David

Von Mering et al. was a very interesting article.  It was also an excellent paper for tying back to the beginning of the semester. The various clades of bacteria were sectioned off to specific environments suggesting it is very hard for bacteria to completly change their environment and be succesful. It is possible, but as the authors put it it is a “long tail.”  I assume that is in reference to the tails of a normal distrobution, where the center is what is most likely and the tails are possible but highly improbable.

What I found most interesting was that the greatest amount of evolution was found to take place in the ocean.  This tied back to what learned early this semester about Pelagibacter ubique.  The species is capable of sustaining a greater amount of diveristy due to lack of drift if I remember correctly.  Presumbly, that could mean soil bacteria are harmed more by negative mutations.  It also make sence considering the bacteria that underwent the most mutations were on the surface of the ocean.  That would be the area that feels UV light the strongest.  More UV light means more mutations.  It would be interesting to see if bacteria on the surface of the ocean have the better photolyase and other DNA repair mechanisms.

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Theme and Variations in the Evolutionary Pathways to Virulence of an RNA Plant Virus Species

I figured out I was reading some of it wrong on the virus.  The section they were talking about was on the VPg of the virus.  I’m not entirely sure what a VPg is, but it from what I gathered on wikipedia it has something to do with RNA synthesis.  Looking back I should have looked up VPg before.  It was mentioned briefly in the introduction, but I completly missed it until i started rereading the paper.  I also should have read the materials and methods before the rest of the paper.  It made a whole lot more sense after that.

The VPg has been found to have an affect on virulence in past studies.  The goal of this study was to compare multiple regions and from the same host over a set length of time.   They ended up finding parallel evolution on a specific codon of the gene.  This resulted in increased virulence.

On a side note I finished up the math for the AUC’s.  Hopefully I calculated it correctly.  I’m not goingto give away the answer, but WOW on the final results. For anybody wondering, I’m pretty sure the SRP tied Dan for swim and swarm. The SRP didn’t change the results though.

Edit: I was wondering why AUC is better than using something like line of best fit and finding the best growth curve.  Granted I don’t fully know what the AUC means. . . just how to calculate it(hopefully).  I feel like a robot.

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The Velicer and Yu paper was very interesting.  M. xanthus uses both pili and fibrils, both of which are needed for swarming.  They knocked out a gene critical for pili production and as predicted swarming rates dropped significantly.  In the next step, they selected for better swarming bacteria in 8 different lines.  6 were done for 32 transfer cycles and 2 were done for 21 transfer cycles. 

The last two were the most important.  After 21 transfer cycles, both evolved swarming rates that were close to the wild type in relative fitness.  The other 6 were nowhere near wild type and were only slightly better at swarming than the original mutants.  They found fibril production was responsible for the swarming because when they inserted a dsp mutation (inhibits fibril production) in the two lines swarming ability dropped significantly. 

The take home from this paper is the fact that the two lines evolved the ability to swarm in a different way than the wild type.  The fact that it happened in two out of the 8 lines indicates that it may be a mutation that could occur in the wild under the right circumstances with some frequency. 

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I found the Turner Et al. papers to be very interesting read.  It took me a little time to get past the defective interfering particles part for some reason.  After it sunk in that that was not what was being talked about in the paper things made much more sense.   My only real complaint was that it was hard to figure out what where the numbers from the pay-off matrix came from.  Now that I understand where they came from the overall picture makes much more sense.  It was very interesting to see a situation where the environment selected for an evolution strategy that was not ideal.  They viruses were becoming selfish even though cooperation was the best alternative overall.

The Escape paper was also very fascinating.  The fact that they were both evolved to be more fit probably helped to show some better real world results, although I was surprised by the 6% fitness cost associated with the h marker of phi H2.  The leveler playing field resulted in mixed results.  When there are more cooperators at the beginning, defectors win and vice verca.  This showed a much more complex situation.

It was  very interesting to read about a theory that is opposed to the idea that the best long-term choice will always take over a population.  Evolution is not guided.  It is a series of random events that interact with the environment in new ways.  Because of that, I see no reason why a less than ideal evolutionary path could not be followed.    Short sighted gains can very easily take over long-term gains even when the long-term is more ideal.  This was clearly seen in the experiment.  It was better for the viruses to cheat off of each other even though cooperation would have worked best in the long run.

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I personally think the amount of diversity and complexity in such a system as described in Fukami Et al is amazing.  It is at first glance a simple system, but after closer examination many levels of complexity are seen.  Not only are there three ecotypes that take over specific niches, but the order and quantity are vastly important.

One thing I was wondering about was the boom-and-bust dynamic.  What exactly triggers it.  It was mentioned in the Fukami paper and after looking at the growth in the Rainey paper it appears to have happened there.  I did not know if anyone else knew mutch about it.

I have also been thinking about protists.  Bacteria have been used for models for sexual organisms in the experiments we have been looking at.  Protists grow very rapidly like bacteria do, and I was wondering if they had any limitations holding them back from being a good model.  I have also been trying to find out if any reproduce sexually. So far  I have not had any success in that regard.

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I’m glad I took biostat last semester. Statistics played a major role in Coopers paper. That is actually one thing I admired about the paper. Many times people will brush over the statistics behind their argument. Cooper did an excellent job of backing up what he said. Where ever relevant, he put in the statistical significance of what he was talking about. I found this to be much more effective placing the information it tables exclusively.
One thing that got me thinking was that the experiment found recombination is only useful when there is a high level of mutation. I always assumed organism that rely on asexual reproduction would be more reliant on mutation than those that use sexual reproduction. Apparently I was wrong. Eukaryotes usually have a mutation rate between 10^-4 to 10^-6 while bacteria have mutation rates at around 10^-8. That is a whole lot less. That goes allong with the findings of this experiment pretty nicely.

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Here’s my “Best of” blog for evaluation.

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I thought Travisano Et. al was a very interesting study. They developed an excellent way of tracking evolutionary patterns over time. While most study has been spent trying to view patterns of evolution over time it was very interesting to actually be able to compare ancestors to more recent samples. It is very hard to tell about evolution in present day bacteria since you can only compare relatives. The ancestors are long gone and there is no fossil record to dig up.

The major thing I liked about this experiment was the fact that you could actually see the changes that occurred. In all the other papers we looked at the scientists were comparing different species or strains to see how the related to each other, in many cases as an attempt to form a tree of life. In this paper they have started to make their own branch of the tree of life. I was curious about how the different strains evolved. I did not see it mentioned in the paper, but that might just be because I missed it but. I could tell the strains all followed a similar path, but I wanted to know if the way they accomplished this task varied greatly among the groups. That is to say, did they have changes on the same genes or were they completely different methods of adapting to the new environment. If followed long enough these divergences could show how different species form over time.

I shared the same doubts as Abe about it being to soon to be sure about cell size not being an adaptation. My reasoning was the same, but had been spurred by an article I read a few days ago. It was a New York Times article about a study that had been just published that followed E. coli in minimal glucose for 40,000 generations and counting. I think the article is about is actually a continuation of Travisano Et. al. Richard Lenski is involved in both and the 40,000 generation one has been going on since 1989 and is still going. Anyway, now the cells are twice the size of when the experiment was started. That difference tells me it must be an adaptation. They also divide 70% faster. It is very possible that 2000 generations is not enough time to see signs of an adaptation.

Heres a link to Lenski’s website, where he has all the data from the experiment. It’s virtually all numbers so I did not get much out of it.

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I saw this video on youtube. . . who knew that the ocean was not an environment.  The sad thing is that this might be real. The front fell off.Edit: After some searching I am fairly sure it was a comedian. It did not help that there were several Senator Collins in Australia 

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