Equivalent competence phenotypes?

I am working on the Discussion of our competence manuscript and am trying to think about whether all DNA uptake phenotypes might be functionally equivalent. This means that cells are equally happy if they each take up 100 molecules or only one molecule. Given what we know about competence I don't think that this is the case but we evolutionists like to consider neutral explanations along with selective ones. So here is the paragraph:

"An additional ultimate cause of variation in competence is that the majority of the variation is functionally neutral. This would be the case if has been beneficial for cells to take up some DNA with the precise quantity being unimportant. Under this scenario, the high DNA uptake phenotype of strain Rd would be functionally equivalent to the low DNA uptake phenotype of strain Eagan. If we assume that all cells in a culture are competent then in our assays each Rd cell took up an average of 89 molecules of donor DNA whereas in Eagan each cell took up only one molecule of DNA. Each double-stranded donor molecule was 444 nucleotides (222 bp) so Rd and Eagan would have taken up 444 x 44 = 19,536 and 444 x 1 = 444 nucleotides, respectively. Considering the genome sizes of these bacteria are greater than 3.6 x 10^9 nucleotides (1.8 Mb), the DNA taken up by both strains would contribute little to whole genome replication. However, DNA is taken up by most strains when replication is needed for DNA repair and not whole chromosome copying. Depending on the extent to which cells need to use DNA replication during repair, every nucleotide taken up may help so the differences in competence between strains are likely to have a strong influence on the function of competence."

I am trying to think of obvious flaws in this reasoning. I am sure they are there but my brain is tired today.

A forest of trees

How much recombination occurs in H. influenzae? Does more recombination occur in strains that are more competent?

The answers to these questions can be partly addressed by inferring the phylogenetic relationships of H. influenzae strains, something that I have been working on for a few months now. The first way I did this was to take different sized fragments of the genome, align the same fragment from all strains, and infer a topology for each fragment. When I determine which clades all trees topologies have in common, I find that none of the clades are present in a majority of topologies. What this means is that if we take a the same section of the genome from each strain, the evolutionary history of this section will differ from another section. Recombination is a likely culprit here and statistical tests I've done so far do support recombination.

But there is a problem with this consensus tree approach. Not all of the genome fragments are the same length. This means that some topologies are based on an alignment that is >200 kbp while others are based on an alignment that is <200 bp. Clearly, the topology inferred using the lengthier dataset should be weighted more than the topology inferred using the shorter dataset. But weighting individual topologies when determining a consensus tree is not possible, as far as I know (if anyone knows differently, I would love to hear about it).

To get around this problem concatenating all of the genome fragment alignments into one large alignment can be done. I have done this and am currently inferring the topology using different methods such as parsimony, distance, and maximum likelihood. The parsimony and distance analyses produce a well-supported fairly resolved phylogeny but the two methods do not agree on all clades. Now I am waiting for the outcome of the maximum likelihood analysis that may take a few days, given the alignment is over 1.8 Mbp. If all three methods produce different topologies I suppose I can believe the clades all of them agree upon, if there are any. Then I can have confidence in these clades and use them to map competence onto the phylogeny. But I am also using a completely different method implemented in ClonalFrame that will exclude regions that have been influenced by recombination and infers the phylogeny based on the clonal frame only. We will see what all these methods agree upon, if anything. Nevertheless the outcome will be interesting.

Scientist doping

I am currently sitting in on an ethics class because NIH requires that I do so if I want to accept their funding. I had an ethics class as a graduate student and we talked about all of the usual things like stem cell research, gene therapy, experimentation with animals, etc. The ethics class I am in now is a bit different, which is nice because I can think about some new issues. The issue up for discussion yesterday was sports doping, which is when athletes take drugs to enhance their natural abilities. Most people find this unethical because athletes should perform using only what is available to them naturally. Enhancement is considered to be unfair and a sign of bad sportsmanship. But I couldn't help but compare the enhancement of athleticism using steroids to the enhancement of brain power/alertness using caffeine. Every day I enhance my alertness by drinking tea or coffee. Does this mean that I have an unfair advantage in science because I am doping my brain with caffeine? I would say no but only because the majority of scientists I know rely on caffeine themselves. Basically, we are all enhancing our natural abilities. But no one has a problem with this. So I guess caffeine enhancement of scientists is not as unethical as steroid enhancement of athletes. Why is this?

Recombination

In my previous post I eluded to the lack of resolution in the H. influenzae phylogeny. I have now attempted to use three different methods of phylogenetic reconstruction (maximum likelihood, distance, and parsimony) and, due to some characteristics of the datasets, only parsimony was feasible. From the whole genome alignments I had 101 alignment files. A phylogeny was inferred for each of these alignment files using parsimony and 100 bootstrap pseudoreplicates. Most clades in each of these topologies had poor bootstrap support. Then, a consensus tree was inferred from each of the 101 trees. Guess what? Absolutely no resolution of any clades, nothing, a "star" phylogeny.

Is it due to recombination? Probably. I tested for recombination using a parsimony based approach (Bruen et al. 2006. Genetics 172:2665) in each of the 101 alignment files (so within each alignment region, not between alignment files) and found that only 11 alignment files did NOT have evidence of recombination. The lack of recombination in all but one of these is probably due to the small size, most of the 11 were under 1 kb.

What does this all mean? Recombination was previously thought to be rare in H. influenzae, based on linkage between phenotypes and multi-locus enzyme electrophoresis types. But sequence data is changing what we know about the population structure of H. influenzae. MLST studies have already shown modest incongruence between gene trees and fancy Bayesian statistics have shown that the recombination rate is moderate. My work is now showing that recombination occurs often enough to obliterate phylogenetic signal. My next question is: is it mostly due to transformational recombination?