. Under the infinite sites model, the number of segregating sites Sn has Proof: Let sj be the number of segregating sites created when there were j lineages. While there are j lineages, we may get mutations at rate 2Nuj, and coalescence at rate j(j-1)/2. Mutations occur before coalescence with probability: k successes: It’s a shifted geometric distribution:

The HKA test (Hudson, Kreitman, Aguade) L loci are sequenced in populations A and B Each locus is supposed to be behaving as an infinite site locus Slow Number of segregating sites in locus i and population A and B (Polymorphism) Number of difference between two random gametes from A and B (Divergence) chimp human Fast Our null hypothesis is of neutral evolution for T’ generations with population sizes 2N and 2Nf, but starting from a single ancestral population of size 2N(1+f)/2 We do not know: chimp human We want to allow different loci to have different mutation rates Purifying Selection chimp human What is the expected divergence?

The HKA test (Hudson, Kreitman, Aguade) Number of segregating sites in locus i and population A and B (Polymorphism) Number of difference between two random gametes from A and B (Divergence) B A Coalescent in ancestral population Variance of Poisson variable Divergence Is a Poisson variable Variance of S with n=2

The HKA test (Hudson, Kreitman, Aguade) Number of segregating sites in locus i and population A and B (Polymorphism) Number of difference between two random gametes from A and B (Divergence) There are L+2 parameters that we should estimate. This can be done by solving the equations: Find Find Find Find

The HKA test (Hudson, Kreitman, Aguade) Number of segregating sites in locus i and population A and B (Polymorphism) Number of difference between two random gametes from A and B (Divergence) The goodness of fit can be expressed as: Significance is best tested using simulations (although we can assume normality and independence and use chi square/g-test with 3L-(L+2) degrees of freedom)

Population dynamics? (small turns bigger?) Selective sweeps? Mutational effects (gene conversion?) Example: Drosophila chromosome 4 Slow Collecting data from a target locus: Size, Polymorphism, divergence Collecting data from a control neutral locus: Size, Polymorphism, divergence chimp human Fast chimp human This data is unlikely given our model, why? Purifying Selection chimp human

Impact of local recombination rate In drosophila, we find strong correlation between recombination rate and the level of polymorphism: High recombination regions have high polymorphism Low recombination region have low polymorphism One possible reason is that high recombination makes mutation rate higher If this was the reason, we should have observe correlation of recombination and divergence big diff No diff But divergence and recombination are not correlated The explanation may therefore be more efficient selection in high recombination regions and hitchhiking Presgraves 2005 We indeed see high dN/dS in high recombination – more efficient fixation of beneficial mutations

Rat Mouse Human Compensatory mutations in proteins? PDB structures Homology modelling Pairs of interacting residues 3-Alignments Find pairs of mutations in interacting residues (DRIP) Coupled: occurring in the same lineage Uncoupled: occurring in different lineages So far these types of methods generated very limited results..(why?) Choi et al, Nat Genet 2005

Codon volatility • Volatility is the number/fraction of adjacent non-synonymous codons • Genes under positive selection may have increased volatility • Think about the distance from the stationary codon distribution • No need to align!! Plotkin et al, Nature 2004

Using extensive polymorphisms and haplotype data, recent good examples of positive selection: the analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus3, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation4, 5, in Europe; and EDAR and EDA2R, both involved in development of hair follicles6, in Asia. Sabeti et al, Nature 2007

Time resolution of different positive selection methods Sabeti et al, Science 2005

The Neanderthal genome – Green et al. 2010

Selective sweeps Selective sweeps in human will eliminate common human-Neanderthal variation

Genome evolution