Archive for June, 2009

Trekking in the Evolution Range

June 29, 2009

I just returned from a short trek through the Evolution Range in the California Sierra Nevada.  It’s a ruggedly beautiful landscape, and all the peaks are named for famous evolutionary biologists (Lamarck, Darwin, Mendel, Haeckel).  You can view my Flickr media here.

I think “evolution” is the theme of my 2009 summer, given my recent participation at the Evolution conference, my upcoming participation at Burning Man (the 2009 theme is “evolution”), and this recent wilderness trek in the Evolution Range.

Obscure notes for the future:

  • This year, patchy snow remained as low as 11,000 feet.  The switchbacks above Upper Lamarck Lake were snow-free, but the terraced plateau to Lamarck Col was mostly buried.
  • In the midday sun, the snow over Lamarck Col was slushy and we did not need an ice axe.  I suspect a morning climb (when the snow is icy) would be dangerous without axe and crampons.
  • The cross-country route through Darwin’s Canyon is straightforward, but the boulder-climbing can be exhausting.
  • There exist many excellent campsites below Darwin’s Bench before Colby Meadow.
  • This year, the mosquitos were active in Evolution Meadow, but they weren’t insufferable.  Given the cold temperatures and auspicious lack of wilderflower blooms, I suspect we experienced an early mosquito hatching and later weeks will have bigger swarms.
  • My favorite campsite in McClure Meadow is beside the trail, west of the ranger station, near the outflow of the meadow.
  • The best place to ford Evolution Creek is in Evolution Meadow, not at the official PCT crossing.
  • An awesome campsite exists in Piute Canyon, on a southern-facing cliff about 2 miles downhill from Hutchinson Meadow.
  • Although most climbers approach Pilot Knob from the eastern saddle, you can also climb from the southeast face (and avoid climbing the saddle).  I suspect the southast face offers more sand and smaller boulders than the eastern ridge.

a video postcard

June 27, 2009

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

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.

Evolution 2009: Day 1 roundup

June 13, 2009

I’m attending the Evolution 2009 conference in Moscow, Idaho.  Below are some notes from the first day.  There are eight separate lecture tracks, so it’s impossible for me to see everything.  I’m mostly attending lectures focused on phylogenetics, systematics, and molecular evolution. . .

This morning, I planned to hear Peter Turchin talk about “warfare and the evolution of social complexity.”  Unfortunately, I missed his lecture due to an unpublished schedule rearrangement.  Instead, I listened to talks on the subject on speciationAsegul Birand presented simulations which demonstrate species’ range affects speciation rates.  Marcus Kronforst characterized hotspots of genetic differentiation in Heliconius butterflies; specifically, Marcus showed that wing coloration patterns are adaptive traits that generate reproductive isolation.

Later, I attended a mid-morning session on phylogenetic methods. . .

Jennifer Riplinger (from Jack Sullivan’s lab) discussed the problem of model selection for maximum likelihood bootstrap replicates.  In theory, we should perform model selection for each bootstrap replicate; in practice, most people use the same maximum likelihood model for all replicates.  Jennifer examined the role of replicate model selection on CytB, 18S RNA, and COX1 sequences.  Her results show that model selection for individual bootstrap replicates is unnecessary and does not yield significantly different bootstrap values.  Jennifer makes a good point, but I would like to see her analysis repeated for simulated datasets, where the true phylogenetic partitioning is known.  Furthermore, everyone should be careful about placing too much trust in bootstrap values (see Douady 2003).

Randal Linder presented a software tool “SATe” to simultaneously align sequence data and estimate phylogeny.  Given the short time allowance (only 15 minutes!), I had a difficult time determining how SATe is different from ALIFRITZ or Bali-Phy.  Randal used the “SP” metric to show that SATe produces more accurate alignments than ClustalW, MAFFT, MUSCLE, or Prank.  I am unfamiliar with the “SP” metric, and I wonder if his analysis would yield different results if he used AMA — instead of SP — to measure accuracy.

Alethea Rea presented the “NeighborNet” method to infer phylogenetic networks (instead of trees).  This approach is useful when the true evolutionary history of homologous genes involves recombinant events and/or lateral gene transfer.

Jason Evans (of the Sulllivan Lab) talked about his approach for averaging models during phylogenetic inference.  Due to the short time constraint, I didn’t entirely understand his cost-based averaging method.  I think integrating uncertainty about the evolutionary model is an appealing phylogenetic problem, but I need to read Jason’s publication before I can say anything critical about his particular method.

Rachel Schwartz talked about error in phylogenetic branch length estimation.  Rachel used simulations to show that Bayesian branch lengths (estimated using Mr. Bayes) generally underestimate the true branch length, while maximum likelihood branch lengths generally overestimate the true length.  The underestimation/overestimation bias is magnified for “deep” internal branches.  In general — for a rooted tree — Bayesian branch lengths make old nodes older and young nodes younger.  On the other hand, maximum likelihood branch lengths make old nodes younger and young nodes older.  Overall, the bias is less-pronounced for maximum likelihood estimates, and therefore Bayesian branch lengths should probably be avoided.  Rachel’s talk was robust and comprehensive, and I look forward to reading the forthcoming publication.

Finally, I attended an afternoon symposium in which Michael Alfaro discussed a method (named Medusa) for integrating fossil information into phylogenetic estimates of birth/death rates.  Afterwards, Brian Moore (from John Huelsenbeck’s lab) presented a collection of Bayesian tools for estimating phylogenetic divergence times and diversification rates.

OK, that’s it for now.


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