I got some data for the transformation frequency at five different markers. They varied. This isn’t anything ground-breaking; reports of a "position effect" for transformation go back decades and a couple of recent studies in other organisms bear it out. The underlying cause of variation in transformation rate at different positions likely stem from two sources: the physical structure of the chromosome and the sequence composition of the recombination substrates. The former case is reasonably well-worked out for analogous processes in eukaryotes: For example, in yeast, heterochromatic regions are recalcitrant to recombination, but these sites become recombinogenic in mutants with defective heterochromatin assembly. Sequence composition has also been shown to affect the efficiency of recombinational strand exchange in several different contexts, both genetic and biochemical. This latter type of variation is not traditionally considered a "position effect", but is difficult to distinguish from the former.
Anyways, I wanted preliminary data showing that I can, in fact, detect differences in transformation at different genomic positions, since a big part of my proposed work will involve measuring to very high resolution this position effect...
I used MAP7 donor DNA to transform three independent Rd competent cell preps. MAP7 is highly similar to Rd, except that it carries several point mutations that confer antibiotic resistance. There are likely other unselected differences between Rd and MAP7, but few. Thus, differences between marker transformation rates are likely to predominantly reflect chromosome position effects, rather than sequence divergence between donor and recipient.
This latter point isn’t strictly true: in order to see transformation, a genetic change has to be made, and the selected MAP7 point mutations are genetic differences. But because in our preliminary sequencing data, we saw long stretches of donor-specific DNA with dozens to hundreds of SNPs, I don’t think these single-nucleotide differences are contributing too hugely to the observed variation in transformation rate.
Here’s the data for the five markers individually. Vertical bars indicate the mean transformation frequency per viable cell to the indicated antibiotic resistance allele. The inset circle shows a rough map of the location of the MAP7 markers. (Sorry about the lack of an origin.)
Indeed, I see a ~5-fold range of transformation frequencies, from ~1/500 to ~1/100. Since this is only an arbitrary sampling of five sites, the range of variation across the chromosome could be much higher.
As previously discussed, these values underestimate the transformation frequency per competent cell. Competent cultures typically have both competent and non-competent cells, and the “fraction competence” is typically measured by looking at co-transformation frequencies. These are often higher than expected, even for unlinked markers, a phenomenon termed “congression” and interpreted as a binary distinction between competent and non-competent cells in the culture.
The technical value of this is that I can elevate the observed transformation frequency at one locus by selecting for transformation at another unlinked locus, since this eliminates all non-competent cells from the culture, providing potentially higher sensitivity on our proposed sequencing experiments. It also dampens differences in culture-to-culture variation caused by big differences in fraction competence (not shown).
However, there is at least one old report using Bacillus that suggests congression does not simply reflect a binary distinction between competent and non-competent cells . If cells only came in those two flavors, we would predict that any pair of unlinked markers would show the same level of congression, yet they report that different pairs of markers had different congression frequencies (aside from due to linkage). They go on to suggest an interesting model for their observations, but my concern is more technical:
Does selecting for transformation at different loci affect the tranformation rate at a second unlinked locus?
If the answer is yes, then selection for transformants at a locus would be a poor way to elevate the transformation rate at other unlinked loci, since it would be biased in an unknown way. I also measured co-transformation of Nal resistance and each of the other four. Nal is “unlinked” from all the others (i.e. DNA fragments from standard DNA preps will always be too short to contain the NalR allele with another antibiotic resistance allele), so I can measure “congression” four times.
Here is the data. So, for example, the first bar was calculated as: f(kanR nalR) / f(kanR). This normalizes each bar to the nalR rate (i.e. “the frequency of nalR among kanR transformants”).
The first thing to note is that the scale bar has changed relative to the transformation/cfu. For each of the 3 cultures, there was ~3.5 fold increase in the observed transformation rate, which would be expected if ~1/3 of cells in each culture were competent.
The second thing to note is that selecting for any of the four markers had no effect on the NalR transformation frequency. So the answer to the above question is no. Phew! The Bacillus result was cool, but I’m glad it isn’t the case here. A binary competent/non-competent model is perfectly reasonable in our system (though this does not exclude the possibility of variation among competent cells). With this in hand, I can now plot the co-transformation data with respect to NalR. If selecting for NalR only eliminates non-competent cells but does not change the underlying transformation frequencies per competent cell at the other unlinked markers, then life is good.
Here’s the data. So for example, the first bar was calculated as f(kanR nalR) / f(nalR). This normalizes each bar to its own rate (i.e. “the frequency of kanR among nalR transformants”). For the nalR/competent cells, I used the average of all 12 points in the previous plot.
This data closely resembles that of the first figure, except all the values are ~3.5 -fold higher.
Woo! Next I should probably repeat congression data for linked markers, and repeat experiments with more divergent donor DNA.