1. PHYLOGENETICS CONTINUED TESTS BY TUESDAY BECAUSE SOME PROBLEMS WITH SCANTRONS

2. consensus tree can also ask equally- supported trees (equally parsimonious, equal likelihood) how well they all support same nodes doesn’t have to involve subset of data like in bootstrap may summarize the stable parts of tree across 2+ trees bacde bacde bacde

3. CONSENSUS phylogeny is not fully resolved where there is disagreement among equally-ranked trees blue indicates dinosaurs with bifurcation of neural spine in vertebrae http://svpow.com/papers-by-sv-powsketeers/wedel-and-taylor-2013-on-sauropod-neural-spine- bifurcation/

4. support for the method do we believe phylogeny reconstruction works? need to test it against a known history (fish(salamander(bird(mouse,human)) we feel pretty strongly about experimental phylogenetics uses virus evolution to go one step further

5. experimental evolution growing T7 phage on E. coli plates; speed up mutation process by adding mutagen 40 generations 40 generations 40 generations

6. experimental evolution so phylogeny is known, and ancestral strains can be kept in freezer sequence part of DNA and use parsimony, likelihood, and other approaches consistently got the right (TRUE) answer! can also track “traits” on this tree, e.g. changes in growth rate and plaque size on E. coli plates (and check against actual ancestors)

7. # DNA mutations on this branch Text: “Because constructing phylogenies, and science more broadly, is often a process of evaluating evidence, scientists often test the effectiveness of the methodologies used to draw conclusions.” Rem: each branch is 40 generation s

8. case studies text goes through Origin of Tetrapods, Human phylogeny, Darwins finches, HIV show phylogeny, explain the likely mechanisms for pattern

9. well-supported phylogeny of rabies virus lineages, coded by host bat species

10. Phylogeny: how? Methods from Streicker et al (2010 bat rabies phylogeny paper) what gene region(s)? PCR of gene with primers how sequence data generated sampling effort

11. Phylogeny: how? Methods from Streicker et al (2010 bat rabies phylogeny paper) tree criterion: uses statistical model of DNA evolution every type of mutation happens at different rate, as observed mutations happen at different rates across codons in protein-coding genes are our data consistently supporting same phylogeny? outgroup comparison ‘roots’ phylogeny at ancestral node

12. Phylogeny: how? Methods from Streicker et al (2010 bat rabies phylogeny paper) coalescent: statistical model of how different evolutionary histories of drift, selection, migration, and change in population size are associated with DATA treat bat species as locations and ask how frequently migration of virus among species could explain pattern we see now? oh, no. now it is getting gnarly.

13. For RNA viruses, rapid viral evolution and the biological similarity of closely related host species have been proposed as key determinants of the occurrence and long-term outcome of cross-species transmission. Using a data set of hundreds of rabies viruses sampled from 23 North American bat species, we present a general framework to quantify per capita rates of cross-species transmission and reconstruct historical patterns of viral establishment in new host species using molecular sequence data. These estimates demonstrate diminishing frequencies of both cross-species transmission and host shifts with increasing phylogenetic distance between bat species. Evolutionary constraints on viral host range indicate that host species barriers may trump the intrinsic mutability of RNA viruses in determining the fate of emerging host-virus interactions. analysis indicates rate of virus jumping from one host to another

14. so this study requires TWO phylogenies (virus and bats) CST: cross-species transmission

15. neutrality neutral: doesn’t affect fitness of organism compare mutations in protein coding regions: synonymous mutations do not change amino acid, nonsynonymous do if much of diversity is neutral (or nearly so), mutations will accumulate and fix (become a substitution) in populations regularly through time

16. “molecular clock” works for many genome partitions neutrality acts as our NULL HYPOTHESIS

17. different homologous genome regions have different rates, slower rates when more functional constraints remember: fossil record, biogeography/geology, mutation accumulation studies help us estimate substitution rate µ

18. isthmus closes via volcanic uplift ~3.5mya two locations - are they two populations? different allele frequencies, distinct clades on tree: yes compare cytochrome oxidase mtDNA gene: 7% divergence

19. d=2µt µ is the rate of mutations going to fixation (substitutions), under neutrality the mutation rate IS the substitution rate because selection doesn’t accelerate or halt or change probability of fixation here we know t=3,500,000 years, d=0.07 µ = d/2t = (0.07)/(7,000,000) = 1x10 -8 another way to put it, rate of divergence (2µ) ~2% per million years time(t), rate µ along 2 branches

20. what is our assumption in those slides about clock calibration? how would YOU test that? idea is any mutation is equally likely to become a substitution how have we divided (point) mutations up so far?

21. neutrality neutral: doesn’t affect fitness of organism compare mutations in protein coding regions: synonymous mutations do not change amino acid, nonsynonymous do if much of diversity is neutral (or nearly so), mutations will accumulate and fix (become a substitution) in populations regularly through time

22. synonymous is assumed neutral so we can ask if nonsynonymous substitutions happen at a different rate neutrality: nonsynonymous divergence (dN) = synonymous divergence (dS) rate rate, not number of mutations - remember many more ways for a mutation to be nonsynonymous than synonymous does dN/dS =1? (book, elsewhere often this is called kA/kS; adjusts for the “more ways” of nonsynonymy)

24. This is the dN:dS or kA:kS approach we have been discussing if kA:kS >> 1, change has been selected FOR if kA:kS << 1, change is generally BAD if kA:kS ~ 1 neutrality

25. positive selection: amino acid change is favored

26. functional constraints lead to high levels of homology: change is generally bad (purifying selection) region of high homology led to discovery of new functional region that influences mammalian heart disease