Background:

Professor Walter M. Fitch and assistant research biologist Robin M. Bush of UCI's Department of Ecology and Evolutionary Biology, working with researchers at the Centers for Disease Control and Prevention, studied the evolution of a prevalent form of the influenza A virus during an 11-year period from 1986 to 1997. They discovered that viruses having mutations in certain parts of an important viral surface protein were more likely than other strains to spawn future influenza lineages. Human susceptibility to infection depends on immunity gained during past bouts of influenza; thus, new viral mutations are required for new epidemics to occur. Knowing which currently circulating mutant strains are more likely to have successful offspring potentially may help in vaccine strain selection. The researchers' findings appear in the Dec. 3 issue of Science magazine.

Fitch and his fellow researchers followed the evolutionary pattern of the influenza virus, one that involves a never-ending battle between the virus and its host. The human body fights the invading virus by making antibodies against it. The antibodies recognize the shape of proteins on the viral surface. Previous infections only prepare the body to fight viruses with recognizable shapes. Thus, only those viruses that have undergone mutations that change their shape can cause disease. Over time, new strains of the virus continually emerge, spread and produce offspring lineages that undergo further mutations. This process is called antigenic drift. "The cycle goes on and on-new antibodies, new mutants," Fitch said.

The research into the virus' genetic data focused on the evolution of the hemagglutinin gene-the gene that codes for the major influenza surface protein. Fitch and fellow researchers constructed "family trees" for viral strains from 11 consecutive flu seasons. Each branch on the tree represents a new mutant strain of the virus. They found that the viral strains undergoing the greatest number of amino acid changes in specified positions of the hemagglutinin gene were most closely related to future influenza lineages in nine of the 11 flu seasons tested.

By studying the family trees of various flu strains, Fitch said, researchers can attempt to predict the evolution of an influenza virus and thus potentially aid in the development of more effective influenza vaccines.

The research team is currently expanding its work to include all three groups of circulating influenza viruses, hoping that contrasting their evolutionary strategies may lend more insight into the evolution of influenza.

Along with Fitch and Bush, Catherine A. Bender, Kanta Subbarao and Nancy J. Cox of the Centers for Disease Control and Prevention participated in the study.

Introduction to Bayesian Analyses

Paul Lewis, from EEB department, is one of the pioneers applying a Bayesian framework to the analysis of molecular data. His lecture notes for his Introduction to Bayesian Phylogenetics are here (about four hours of lecture). Essentially, the Bayesian approach tries to assess the probability of a model, bipartition or range of a parameter value - in contrast to ML, which assesses the probability of the data given a model.

It has been shown that under some conditions the biased sampling of tree and parameter space converges on the posterior probability. The approach most often used in recent months is Markov Chain Monte Carlo sampling. The principle is illustrated by a little program that Paul Lewis wrote called MCRobot. This little robot runs around in two dimensional space over which different distribution can be defined. The walk of the robot is biased in a way so that probability to find the robot in a place is proportional to the defined distribution.

MCRobot demo - you are welcome to play yourself.

MrBayes is doing the same as the MCRobot, but it walks around in tree and parameter space. For each place it visits, the program calculates the likelihood. The decision to take or reject a step is based on the likelihood. From the evaluation of all the trees and parameters visited (minus the burn-in phase), one can calculate the posterior probabilities of trees and parameters.

Exercise1:

The goal of this exercise is to learn how to use MrBayes to reconstruct phylogenies.

1. Save testseq1c.nex. This is the dataset of vacuolar ATPase A subunit you used earlier, but in the NEXUS format that MrBayes reads. Start MrBayes.

2. At the MrBayes command prompt type "execute testseq1c.nex". This will load the data into the program.

3. Type the following commands one by one:
   
   

   lset aamodel=jones
   mcmcp samplefreq=50 printfreq=50 nchains=2 startingtree=random
   mcmcp savebrlens=yes filename=testseq1c
   mcmc ngen=20000
   sump filename=testseq1c.bp
   
       

   "lset" sets amino acid substitution model to be JTT (Jones, 1992). "mcmcp" sets parameters for Markov Chain Monte Carlo: we set to sample every 50 generation, to print results to a screen every 50th generation, run 2 chains simultaneously, start with random tree, save branch lengths and outfile name will be testseq1c. "mcmc" actually runs the chain, and we set it to run for 20000 generations. After the run is finished, "sump" command will plot the logL vs. generation number, that allows to detect the burnin.

4. Type "sumt filename=testseq1c.t burnin=20", where you need to substitute burnin value with the number you obtained in the previous step of the exercise (Note: that burnin values is the # of generations divided by 50, since we sample every 50th generation). This command creates a consensus tree, and shows posterior probabilities of the clades.

  
  begin mrbayes;
   set autoclose=yes;
   lset nst=2 omega=estimate enforcecodon=yes omegavar=ny98 inferpossel=yes;
   mcmcp samplefreq=100 printfreq=100 filename=possel2;
   mcmc ngen=60000;
   quit;
  end;