Posts Tagged ‘Idaho’

Evolution 2009: Day 3

June 15, 2009

Once again, I saw too many talks to list them all.

In my opinion, today’s best session was titled “The Evolution of Molecular Function” with speakers Patrick Phillips, Jesse Bloom, and Joe Thornton.  This symposium presented — and then demonstrated — a “functional synthesis” approach to molecular evolution.

Patrick began by talking about the history of genetics: statistical genetics and Mendelian genetics fragmented into many subfields over the past seventy years (pictured below).

Each subfield asks a unique — but separate — question about genes (pictured below).  For example, population genetics explores how fitness is determined by the transmission of genes; whereas, molecular genetics explores how genes have effects on phenotype.  Ultimately, an interdisciplenary synthesis provides a holistic understanding of the interplay between genes, gene transmission, gene effects, phenotypes, and fitness.

In the spirit of this “functional synthesis”, Jesse Bloom explained how H1N1 flu virus gained resistance to Oseltamivir (a.k.a. Tamiflu).  Oseltamivir binds the neuraminidase active site, which inhbits H1N1 viral release from an infected cell.  It is suspected that Tamiflu resistance began in 2006; as of 2009, almost all H1N1 strains are Tamiflu resistant.  Resistance is conferred by the H274Y mutation.  By itself, H274Y reduces the fitness of H1N1; it was therefore believed that the H274Y mutation would not spread through the flu population.  Consequently, why did resistance to Tamiflu spread?  Jesse speculates — in general — that some nuetral mutations can increase protein stability, thus creating a “stability buffer” enabling fitness-reducing mutations.  For the case of H1N1 Tamiflu resistance, his hypothesis appears to be correct: Jesse revealed that the R194G mutation (a neutral mutation) compensates for the H274Y mutation, thus allowing H274Y to spread through the H1N1 population.

Finally, Joe Thornton talked about the evolution of steroid-hormone receptors.  Whereas Jesse’s previous talk highlighted the interactions of just two molecular mutations, Joe showed how historical trajectories of many mutations led to the incredible diversity and specificity of extant proteins which bind steroid-hormones.  Many of these mutations demonstrate Dollo’s Law, such that they cannot be undone without deleteriously affecting the protein.  For more information, see (1) Thornton, Nature Review Genetics 2004, (2) Bridgham et al. Science 2006, (3) Keay et al. Endocrinology 2006, (4) Ortlund et al. Science 2007, (5) Bridgham et al. PLoS Genetics 2008, and (6) Laskowski et al., Nature Review Genetics 2008.

Okay, that’s it for today

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Evolution 2009: Day 2

June 14, 2009

I saw too many talks today to comprehensively discuss them all.  Here are a few that stand out:

Matt Hahn discussed the correlation (or lack thereof) between protein sequence similarity and protein function similarity.  Although we have increasingly complex models of sequence evolution (using Markov Models, for example), we know almost nothing about how protein function evolves.  Matt raised three questions: (1) How fast does protein function evolve? (2) Can we correlate the rate of evolution for protein function to the rate of evolution for protein sequences? (3) Can we find evidence for differential rates of protein function evolution in different types of protein families? Given the short time constraint (15 minutes!), Matt did not conclusively answer any of these questions — but that’s not necessarily a critique of his lecture.  His hypothesis was that the rate of evolution for protein function should be slower in orthologs and faster in paralogs.  To test this hypothesis,  Matt gathered protein function annotations from the Gene Ontology Consortium and plotted this data against rates of evolution for protein sequences.  Surprisingly, Matt observed (1) orthologs appear to evolve faster than paralogs, and (2) there is no relation between the rates of sequence evolution and functional evolution.  Both of these results are surprising, but difficult to explain.  Obviously, Matt’s results depend on the accuracy of the Gene Ontology annotations, which are unlikely to be entirely accurate.  I think Matt is asking a set of questions that are critically important, but I don’t think accurate answers will be found until we develop a different method for classifying and measuring protein function.

Paul Hohenlohe discussed RAD sequencing with the Illumina Genome Analyzer II to measure genetic variance (as Fst) in stickleback populations.  (RAD sequencing is introduced by Selker et al., Genetics 2007).  Sticklebacks are ancestrally a saltwater fish with bony armor plates.  Sticklebacks colonize freshwater habitats; colonizing populations lose some — or all — of their armor.  Paul used RAD sequencing with Alaskan stickleback populations, and showed that population structures vary between the saltwater and freshwater populations.  Paul’s analysis of stickleback populations provides a compelling example of how RAD sequencing is a high-throughput method for population genomics.

Joe Felsenstein talked about “phylogenetic geometric morphometrics.”  Given homologous extant morphologies with a set of identified (x,y) coordinates, Joe first showed geometric techniques to rotate and translate the extant geometries such that they are “aligned” in an roughly analogous fashion to sequence alignment.  Next, given a phylogeny relating the extant morphologies, Joe discussed a model using Brownian motion to infer ancestral forms — i.e., an ancestral set of Cartesian coordinates.  I’m not a developmental biologist, so I can’t offer much critique of this method.  I’m curious how he plans to deal with missing data — i.e. extant morphologies with (x,y) coordinates that don’t appear in all descendants.

Finally, James Foster talked about “evolutionary computation.”  Specifically, any process which demonstrates replication, variation, and selection will necessarily demonstrate evolution.  James’ point is that evolution can take place on digital artifacts as well as biological artifacts.  He gave several examples of genetic algorithms applied to problems as far-reaching as ML phylogenetic estimation (Zwickl 2006) , electronic circuit construction (Koza 1985), and jet engine design (Rechenberg 1966).  I totally agree with James’ point that evolutionary computation is useful to solve a wide gamut of problems, but I’m afraid his point fell on many deaf ears at this biologically-focused conference.

Okay, that’s it for now.