Komodo rocks!

Today we started using Komodo Edit as our editor for Perl.  It's much better than Mi:  better colouring (the numbers are all in red), more reliable indentation, and it catches syntax errors on the fly.  And, very cute, when I typed "if (" , it automatically added the second bracket ")" on the far side of my cursor, to make sure I remembered that I needed to close the (if condition) brackets.

Our most immediate goal is to transfer some of the features of our old/abandoned program into this new one.  This includes some basic/sensible features: reading the parameter settings for each run from a separate file, rather than changing the code of the program, and having a mechanism to neatly end the program and save the interim work when it's interrupted with a control-C.

There are also a couple of quite clever features specific to the issues our models raises.  One is when to print interim reports on the model genome's status.  Depending on the parameters being tested, runs can take anywhere from a few hundred to a hundred thousand or more cycles, with rapid change only at the beginning.  Rather than printing reports at a fixed interval, we have the reports printed whenever the number of cycles increases by a specified percentage.  This gives very frequent updates at the beginning of a run, and reports at increasingly long intervals as the number of cycles gets large.  

The other clever feature is how the model decides that the evolving features of the genome have reached an equilibrium.  This is checked by comparing the recent state of the genome (mean of some attribute in the most recent print interval) with the mean of the same attribute in the previous interval and over the whole run.  By using the increasing print interval as the unit of measure, we get sensitivity appropriate to the rate of change. 

No time for Perl today

But I did spend some time with a post-doc working on the Discussion of her manuscript. I had forgotten that last time we worked on it we tore the existing Discussion into shreds and came up with a new organization. So this morning I was discouraged to see that we had improved our Discussion out of existence, but our new organization is so much better that we soon had at least half of the text in place.

On the Perl side of things, a very helpful commenter (Neil) pointed out that finding the missing/extraneous curly bracket would have been easy if we were using an editor that highlights and validates syntax. We're using an editor called 'mi'. It lets us specify that our text is Perl, and uses colours to distinguish between different kinds of text (comments are red, text to be printed is grey, functions are green, operators are sort of purplish, 'while's and 'if's and 'my's are blue), but it doesn't sort out hierarchical stuff like the levels of brackets, and the levels of indentation keep going to hell (possibly my own fault). I'd love to hear about a better Perl editor for Macs, if any reader knows of one.

Who knew that 'while' loops can't be nested...

Substantial progress on the Perl model of USS evolution.

First the undergrad and I added the code that tallies up the scores of every sliding-window position in the genome. It didn't take us long to get it running (a few stray semicolons, etc.) However after now Keith's comment on yesterday's post I think there are more efficient ways to do what we've done.

Then we created code that does the recombination a completely different way, so each of the fragments being tested has its own chance to recombine (probability of recombination depends on its score). The code wasn't tricky, but getting it running took ages of tracking down stray curly brackets and discovering that I can't nest two 'while' loops (I changed one of them to an internal 'if' test).

I still need to add in the feature I came up with yesterday, that writes out the genome sequence and the tally of scores only at specified intervals, but now I have to dash off to a 'visioning education' meeting imposed on us by the administration.

Positive control progress on the USS model

The simplest version of our new Perl model of USS evolution has progressed to the state where it runs correctly. This afternoon I've been doing lots of runs, both with a 'positive control' version that replaces a random genome position with a single perfect USS core in every cycle, and with a test version that mutates random fragments and scores them for goodness of match to the USS motif, and then recombined the best-matched one back into the genome. Tomorrow the undergrad and I are going to create a modified version, to try a different way of having the fragments' scores determine whether they recombine with the genome.

With the positive-control version I've been examining the effect of changing the genomic mutation rate. If the mutation rate is zero, the only limit to USS accumulation is the way insertion of new USS disrupts existing USSs. (This happens only because each 10bp fragment is changed to a perfect USS before recombination, and so bears no relation to the original sequence at that position.) Not surprisingly, more USSs are present at equilibrium when the mutation rate is zero, and fewer when the mutation rate is 0.01 or 0.05 changes per position per cycle. The rate of increase in genome score is largely independent of the mutation rate. Because only a single USS is inserted per cycle, the number of cycles to equilibrium depends on the length of the genome.

Wait - good idea! I think we need to add some code to give us the frequency of each sliding-window score at the end of the run. This would let us make a histogram of how many USS-like sequences the genome has at the beginning of the run, and how many it ahs accumulated at the end. Basically, as the sliding-window is scoring match to the motif at each position, it should record the score in a tally (number of sites scoring 0, number scoring 1, number scoring 2, ...... number scoring 10). I could write some inefficient code to do this (barring about a thousand syntax errors - I really should go back and reread the first few chapters of Beginning Perl for Bioinformatics), but this sounds like something the undergrad might have learned an efficient way to do.

Did I learn anything else from the positive control runs? If the genome is very short the program runs very fast but the scores are noisy (no surprise there). I learned that I have no practical insight into how a sequence's USS 'score' reflects the quality of its matches to the motif - that's why we need the tally. I played around with the 'threshold' we use as a cutoff for insignificant matches to the USS consensus, but I think we can't really understand what this accomplishes until we have the score tally.

I also did some runs with the test version (not the positive control). The results of these mostly served to reinforced the importance of the genomic mutations. Under the present recombination system, USS can't accumulate in the genome because they mutate away faster than they're improved by recombination. I tried turning off the mutation of the genome, so that mutation only happens to fragments that are about to be scored for possible uptake. Even with this 'cheating', the genome's USS score crept up slowly and then plateaued at what looks (without the tally) to be a genome with only weak USS sites.

Model systems in evolutionary biology

I spend the weekend, with the rest of my lab, at the Evo-WIBO meeting of evolutionary biologists.  Over breakfast we got into a discussion of the role (non-role?) of 'model systems'.  I did my usual rant about how evolutionary biologists don't even understand the concept of model systems, but I'm wondering whether the problem is partly just the nature of evolution research.

Molecular biology, biochemistry, cell biology and physiology have made dramatic advances, largely because many of them work on the same organisms, so that the findings of one study can be directly used as the groundwork for more studies.  Evolutionary biologists (and ecologists) almost always work on different organisms, and although they publish lots of nice papers these rarely can be applied to studies by other research groups.  

This is partly tradition - a mark of academic independence in evolutionary biology seems to be choosing your own research system (organism+field site+questions of interest), but it might also partly arise from the nature of the field.  The process of evolution is intrinsically tied more to variation than to shared properties - natural selection acts on differences, not similarities.  So maybe choosing to work on different systems just looks like the sensible thing to do.

But it's consequences are unfortunate, because although every nice bit of research claims to have big-picture implications, the lack of transferability means we haven't really gotten anywhere.

Outline of the perl program

(in response to good advice in the comments)

Below is just a list of the main sections of the program, in its present 'test' incarnation.

MAIN PROGRAM:

1.  Get parameter settings from a file (except it doesn't, the settings are hard-coded in this version).

2. Create a random-sequence 'genome' of the specified length and base composition.

3.  Simulate a set number of cycles (presently 100), each consisting of genome mutation, fragment creation, mutagenesis and scoring, and recombination.    

     3A.  Mutate the genome by randomly changing bases with a specified probability.  (This step should be later in the cycle, not here.)

     3B.  Select a specified number of segments of the same lengths, from random positions in the genome.  This will represent fragments in the external gene pool.

     3C.  Record each fragment's sequence and 5' end position.

     3D.  Mutate each fragment's sequence.

     3E.  Score each fragment's sequence for goodness of match to the uptake sequence motif, using a sliding window.  I think the sliding window scores are not being correctly cumulated.

     3F.  Choose the fragment with the highest score.  Put its sequence at the corresponding genome position, replacing the original genome sequence of this fragment.  (This is a simulated form of recombination by gene conversion.)  Any mutations of this fragment that occurred in step 3D will thus become changes in the genome sequence.

     3G.  Score the genome for how well its sequence matches the USS motif.

4.  At the end of all the specified cycles, stop and report.

-----------------------------------------------------

SUBROUTINES:

I.  Creating the original random genome sequence:  This is pretty simple; it just picks bases randomly, with probabilities specified by the base composition.  

II. Mutating the genome or fragment sequence:  This is more complex, partly because the mutations need to maintain the base composition (see subroutine III), but mainly because it does it a relatively non-obvious but more efficient way.  It first decides how many mutations to make, by dividing the genome length by the mutation rate and taking the integer value.  (Oops, this will only work if the genome or fragment is big enough to get more than one mutation per cycle. The 'test' version has only a 100nt genome and a specified mutation rate of 0.001, so it has a real mutation rate of zero.)   The subroutine then randomly chooses positions for this number of mutations, and makes the mutations at these positions.

III. Doing the calculations for the mutagenesis probabilities:  This creates arrays holding the mutation probabilities for each base (A or G or C or T) to mutate to each other base.

I've got to stop this and work on my course's final exam for a while.

Still reading code

I'm working my way through the undergraduate's Perl code, annotating it with detailed comments to explain to myself what I think is going on.

I'm only about 30% through the code, but I think I've found a number of problems, one of them big.  Tomorrow morning I'll tie down the undergraduate and go over everything with him.  Or maybe tomorrow early afternoon - tomorrow morning I'm supposed to help one of the post-docs polish her talk for this weekend's Evo-WIBO meeting.

Reading code

Our Perl-programming undergrad has just sent me a copy of the latest version of his program simulation the evolution of uptake sequences by molecular drive.  So far I've gotten to about line 100 and found several trivial typos and one source of confusion (to me).  I had thought that the order of steps in each cycle was as follows:

1. Choose random fragments of a specified length from genome and mutate them (as if they came from different daughter cells).
2. Score each fragment for its match to the USS consensus.
3. Mutate the original genome according to the same procedure used on the fragments.
4. Replace the corresponding segment of the genome with the fragment that has the best USS score.

But the standard version of this code seems to instead do the following:

1. Mutate the whole genome.
2. Chose random fragments and mutate them (again).
3. Score each fragment for its match to the USS consensus.
4. Replace the corresponding segment of the genome with the fragment that has the best USS score.

So the fragments are getting mutated twice.

In actuality, this 'test' version of the code has a couple of steps commented out, and short-circuits the fragment-generation and mutation steps by simply specifying the sequence of every fragment (as a perfect USS).  I think this makes it a lot easier to confirm the the code that does the scoring is working as intended.  Tomorrow I'll sit down with the undergrad and go over it.

Why "Expelled" is a bad movie

The guy in the next office just got off the phone with a colleague, arguing whether atheists should rise up and express their views. Triggered by the pending visit of Richard Dawkins to UBC. This horrible movie is one of the reasons I think we should:

Expelled

Classes are over!

Let's see if I can get back to posting every day.  First let's see if I can remember what's been on the back burner.

The USS-evolution computer simulation is up and running, and the programming assistant will be able to spend more time working with me and the post-doc to get it doing what we want.  It's close, so I'm hoping for lots of advances and discoveries.  I've signed up to give a short talk about this at an upcoming meeting on sex and recombination (in Iowa, just before the big evolution meeting in Minnesota at the end of June).

The "How do USS constrain genome function" project with the out-of-town bioinformaticist is ready for its final polishing.  A couple of weeks ago she sent me an email which (I think) contains the final data, and it really shouldn't take long now to have the manuscript ready for submission.  I've signed up to give a short talk on this work at the evolution meeting.

The post-doc who's been analysing the variation in competence in a diverse set of H. influenzae strains now has a manuscript that doesn't need a lot more work.  She's going to give a short talk on this work at next week's Evo-WIBO meeting (evolutionary biologists in Washington, Idaho,British Columbia and Oregon).

Both of the molecular biology post-docs have data that has yet to be put into manuscripts (at least I have yet to see the manuscripts).  One has been analysing how CRP binds to recognition sites, and the other has been doing microarrays to find out how CRP and Sxy regulate genes in E. coli.

On the teaching front, I still have to:  fix up the final exam so its a valid assessment tool for our homework-research project as well as for students' understanding; grade 17 term papers that are evaluating intelligent design as a scientific alternative to natural selection; help the graders grade the other ~75 project reports; help our homework grader finish grading the last two homeworks, and prepare detailed keys for these; analyse and post the marks for the 'clicker' questions the students have been answering in classes; administer and help grade final exams for about 360 students; and get all the grades analysed and submitted.

And on the homework-research project front, I (and the wonderful teaching fellow I'm working with) still have to finish a proposal for a small grant to hire assistants to assess the quality of writing in papers and exams by this year's and last year's students; find someone with sufficient Excel skills to transform our clicker-collected survey data into something we can work with; read the literature (I'm hoping the teaching fellow will point me to the appropriate papers); analyse the data; and write the paper.

Insights from a visitor

We are blessed this week by a visit from a potential collaborator - a computer scientist who's done work on uptake sequences.  When we described our computer-simulation model of uptake sequence evolution, he quickly discovered a serious problem arising from the way we have set up the uptake bias function to work.

In our model, a pool of DNA fragments is created each cycle, and the fragment with the best uptake sequence score gets to replace the homologous segment of the genome whose evolution is being simulated.  The uptake sequence score is determined by using a sliding window to compare the fragment's sequence to the designated uptake sequence.  For a long fragment this score is expected to reflect the combined effect of multiple uptake sequences.  But at present the model is using short fragments, so the score is likely to just be that of a single uptake sequence. 

This means that, once the genome contains a 'perfect' uptake sequence at one position, a fragment homologous to that position is expected to always out-score any other fragments in the pool, and thus always be the one that replaces the resident sequence.  Thus one good match prevents the gradual evolution of other not-quite-as-good matches at other positions.

There's a different way to do the competition that doesn't have this problem.  Rather than having multiple fragments 'compete' for the best score, with the winner taking the only opportunity to recombine with the genome, we can have each fragment in the pool independently challenge the odds of being taken up.  Uptake of one fragment would not affect the chance of any other fragment being taken up in the same cycle.  

This will require some rewriting of the code. But it shouldn't be a big deal, and luckily our undergraduate programmer will have more time to work for us once exams are over at the end of the month.

Can I remember how our USS-evolution model works?

Today we're having the first lab meeting in weeks. (When my turn came around a few weeks ago I just kept canceling them, but now I'm starting to see the light at the end of the teaching tunnel.)  We're going to discuss an issue that's arisen in the USS-modeling work being done by an undergrad research assistant, but first I promised to introduce this project.  What can I remember (or rediscover by reading my old blog posts about it)?

The big goal is to simulate how uptake sequences accumulate in genomes of competent bacteria, under the combination of mutation pressure (a randomizing force) and biased uptake preferring fragments containing these sequences.  The model follows a single genome-sized sequence through repeated cycles in which 

1. Random segments of the genome are treated as if they were fragments in an external DNA pool released by descendants of the 'index genome'
2. These fragments are scored for quality of their match to the ideal uptake sequence.  The best fragment is chosen for the uptake step
   
3. In the conceptual meantime, the index genome itself undergoes random mutation, becoming the descendant index genome.
   
4. The chosen fragment's sequence replaces the homologous sequence in the descendant  index genome.
5. This recombinant sequence becomes the new index sequence and the cycle starts again at step 1.

There have been lots of issues to resolve along the way (how the mutation steps maintain the base composition of the sequence, how the uptake sequences are scored), but we finally have a program that runs.  It seems to be working correctly, but the undergraduate who's done most of the work tells me that it isn't causing any uptake sequences to accumulate.  He's quite a sophisticated undergraduate - he has a Biochemistry degree under his belt and is nearly finished a second degree, in computer science - and he's done a lot of statistical analysis to look for the expected accumulation.

I suspect that the problem is that so far the model is using inappropriate parameters (mutation rates too low or too high? uptake bias settings too weak or too fussy? numbers of cycles too short?).  Today's goal is to figure out what these might be.

Research about teaching

It's been so long since I posted that Blogger had a seizure when I clicked on 'New Post'.

I'm still struggling to find a few spare synapses to think about scientific research, but the teaching demands will begin to diminish soon. In the meantime here's a post about the only research I'm currently paying much attention to.

I'm teaching freshman biology to about 380 students, in two sections. With one of our wonderful teaching fellows (supported by UBC's Carl Wieman Science Education Initiative), I'm carrying out an experiment to find out how homework might improve students' understanding of the course material and ability to explain their understanding in writing.

It's been widely assumed, but perhaps never explicitly tested, that doing homework helps students understand course material. Int his experiment we're going to examine whether students whose homework required them to formulate their ideas in correctly written sentences, and who got detailed feedback on their errors, perform better on midterm and final exams than students whose homework required only that they recognize correct answers.

The course I teach (BIOL 121, Genetics, Evolution, Ecology) has no tutorials and no TAs, just graders for midterms and finals; it's never had homework. This term we've split the students randomly into two homework groups that both get weekly homework assignments with very similar content but different requirements. Each homework is built around a single theme; more of a 'case study' than an series of unrelated questions. Students are given some information, asked one or two questions, given a bit more information, asked more questions, etc.

Group B's questions are in formats that can be automatically graded by our BlackBoard course management system - mostly multiple-choice questions, with some matching and fill-in-the-blanks. Group A is asked many of the same questions, but they have to think up their own answers and write them out. If the question does not require writing (e.g. has a numerical answer) the students are asked to give a written explanation of their answer.

Group B students can check the grading of their answers through the online system but get no specific feedback about their errors. Group A students get individual feedback about both their writing errors and their content errors. Part of this feedback is a very detailed grading key that gives, for each question, a sample answer, a numbered list of points a good answer should contain and errors that should have been avoided, and often a reference to lecture notes, textbook pages, or other sources of clarifying information. For each student's submission, each answer that did not earn full marks is commented with numbers indicating which problems the answer contained. For example, Writing error A is 'grammar errors', and Content error 4a is 'misinterpreting pedigree symbols or relationships'.

At the end of term we will compare the performance of the two groups on both the midterm and the final exam. Both these assessments include questions with written answers, allowing us to evaluate both students' mastery of course material and their ability to write clearly and correctly. Students were given a pre-quiz at the first class, and some of these questions are repeated on the midterm or final, allowing direct before and after comparison.

To make sure students feel they have been treated fairly, the course grades will be normalized across the two groups before being officially submitted to the Registrar's Office. The group with the lower course mean will have its grades raised to match the mean of the other group. This seems to be having the desired effect. We had expected some students to protest the unequal treatment, complaining either that Group A had to work harder or that Group A was going to learn more, but this hasn't materialized.

Blogging on (30-year-old) peer-reviewed literature

I'm reading a 1979 paper about GTA (Yen, Hu and Marrs, Journal of Molecular Biology 131:157-168).  The authors devised a way to select for mutants that overproduced GTA, and used these to find out more about how the particles are produced and the DNA they contain.

The mutant they focused on produces enough GTA that culture supernatants transfers any one chromosomal marker to about 0.0001 to 0.001  of the cells in a recipient culture.  As each particle contains about 0.001 of the donor chromosome, and recombination of such short fragments is relatively inefficient, this means that the supernatant probably contains about as many particles as there are cells in the recipient culture.  That was probably about 10^9 per ml.

The mutant grew poorly, and the authors interpreted this as a consequence of increased cell lysis associated with the increased GTA production.  They maintained the strain by growing it in medium they had found to inhibit GTA production (PYE medium), and transfered it to a GTA-inducing medium (RCV) when they wanted GTA.  After this transfer they observed that after several cell divisions about 10-20% of the cells died at the same time that high levels of GTA became detectable in the medium.  

This is a very provocative result (too bad they don't show any data), because it implies that GTA production is very deleterious . I'd never heard of either medium; I wanted to find out what's in them but one of the disadvantages of reading old papers is that they and their references are often not available online.  But simply Googling "PYE RCV" led me to the recipes - apparently they're quite widely used.  PYE is just 0.3% peptone and 0.3% yeast extract, which makes it a slightly more dilute version of our old favourite rich medium LB.  RCV is a defined medium used for R. capsulatus photosynthetic growth; it contains 0.4% malic acid as the only carbon source, 0.1% ammonium sulfate, thiamine, and other salts specified in papers that Springer will show me for $32.

So cells make lots of GTA in rich medium but not in the very poor medium used for photosynthetic growth.  Hmmm...

But searching for the paper with the recipes for these media led me to an even older paper that I need to first read.  This is Marrs 1974, PNAS 71:971-973, and PNAS is on line all the way back to the beginning.  So I'll take a break to read this paper, and post on it before continuing.

More about GTA

Yesterday I found time to sit down with my colleague who works on the 'gene transfer agent' (GTA) of Rhodobacter capsulatus. This helped me sort out a few things that are known about this entity, and a few things that aren't.

Is GTA derived from phage? Almost certainly. My colleague's lab's recent work has shown that some of the genes needed for GTA production are homologs of known phage genes. Old work from Barry Mrrs' group also showed that supernatants of GTA-producing cultures contain particles that look like tiny tailed phages. However, no GTA- control cultures were examined, and these phage-like particles could be produced from a defective prophage unrelated to GTA.

GTA particles contain chromosomal DNA fragments about 4.5kb, but nothing is known about how the DNA comes to be packaged in these particles. This information is critical to understanding how evolutionary processes act on GTA.

Old Cot-curve and restriction analyses were consistent with the fragments being derived from random positions in the chromosome, but the resolution is very poor. The issue could be nicely resolved by isolating DNA from the particles and hybridizing it to Affymetrix chips. Unfortunately my colleague says that getting sufficient GTA particles is quite difficult, as yields are both very low and not very predictable. An attempt to find out whether the ends of the fragments are blunt or staggered was unsuccessful.

From an evolutionary perspective, the most critical missing pieces of information are probably whether GTA is always (or often) accompanied by the death of the producing cell, and whether genes allowing GTA production can be transferred by GTA. That's because, if the genes are anything more than accidents of evolutionary history, they must either enhance the fitness of the cells they are in or spread into new cells faster than they kill their present cells.

If cells can produce GTA without dying, they must have a way to pass the particles out through the cell membranes without destroying them. Some filamentous phages can be secreted by living cells, but I think the tailed phages GTA is thought to resemble escape only by lysing their hosts. The amounts of GTA produced are sufficiently small that this might entail death of only a tiny fraction of the culture.

And if GTA does kill its cells on the way out, GTA could persist of evolutionary time only if it either spread between cells like an infectious agent or greatly increased the fitness of its close kin. Neither of these seem very likely, but I'll post more about this later.



I've now got some old papers to read.

New microarray data

The post-docs have finished the first-pass analysis of how E. coli gene expression is affected by both the E. coli Sxy and the H. influenzae Sxy proteins. I suppose I shouldn't be surprised that it's more complicated than I had hoped. For example, unlike the situation in H. influenzae, in E. coli there are also groups of genes whose expression goes down when Sxy is present.

One complication is that these cells are probably seriously OVER-producing Sxy. Unlike H. influenzae, where we've only done arrays of cells expressing a single-copy sxy gene under its natural promoter, these E. coli studies used a sxy gene on a high-copy plasmid and under a highly inducible promoter. We know that prolonged expression of Sxy from this plasmid produces large quantities of denatured Sxy (in inclusion bodies) and we don't know the extent to which even the 30-minute expression used for the array studies might create a situation unlike that of natural sxy expression.

Thermodynamics of home heating

This isn't a real research post, but my friends/colleagues thought I was wrong when I explained this to them so I want to see what others think.

I live in a condo and my apartment is heated by electric baseboard units (call this heating electricity). Like everyone else I also use electricity to accomplish domestic tasks such as lighting, cooking and refrigeration (call this working electricity).

I argue that the inefficiency with which I use working-electricity processes in my home is irrelevant to my electricity consumption because all the electricity used by these processes ultimately becomes heat. This applies not only to 'wasted' energy such as the heat put out by light bulbs, but to the work I'm using the electricity for, such as the light itself. That's because work becomes heat; for example light becomes heat when it is absorbed by the surfaces it hits. Thus every watt of electricity I use for cooking or lighting sooner or later becomes heat, and as such proportionately reduces the amount of electricity I need to send directly to the heaters. In effect I'm getting my working electricity for free.

The argument doesn't apply to working energy that gets lost as light out the windows or sound through the walls or hot water down the drain. And maybe not to the energy equivalent of the information I'm transmitting to Blogger with this post, though I suspect that is somewhere between infinitesimal and nonexistent. But it applies to everything that happens in the apartment.

The argument also doesn't apply when the weather is warm enough that any heating needed is less than the heat produced by working electricity. And if the weather ever got hot enough that I used electricity for cooling, I'd be paying double for the energy wasted by my working electricity - e.g. once to run the computer and once to run the air conditioner to get rid of the heat.

And it wouldn't completely apply in winter if I was able to use natural gas or a similarly cheap energy source for heating. But, given that I'm stuck with expensive electrical heating, I console myself with the thought that all the rest of my electricity is free.

So, blogosphere, do you agree?

Gene transfer agent

A colleague's lab has been working on the molecular biology of the 'Gene Transfer Agent' (GTA) of the bacterium Rhodobacter capsulatus. He and I have very different ideas about the evolutionary function of GTA, and we plan to sit down together and work through our disagreements, maybe coming up with a synthesis as a review article. I haven't been paying close attention to GTA, and in this post I'm going to take the first step by summarizing what I think I remember about it (before I go back and read any papers).

The phenomenon: Cultures of R. capsulatus have been known for many years to produce small phage-like particles, each consisting of a protein coat surrounding a 3-4kb fragment of R. capsulatus DNA. These particles can be separated from the source culture and are able to introduce their DNA into other R. capsulatus cells, where it can recombine with the chromosome and change the recipient cell's genotype. The variety of genes that can be transferred suggests that the DNA fragments may be random segments of the source cell's DNA.

I read about GTA when I was in grad school in the 1980s and first becoming interested in the evolution of processes causing gene transfer. I was already coming to the heretical conclusion that bacterial gene transfer by conjugation and transduction occurs as accidental side effects of infectious processes, not because such transfer is beneficial to the cell. At that time only Barry Marrs' lab had worked on GTA. My supervisor, the phage biologist Allan Campbell, thought that GTA was probably produced by a defective prophage. I was working on a cryptic prophage at the time, and this made sense to me. GTA would then be a form of transduction, a side effect of activity of genes whose normal function is to package phage DNA so it can infect new host cells.

The genes: More recently my colleague's lab has identified the R. capsulatus genes responsible for production of GTA and has partially characterized their regulation. As I recall, these genes are in a couple of clusters that do resemble defective prophage but that also have some properties of normal genes. In particular, aspects of the regulation suggest selection for a cellular function. My colleague has also done some analysis of the distribution of the GTA-producing genes, and as I recall this was not consistent with a single acquisition of a defective prophage. He thus interprets his findings as evidence that the ability to transfer genes by GTA is beneficial to R. capsulatus, i.e. that GTA has evolved as a form of bacterial sex.

Questions that I think have not yet been answered, or whose answers I forget: Does the individual cell that produces GTA die, as phage-infected cells normally do? Do only a small fraction of cells in a R. capsulatus culture produce GTA? How many genes are specific to GTA production (have no other function in the cell)? Have phage-derived genes acquired cellular functions independent of GTA production? Does GTA production directly reduce fitness? Can the ability to produce GTA be transferred by GTA? How strong is the phylogenetic evidence?

Next steps: Perhaps we should start our collaboration by working our way through the GTA literature, starting with Barry Marrs' 1974 PNAS paper. This would have the advantage of giving us both the same foundation of facts and factoids (things that look like facts but later turn out to be wrong) to base our discussions on. At the same time we ought to read one or more papers that clarify the evolutionary issues. My "Do bacteria have sex" paper is an obvious choice but shouldn't be the only one.

I'll ask my colleague to read this post, and we can then set up a time for our first meeting and decide what we should read in preparation for it.

Sorry, wrong link

Here's the link to my TEACHING blog: RRTeaching

Sorry for the paucity of posts

The research side of my brain has been devoured by the looming need to teach introductory biology to 450 freshmen (two sections of 225). Last year at this time I was focusing on grant proposal writing, and so I let my teaching coast on the course preparation I'd done the year before (the first year I taught this course). This year I'm trying to make up for last year's neglect, and my brain is struggling to come up with concept maps and personal response system questions and stimulating homework assignments and lecture content that better matches our new learning objectives and classroom activities suitable for large lectures and ...

But I did spend much of the last couple of days working with one of the post-docs on her manuscript about the competence phenotypes of diverse H. influenzae strains. One of the issues that came up about the Discussion is why our standard lab strain is one of the most competent, rather than being more typical of average strains.

Our initial thought was that perhaps, over more than 50 years of lab culture, descendants of the original isolate had been gradually selected for higher and higher competence in lab transformation experiments. That is, each time a transformation was done, variants that had taken up or recombined more DNA would be enriched in the plate of transformed colonies. But such transformants do not replace the original lab stock, but become new lab strains with new names and new places in the freezer. The original strain has (I think) always been maintained as a frozen stock, with individuals occasionally replacing their depleted vials with a new culture grown from descendants of a previous one. Depending on the culture history int eh intervals between thawing the parental stock and freezing a new one, these cells are likely to have been variably but unintentionally selected for improved growth in broth or on agar, of for longer survival after growth had stopped. We have no particular evidence that the ability to take up DNA would have played a significant role in this selection.

But there are other explanations for why the Rd strain is so competent. First, it was not a completely random isolate. The original H. influenzae transformation paper (Leidy and Alexander 1952?) reports testing strains of different serotypes, with Rd being the most competent. Second, if this most-competent isolate had transformed poorly, H. influenzae might not have become the first model organism for studies of competence in gram-negative bacteria.

We'll need to concisely explain this thinking in or Discussion, as a reviewer is likely to raise the issue.