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Whew! So another grant out-of-the way for now; another one almost done; and my own postdoc re-application in the works… A brief respite… Maybe it’s time to blog.

Last time (two months ago), I started showing some pictures from IGV showing our raw sequence data aligned to the Rd genome. Here, I’ll do yet another summary of our preliminary experiment, as we pitched it in our grant application, which will lead nicely into what I’d like to do next time… talk about alternatives to multiplexing DNA samples by barcoding…

So we’re studying transformational recombination in H. influenzae, where cells take up DNA from the media and incorporate it into their chromosomes. We think we have a decent model of the mechanism from studies in H. influenzae and other organisms:

(My figures here might have been a little degraded on their journey into the blog)
But until our little sequencing experiment, we could only infer the extent of transformational recombination of a chromosome based on the transformation and co-transformation frequencies of phenotypic markers. We’ve learned a lot just from obtaining four recombinant genotypes. Here’s what the experiment looked like in overview:
DNA from one isolate (NP NovR NalR) was incubated with competent cells of another (Rd), and transformants were selected. Two were NovR and two were NalR. We got sequence data from our collaborator for all four of these in a pool, two of them individually, and each parent (Rd and NP) individually. Here’s the figure we used to illustrate what our data looked like:
Hmm… probably that’s a low-resolution picture, but working from the bottom of the figure:
The lower panel shows the frequency of NP-specific SNP alleles across the Rd chromosome for the pool of four chromosomes. Blue dots at 25% indicate that 1 of 4 recombinants contained the donor-specific allele, while blue dots at 50% indicate that 2 of 4 recombinants did. The two red dots indicate the two selected markers (NovR and NalR), which as expected are at 50%.

In the upper panel, a zoomed view around the NovR-containing region is shown. The blue dots clearly define the donor DNA segments, but since there are overlapping donor segments, their appropriate assignment to different recombinants is unclear:
But because we also sequenced one of the NovR recombinants, the assignment of all the segments is made apparent. The green bars at the top of the figure show the donor DNA segments in Recombinant A, and so the donor segment spanning NovR in Recombinant B is unambiguously inferred.

Notably, there are several clustered donor segments in Recombinant A. This suggests that processes like mismatch repair may be disrupting larger original DNA fragments during recombination. For example in the upper panel of Figure 3 above, the area shown by the small purple circle appears to be a mismatch repair event around an insertional deletion difference between Rd and NP. Here is what that region looks like in IGV:
This IGV picture is showing our sequencing reads against the NP genome (the donor). The top track shows our Rd reads mapped to NP; the middle track show NP reads mapped to NP, and the bottom shows Recombinant A reads onto NP. I looked at the whole-genome alignment in this interval and found that the structural variation here is due to an insertional deletion: the alignment breaks and NP has 128 bp that doesn’t align with 52 bp of Rd.

Here is how I interpreted this event in the context of the larger NP donor segment:
Okay! Cool!

It’s going to take a while to fully parse this data, but more important is how we should go about collecting more. We certainly think we can increase our pool size, but as it is now, we can’t obtain “linkage” information from the pool. The obvious solution, barcoding individual DNA samples, presents monetary, technical, and computational problems. However, there may be another way… (continued...)

Mutagenic recombination?

Okay, this is pretty cool. I will probably discover that it’s just some artifact of the mapping, but digging into the transformant data some more reveals what appears to be a high number of mutations within recombined segments (alleles that have neither donor nor recipient identity)...

For this analysis, I used much more stringent criteria for calling SNPs, so that low quality SNP calls would not contaminate the result. This is particularly important here, since we might expect to get lower quality SNP calls in the recombinant segments, due to the relatively high divergence between the donor and recipient genomes and the limitations of current mapping algorithms.

For TfA, there were 802 unambiguous donor alleles and 19 high-quality novel alleles, while for TfB, there were 902 unambiguous donor alleles and 21 high-quality novel alleles.

The two plots below indicate the presence of unambiguous donor alleles in blue bars going to 1 (which defined the recombinant segments), and the presence of unambiguous mutant alleles in red bars going to -1. (Click to enlarge)

That looks pretty striking! Mutations are clearly clustered into the recombined segments!

A few of the "novel alleles" in the two genomes are shared. 4 of 6 are in the first overlapping donor segment, and the other two are outside the donor segments. It is still early to be too confident in this result, but still! It is very suggestive.

I’ve never really taken the supposed causal connection between recombination and mutation too seriously, since the evidence mostly seems correlative to me, but if this result holds up, I think it will be a uniquely clear-cut example of mutations induced by recombination.

Before being confident in the result, I need to map the data back to the donor genome, and cross-check the result. If that works, some simple PCR and traditional sequencing should readily confirm or refute this deep sequencing result. (continued...)

Recombinant Genomes: First Pass

We’ve now got deep sequencing data of donor, recipient, and transformant genomes. And it is, indeed, “deep”, at least in quantity. Here’s the post where I illustrate the details of how the sequenced DNA was obtained. There were five “lanes” of sequencing obtained, of which I will talk about the first four here:

Lane 1: Rd (the recipient genome)

Lane 2: 1350NN (the donor genome 86-028NP plus NovR and NalR alleles of gyrB and gyrA, respectively)

Lane 3: Transformant A (the genome of a NovR transformed clone)

Lane 4: Transformant B (the genome of a NalR transformed clone)

So I’ve done a first-pass analysis of the sequencing data using Maq as the mapping algorithm. I left everything at default settings, and so far have only analyzed the datasets with respect to the recipient genome, Rd.

The expectation is that when I map Lane 1 to Rd, there will be few differences detected by Maq; when Lane 2 is mapped to Rd, the bulk of SNPs between Rd and 86-028NP will be detected; and when Lanes 3 and 4 are mapped to Rd, the donor DNA segments transformed into the recipient background will be identified. For lanes 3 and 4, since we know the locus that we selected for donor information, we have controls for whether or not an appropriate donor segment was detected.

Before continuing, I must specify that this is a very preliminary analysis: I do not consider the quality of the SNP calls, beyond whatever Maq does to make the calls (and culling ambiguous SNPs, as described below). I have not mapped anything with respect to the donor genome. I have not considered any polymorphism between the strains other than simple single-nucleotide substitutions (since Maq only does ungapped alignment). I also missed regions of very high SNP density, since the Maq default will not map any read with >2 mismatches from the reference. Finally, I have only cursorily examined depth of coverage across each genome (it is ~500-fold on average, ranging from ~100 to ~1000 over each dataset).

However, even with these caveats, the approximate sizes and locations of transformed DNA segments were pretty clear...

Here are the number of “SNPs” called by Maq between each dataset and Rd:

Lane 1: 933 (Rd)
Lane 2: 30,284 (1350NN)
Lane 3: 1,870 (TfA-NovR)
Lane 4: 1,881 (TfB-NalR)

Rd versus Rd

The first obvious issue is that when the Rd DNA was mapped against the Rd genome sequence, Maq called 933 “SNPs”. What is all this supposed “variation”?

108 had “N” as the reference base
432 had an ambiguous base as the query

So 540 / 933 “SNPs” are easily explained artifacts-- either ambiguous positions in the complete Rd genome sequence reference, or ambiguous base calls by Maq from the Illumina GA2 dataset.

The remaining “SNPs” may also be persistent sequencing/mapping artifacts, or they may be true genetic differences between the “Rd” we sequenced (our lab’s wild type) and the original DNA sample sequenced back in the 1990s.

To simplify matters I culled any position called a “SNP” between Rd and Rd, as well as all other ambiguous positions, from the remaining datasets.


Rd versus 1350NN

Before turning to the tranformants, I used Lane 2 to make a list of detectable SNP positions between the donor DNA and the recipient chromosome. Of the 30,284 “SNPs” detected by Maq, 29,002 were neither in the Rd SNP set nor had an ambiguous base in either the reference or query.

Note that I am not using SNPs identified by comparison of the two complete genome sequences, but rather those that were unambiguously determined by this sequencing experiment. Rd remains the only reference genome I have used.

I used this set of SNPs as the set of “Donor-specific alleles” to map transforming DNA segments.

The transformants

To identify donor-specific alleles in the transformants, I took the intersection of the “Donor-specific alleles” and the unambiguous SNPs identified by Maq for the two transformants, yielding the following number of SNPs in each transformant:

TfA: 890
TfB: 975

This suggests that about 3.0-3.5% of each transformant genome consists of donor DNA. This value is consistent with what we might expect, based on the co-transformation frequency of the two donor markers into the recipient genome when I did the original transformation that produced the sequenced clones.

Here are plots of the TfA and TfB genomes (genotype 0 = recipient allele, genotype 1 = donor allele):
(Note that images can be enlarged by clicking)

This makes me happy. I was kind of hoping our thinking was wrong and that there’d be all kinds of kookiness going on, but in many ways, having our expectations met is a vastly better situation, since it means that the designs of our other planned experiments are probably sound.

Note that the right-most "donor segment" represents only a single "donor-specific allele" that is identical in both recombinants, as well as being surrounded by Rd vs. Rd SNPs. It is highly likely that this singleton is an artifact. All other donor segments are supported by many SNPs, including the overlapping segments ~200,000 bp. This latter shared segment may suggest a hotspot of recombination.

Control loci

The following two plots zoom in on the segments containing the control selected loci. The first has red lines bounding the gyrB gene in TfA, while the second has blue lines bounding the gyrA genome in TfB. Also in each plot, the masked positions (those that were left out due to ambiguity or presence in the Rd vs. Rd comparison) are show (for the first, in orange; for the second, in grey):
Within each marker gene, there are a few recipient-specific SNPs. These have to do with the fact that the PCR fragment I used to add the NovR and NalR alleles of gyrB and gyrA contained recipient SNPs and some of these ended up in the donor genome.


Okay! That’s almost it for now. There’s a looong way to go, but happily I suspect that there is indeed biology to learn even from these “meagre” couple of genome sequences.

My next task will be to account better for where I am blind. I used stringent criteria to determine allele identity here. I am quite confident in what was found, but I’m not sure sure about what I didn’t find. That is, I suspect false negatives, but not false positives, based on how I’ve done this so far. (continued...)

Mismatch repair versus Segregation

Things have gone swimmingly with my strain construction plans, and indeed today I am extracting DNA that will presumably be sequenced. To recap, I made a couple of clinical isolates (86-028NP and PittGG) resistant to novobiocin (NovR) by transforming them with a bit of left-over NovR allele of the former postdoc. I then isolated the new strains’ DNA, and used these to transform the standard KW20 Rd strain. By selecting for NovR, we can be certain that the clones I pick took up DNA and recombined it into their genomes.

One technical issue arose, however, which required a little bit of thought: Should I have streaked for single colonies? I.e. once I had my transformants, it might be a good idea to streak out individual colonies to make sure I purified them away from any background or broke apart any doublet colonies. No big deal, but after talking it out with Rosie, we decided to skip it. Why? So that we might get lucky and distinguish recombination followed by mismatch repair versus recombination followed by segregation. In the following figures, I illustrate what I mean by this…

In this first one, the donor DNA is shown in red, and the recipient chromsome is shown in two colors, blue and green, to distinguish the strands. The lowercase letters indicate polymorphic sites in the donor genome. Little a is meant to be the selectable marker, in this case an allele of gyrB:
Donor DNA is incubated with competent recipient cells, and recombination of single-stranded DNA leaves patches of heteroduplex in the genome, shown as small red patches on either the blue or green strands.

After this, the cells have a chance to perform mismatch correction to fix any heteroduplex. I select for cells that have little a by plating to novobiocin plates, so only cells that end up a/a will survive an make colonies. (I am not going to show any examples of restoration repair, in which donor alleles are repaired back into recipient alleles… this will be invisible in our analysis.)

In the below example, I show the A/a and B/b heteroduplexes getting mismatch repaired into a/a and b/b, whereas C/c and D/d heteroduplexes remain unrepaired (they escape correction). What will happen in such as case is the generation of a sectored colony, in which (in principle) half the cells would have one genotype and the other half a different genotype:
In the above example, the original transformant segregates the c and d alleles into different cells, while a and b end up in all cells. If the whole resulting colony is grown up and sequenced, the a and b alleles will be the only ones observed, while at the other two loci, there will be a mix of C and c, along with a mix of D and d. We wouldn’t be able to tell “phase”, i.e. whether c and d were on the same or different chromosomes, unless we did streak for singles and the sequenced several clones. But as a first pass, this could be a really interesting analysis. It will also serve as excellent proof-of-principle for our more intense sequencing plans.

There is a caveat, however, which means we need to get a little bit lucky to be able to distinguish these phenomena (mismatch repair versus segregation). We won’t see two different genotypes, if the A/a heteroduplex isn’t mismatch corrected:
The issue isn’t that segregation didn’t happen; the problem is that one of the segregants dies under selection for little a.

Thus, if we see a pure genotype, then either all mismatches were corrected, or our selectable marker didn’t mismatch correct.

When I pre-screen my transformants to make sure they’re not spontaneous mutants, I might be able to pick a colony where I think segregation is occurring. If I get the standard sequencing traces back and see mixed bases in the chromatograms that corresponde to donor and recipient alleles, I’ll pick that kind of clone for sequencing…

One sort of sad note here, in terms of the more distant future, is that mismatch repair mutants, which should be quite useful for understanding transformation, will need to be transformed without selection if we hope to recover isolated segregants from individual transformants. (continued...)

E-Z Strain Construction

As preliminary data for our genome-wide recombination analysis (outlined in this post from Rosie), we want to sequence the whole genome of a single transformed clone in the next couple of months. The idea is to transform our standard KW20 Rd strain with DNA from one of the other completely sequenced strains (probably 86-028NP, possibly PittGG), select a single transformed colony, and sequence its genome.

This will provide us with all sorts of useful preliminary results:

1. Show that we can indeed handle the type (and amount) of data we’ll be obtaining.
2. Estimate the total amount of donor DNA a single recipient recombines (and fixes) into its genome.
3. Estimate the length of recombination tracts (gene conversions) / the strength of “linkage”.
4. Estimate mosaicism of donor and recipient sequences (mismatch repair).
5. Estimate the transformation rates for different classes of single-nucleotide differences (for example, the number of A->T transformation events observed versus the total A->T differences between the strains)

In particular, item (2) will be crucial for estimating the total amount of sequencing we would need to measure transformation rates per polymorphism across the genome. Simple transformation assays with DNA from the multi-antibiotic resistant MAP7 strain suggest that possibly 20-50kb of DNA may be replaced in a single transformant, but this type of analysis is restricted to only a few different sites in the genome and is very roughly calculated.

The analysis of a single transformed genome will still be preliminary with regards to (3)-(5), for which we will want genome sequences for several independent clones. In the future we are likely to barcode and pool independent transformants, since we expect that a single lane of Illumina sequencing will be overkill for a single Haemophilus genome of less than 2 Mb (250X sequence coverage).

Anyways, one issue with producing the material for this first sequencing experiment is that we need to make sure that the clone we select comes from a cell that was indeed competent and did indeed get transformed. Since only a fraction of cells in a competent culture are competent, we would be wasting a lot of time and money, if we accidentally just re-sequenced our recipient genome.

In order for this to work, we need our donor strain to carry an antibiotic resistance marker. By selecting for recipients that become resistant, we can be sure the clone we select took up DNA that got recombined into the genome. (This may also create a bias for donor alleles near the selected site, due to “linkage”.)

To this end, I am doing the following:

1. Made a couple strains (KW20, 86-028NP, and PittGG) resistant to novobiocin. I just did this. It worked like a charm thanks to the former postdoc having a well-organized lab notebook and a well-organized freezer box containing a tube with a NovR allele of gyrB already prepared for me. This was also my first time doing overnight transformations. I couldn’t believe how easy it was: Add a frozen aliquot of cells and some DNA to some sBHI media, let the cells grow overnight, and plate them the next day. There were plenty of resistant colonies this morning.
2. Prepare DNA from the newly produced 86-028NP NovR and PittGG NovR strains. I’ll do this tomorrow from the overnight cultures I just inoculated.
3. Transform KW20 with this DNA. I’ll use competent cells I already have tomorrow, after my DNA prep.
4. Saturday, assuming I have NovR transformants, I’ll pick and grow up some transformed colonies overnight.
5. Sunday, I can prepare this DNA, and that’ll be what we can send for sequencing!

So if all goes well, we should have our material in a few days! Then we wait. Then the real work begins…

(As a side note, 86-028NP indeed appears to already be resistant to another antibiotic, nalidixic acid. I will check to see if this resistance is transformable when I have the 86-028NP NovR DNA in hand.) (continued...)

Homoplasy versus Recombination

Making up for lost blogging time!

Just to get started doing something with this alignment, I just grabbed a single 68,580 bp alignment block out of the XMFA file I generated (as described in the last post), which was itself a simple FastA formatted file. So I could use whatever programs could handle a simple FastA alignment and not worry about converting around to different formats yet, or coping with contig and rearrangement boundaries.

Here’s a picture from MAUVE of the aligned block I used (KW20 Rd reference coordinates 401,890 to 467,839). It’s the orange block shown below.
Based on some recent reading, I tried a couple different programs designed to detect signals of recombination from multiple alignment files. There is a serious caveat here, in that I do not understand exactly how these programs work, and since they were designed for use on sexual eukaryotes, it’s quite possible that the assumptions of the programs will not be accurate when applied to Haemophilus. Nevertheless, I sallied forth, just to see if they’d at least work with my sequences. I can come back and try to understand the meaning of the output more slowly...

LDhat

First, I tried LDhat, which is composed of a suite of programs aimed at detecting recombination in a population sample. I didn’t have any difficulty downloading and compiling the program, which was nice. I wish I could tell you more about it, but I never actually made it work, so there’s not much point yet. I got the “Convert” program to make the requisite files from an only slightly modified FastA, and I eventually got the “Pairwise” program to work. This program generates a “look-up table” which is required for the other programs to operate.

At first, the program kept choking on my data and was unable to produce the requisite look-up table for my data. So I fed it a pre-made file that can be found at LDhat’s website. The look-up table I used was first converted to a 15 taxa file using their “LKgen” utility. Then “Pairwise” worked and generated me a new look-up table that should theoretically have worked with the other programs. I next used the “Interval” program, but I could never get past here. It wouldn’t do the analysis, because the look-up table was somehow lacking. It was not “exhaustive”, whatever that means, and there was apparently a disparity between “npt” (which I assume stands for Nonparametric test) and the data.

No clue. Moving on. (I’ll try to return to this some other time, since the program “Rhomap” looks like what I’m looking for).

PhiTest

Next I turned to PhiTest, which was also easily downloaded and compiled. This program uses a principle that seems pretty sensible. It examines “incompatibilities” in phylogenetic signals. If two lineages of bacteria diverge and never recombine, then adjacent polymorphisms will most likely be “compatible”, that is both polymorphic sites will have the same phylogenetic signal (i.e. they will support the same tree topology). On the other hand, “incompatible” sites could have two possible histories, one in which there was recurrent mutation in different lineages and one in which there was recombination between lineages. This is illustrated nicely in a figure from the paper reporting PhiTest:

"Figure 1:
The dual nature of incompatibility. Two possible histories for a pair of incompatible sites are shown: (a) two incompatible sites explained by a recombination event and (b) two incompatible sites explained by a convergent mutation. Mutations in the first site are indicated by open circles and mutations in the second site are indicated by solid circles. To explain the incompatibility between the pair of sites either a recombination event must be invoked or a homoplasy must have occurred in the history of one of the sites."

Figure 1A:
Figure 1b:
So in (a) the black mutation only occurred once and was subsequently shared between the central lineages, whereas in (b) the black mutation occurred independently in the two lineages.

They more rigorously define “compatibility” thusly: “Two sites i and j are compatible if and only if there is a genealogical history that can be inferred parsimoniously that does not involve any recurrent or convergent mutations (known as homoplasies as in Figure 1b). If the two sites are not compatible, they are termed incompatible. Under an infinite-sites model (KIMURA 1969) of sequence evolution, the possibility of a homoplasy does not exist, and so incompatibility for a pair of sites implies that at least one recombination event must have occurred, as in Figure 1a.”

So the idea of recombination detection algorithms is to exploit the notion of incompatibility, but it is extremely important to note that there are really two possibilities for any incompatibility: (a) It is a true “homoplasy”, i.e. a recurrent mutation; (b) it indicates recombination. The basic idea of the NSS method (which is apparently what LDhat is using) is to look at neighboring pairs of sites and find regions where incompatibilities are clustered together, suggesting perhaps a hotspot.

The other, possibly more eukaryotic, aspect is that sites that are more distant will tend to be less compatible, since they’d more likely have had crossovers between them. Our provisional model for natural transformation would not necessarily have this feature, however, since we do not expect our recombination events to be crossover-like.

The authors of PhiTest produced a different measure than the NSS model that looked at the “Pairwise Homoplasy Index” for each pair of SNPs in an alignment. This gives a test statistic of the minimum number of homoplasies in the alignment, essentially based on a parsimony criterion. They could then apply permutation tests to measure the significance of a given PHI for an interval. Happily, the program will also output the significance under the NSS and MaxChi models.

For my alignment, PhiTest gave the probability of no recombination as 0 for all three models. So it looks like there’s quite a bit of homoplasy in my alignment, evidence of recombination. (It’s worth noting that PhiTest and LDhat both explicitly ignore gaps in the alignment.)

But that’s not good enough, what I really want is a plot of recombination rates across the alignment block. The figure I made from this post was the output of that analysis. It is only showing the ~9500 polymorphic sites and ignoring all the gaps and identical sequences. It’s showing, for each pair of SNPs, the probability of recombination (assuming that all homoplasies are due to recombination and not recurrent mutation).

It pretty much looks like there’s evidence of recombination almost everywhere! There’s just a couple areas that look like they have extensive compatibility, so it may actually be a lot easier for us to detect places where there’s been little recombination than places where there’s been lots.

I’ll need to go back to the raw data and figure out which set of SNPs are showing evidence of low recombination and see where those spots are in the alignment. They could be horizontally transferred segments with few USS?

I’m not sure what to make of this. It complicates things. To what extent can I trust these algorithms to find recombination and not recurrent mutation? The divergence between my sequences isn’t that high (only ~3%), so recurrent mutation doesn’t seem likely to be a huge problem, but still...

Anyways, I went ahead and used the other utility that comes with PhiTest, called Profile. This does PhiTest on sliding windows, providing a P-value for each segment. Here’s what it looked like, when I did a “Profile” of non-overlapping 500 bp chunks of my alignment:
The x-axis is the position in the alignment, and the y-axis is the p-value of the PhiTest for that particular 500 bp chunk. The line indicates a P-value threshold of 0.05, so any segment below that line has evidence of recombination, based on PHI. Segments that have higher P-values may either have compatible SNPs, or for the very high P-values, be sites with very little SNPs.

This seems promising, if we can trust that we’re measuring something meaningful. I have a strange feeling that using homoplasy to measure recombination is going to be fraught with difficulties, but it’s pretty much the best principle that we can work with to detect recombination in these population-level samples. Luckily, both PhiTest and the other methods are sophisticated enough that they are evaluating the homoplasy index for a given pair of SNPs relative to other surrounding SNPs, so when incompatibilities are detected, they are standing out from other possible pairings. This should help with the problem of true homoplasy, but then again, maybe it doesn't. I'll need to talk to my friend Corbin about this stuff.

Whew! Okay, enough on this for the moment... (continued...)

Interfering Clones

Since I’m new around here, I thought I’d try and start reflecting on the big picture surrounding the Redfield lab’s research, starting with that last post on Sciara. This will end up taking me several posts, because I’d like to articulate my own understanding of the evolution of sex, which is somewhat muddled and rather uneducated. So this post is meant to be introductory to make sure I have the most basic elements down.

A primary mission of the Redfield Lab is to understand why some bacteria are naturally competent. That is, why do some bacteria have the ability to take up DNA from their environment? Since natural competence requires upwards of a couple dozen gene functions, the pathway must have some direct selective benefit to naturally competent bacteria. Several non-mutually exclusive hypotheses have been posed to account for the maintenance of the natural competence mechanism, namely:

1. “The Repair Hypothesis”: As a source for DNA templates for the repair of DNA double-stranded breaks;
2. “The Food Hypothesis”: as a source of nucleotides for food; and
3. “The Sex Hypothesis”: as a way of shuffling genetic material by transformation with uptake DNA.
   

Of course, these hypotheses each have their pros and cons, and to my naïve eyes, each seem somewhat context-dependent and are surely not mutually exclusive. But I’d like to turn my attention mainly to the third hypothesis, which is an attractive one, but perhaps requires the highest burden of proof, since the benefits conferred in this case act not on the level of the individual but on the level of the population.

My own understanding of the evolution (or rather, the maintenance) of sex has been hampered mostly by one big problem: In introductory material, it is almost always implicitly in the context of animal sex: or more specifically obligate sexual reproduction between dioecious anisogamous diploids. When I go to a textbook or the internet to look for introductions to the subject of the evolution of sex, the issues are almost always partially confounded by this context. Never mind that for many sexual eukaryotes, sex is facultative or even rare. Never mind that for many sexual eukaryotes, haploidy is the norm. Never mind that there are plenty of critters whose gametes are of equal sizes (isogamous) and make equal contributions to the zygote (this is really extreme in some protists, where the parents are the zygotes!). Never mind that the sexes are not always kept in separate individuals. Etc. I tend to think that this is extremely misleading and I'll likely return to my favorite hypothesis for animal sex (“we’re stuck with it”) at a later date.

When I start thinking about bacteria, what is even meant by “sex” is confusing. In many cases (with the exception of natural competence) sex-like mechanisms are mediated by parasites, like transduction and conjugation systems. To quote the eminent John Roth, “In bacteria, sex is a venereal disease.” But since transformation via natural competence is often touted as an analogy to sex, I’d better get some idea of what kind of help it could provide outside the typical higher eukaryote context.

There is one common pedagogy that helps me considerably in understanding a possible advantage of sex, but this is really about the putative advantage of recombination. Even in the case of recombination, there seems to be a fine balance, as many organisms have modulated how much recombination is allowed through subtle alterations in their lifestyles. So while recombination can offer advantages, it is clearly not an absolute advantage.

Okay, so why can be recombination useful? First and foremost, recombination is used for repairing broken DNA, not for sex. This is why recombination exists. All cellular lifeforms (along with many cellular parasites) need the ability over the long-term to repair DNA double-stranded breaks and some other types of DNA damage by recombination. DNA replication itself becomes a problem in the absence of recombination mechanisms. In this context, rather than increasing diversity, recombination is anti-mutagenic. So first, the advantage of recombination between individuals has to be disentangled from the advantage of recombination within individuals. At least for me, this isn’t as easy as it sounds, but for now, I’ll just distinguish sexual recombination as the kind that shuffles the genetic material between two related individuals.

One common explanation given for the advantage of sex is to escape Muller’s ratchet, which posits that in the absence of sexual recombination, genetic drift can cause weakly deleterious mutations to fix and accumulate in populations merely by chance. This requires the effective population size to be small enough for a population to feel the effects of genetic drift. In infinite populations, all deleterious mutations, no matter how weak, will be eliminated by natural selection. But in finite populations the ratchet causes more and more weakly deleterious mutations to slowly arise in a genetic background, slowly leading to the extinction of the population as its mean fitness inexorably goes to zero... i.e. mutational meltdown.

An affiliated idea (or perhaps simply a more generalized form?), the Hill-Robertson effect, suggests that selection is less efficient for two linked loci under selection than for two unlinked loci under selection. (In a perfectly asexual organism, all loci are perfectly linked.) The following figure (from Wikipedia) illustrates a version of this, called clonal interference.
The x-axis is time and the y-axis is the abundance of a given genotype, where everyone starts out ab. The top graph illustrates a sexual population, while the bottom graph illustrates an asexual population. In the asexual population, beneficial mutations (capital letters) that arise at different loci in different individuals cannot recombine to make the most fit genotype (AB), so one ends up lost (that’s the interference). In order for the two beneficial mutations to end up on the same genetic background, they must independently arise on the same background. With sexual recombination, the two mutations can arise on different individuals and still end up on the same genetic background. I think that this type of clonal interference would still occur in infinite populations, but am not really sure. (This is relevant, since in bacteria, population sizes may often be so large as to reduce the problem of drift.)

I can imagine that clonal interference could be a problem for bacteria, even with large population sizes, and how natural competence could allow a group of bacteria to escape from this interference. So the “Sex Hypothesis” is certainly plausible. The difficulty, then, is how to demonstrate that this is really why natural competence is maintained. Maybe “why?” is simply always a difficult question to approach experimentally...

Okay, so there we go: Presumably, one major advantage of sex is that it allows for recombination between different individuals, which lets fit alleles get together and bad alleles to get culled by selection more rapidly.

For now, I’ll leave it there and just mention that it isn’t necessarily a good thing for the individual to have recombination with some arbitrary related DNA from its environment (or by sex), even assuming that it isn’t somehow damaged. If you have perfectly good co-adapted genes in your genome, replacing one of your alleles with some other one may end up making you less fit... (continued...)

Sciara and the problems of sex

At the risk of getting too far afield, I want to talk about one of the craziest animals around with respect to transmission genetics. I promise to bring this post back around to bacteria in the future.

The sciarid flies (fungus gnats) illustrate some of the most tantilizing mysteries of genetics in one compact package. When I first learned about the fungus gnats, I found them interesting from the perspective of chromosome mechanics, but now I also think these little flies also illustrate something interesting (or maybe just confusing) about the putative benefits of a sexual lifestyle (one of the major hypotheses for why bacteria are naturally competent).

There are three kinds of chromosomes in fungus gnats, each of which behaves in an unusual way compared to familiar eukaryotic transmission genetics. In this post, I’ll focus on the autosomes, which illustrate how tangled and confusing the issues can become when thinking about the evolution of sex.*

In fungus gnats, males only transmit their mother’s autosomes! That’s right: The males are diploid, obtaining a complement of autosomes from mom and a complement from dad. But somehow, their sperm only include maternal autosomal chromosomes. (Females transmit their autosomes in the ordinary fashion.) From a chromosome dynamics and epigenetics standpoint, this is a very interesting germline development and meiosis** (and offers some entertaining genetics thought-experiments), but what about with respect to the evolution of sex?

Fungus gnats aren’t the only group of animals where males only transmit their maternal autosomes; it’s also true in haplo-diploid hymenopterans, like honey bees. In these animals, males arise from unfertilized eggs (sometimes called arrhenotoky), so that male honey bees are typically haploid. Thus the sperm these males make is also non-recombinant (by necessity, since there’s no homolog there at all) and contain only maternal genetic material.

Why is this of interest? I personally often get confused when reading even simplistic writings on the evolution (or maintenance) of sex. Part of the problem is that numerous issues are often entangled, in this case the issue of ploidy and several overlaying issues of recombination. So describing this unusual lifestyle is not meant to simplify things, but rather to point out just how difficult it can be to think about why an organism might adopt a particular lifestyle.

I’ll talk more extensively about the (simpler) theoretical reasons why sex is thought to be advantageous in another post, but for now the one really major reason that’s always cropping up is that sex offers the chance to recombine two different genotypes to produce new combinations of alleles in the next generation. So sexual recombination, or generating haplotypic diversity, is often considered a major force that maintains a sexual lifestyle.

This is all fine and good, but there appear to be numerous instances of sexual lifestyles that have ameliorated-- but not eliminated-- recombination, raising a variety of questions. A good example of this is the absence of recombination between autosomes in drosophila (fruit fly) males. For a given autosome, a male fruit fly will transmit to his offspring either his mother’s chromosome (which will be recombinant with respect to his maternal grandparents) or his father’s chromosome (which will be non-recombinant with respect to his paternal grandparents). So all else being equal, linkage disequilibrium will break down more slowly in such a population where one sex doesn’t undergo crossing over, as opposed to one where both sexes do.

Genetic transmission is a bit different from this for the fruit fly sex chromosome, the fungus gnat sex chromosomes and autosomes, and all the chromosomes in a honey bee. So for sex chromosomes, since males in all three groups get only a single X from their mother, it will be recombinant with respect to their maternal grandparents but transmitted intact to a male’s progeny. And for the autosomes of both fungus gnats and honey bees, the same thing is true.

But with fungus gnats, males adopt a diploid state, but still behave like a bee with respect to transmitting their autosomes. What’s going on here? One common explanation for the alternation of generations and the haplodiploid lifestyles (i.e. keeping separate haploid and diploid states) is that haploids and diploids feel the effects of natural selection differently. While a recessive allele in a diploid population only manifests when the allele is homozygous, it is always exposed in a haploid. So selection acts more quickly on haploids, and greater allelic diversity (and slower selection) can exist in diploid populations. By having both states in a population, the benefits of both can be enjoyed.

Okay, so that’s all pretty confusing, but I think the take-home is this (all else being equal, except ploidy and genetic transmission mechanism):
(1) Fungus gnat males usually get to cover their recessive alleles by being diploid-- as with fruit flies. But honey bee populations feel selection much more strongly, since their recessive mutations are always uncovered by another allele in males. So deleterious alleles can presumably be weeded out more efficiently in the honey bee than the other two, while recessive alleles have more of a chance to lurk in the fly populations.
(2) But on the other hand (all else being equal) linkage disequilibrium would presumably break down more quickly in fungus gnats and honey bees than in fruit flies, though not as quickly as if homologous pairs recombined in both sexes meiosis.

So there seems to be numerous forces balancing each other here. At a minimum, even ignoring the rest of the factors, the way alleles and combinations of alleles will behave over generations in these three animals will be distinct, depending on the mode of genetic transmission and the ploidy in the different males. The only thing that (all else being equal) is the same between all three animals is the sex chromosome. So the fungus gnat lifestyle offers an interesting hedged strategy as compared to the more familiar fruit fly and bee lifestyles, as well as having some very compelling chromosome mechanics.

* I’ll mention the other two types of chromosomes, just to illustrate how much interesting stuff is going on here: (1) The sex chromosome (X) always non-disjoins in meiosis II in males, such that every sperm is disomic for the X chromosome and thus every zygote is trisomic for the X. Later in development, the elimination of 1 or 2 X chromosomes determines the sex of the animal (female or male, respectively). (2) The limited chromosomes (L) are small heterochromatic knob chromosomes that are only present in the germline, and are somehow eliminated from somatic tissues by selective non-disjunction. So those are two very interesting phenomena in sciarids that will hopefully deserve future mention...

** How do the males accomplish this featof maternal-only autosome transmission? First of all, like many other male dipterans, there is no crossing over between homologous chromosomes. This is also the case in the model organism Drosophila melanogaster. Second, somehow only the maternal autosomes form kinetochores at their centromeres in the meiosis I division, so the paternal autosomes get left behind by the unusual asymmetric spindle of the male meiosis. This means that throughout the germline mitotic divisions, something distinguishes the maternal from paternal chromosomes that then manifests in the first meiotic division... (continued...)