Now that's a time course!

I tested the effect of allowing the cells time to express their new kanamycin-resistance gene, and found that it didn't matter at all. But everything seemed to be behaving well, so I went ahead and repeated the big time course experiment anyway. And it worked very nicely this time.

The top graph shows how the cultures grew under the different treatments. BHI is the rich medium, and they grew nicely in it. Adding 1mM cAMP slowed growth down a little bit, which is not surprising,as cAMP is a powerful metabolic signal molecule. Transfering the cells to the starvation medium MIV stopped their growth, and even caused quite a drop in cfu/ml, but after a few hours they began to grow again. This could be because A. pleuropneumoniae differs from H. influenzae in being able to synthesize its own pyrimidines - we would need to check its genome.

The lower graph shows the transformation frequencies of the cultures at the same times the cfu were measured. Cells in BHI did become quite competent when the culture density got high, just as in H. influenzae. Transfer to MIV rapidly stimulated competence, but only to the same level that develops 'spontaneously' when the culture gets dense in BHI. (In H. influenzae MIV competence is about 10-100-fold higher.) Adding cAMP to the BHI didn't appear to affect competence at all; the slightly lower competence is likely an indirect effect of the slightly slower growth rate.

This is prettier time course than the one I was trying to replicate, so this will probably be the figure that goes into the manuscript.

Praise those who post Excel Tips!

Here's a graph of some data:
How do I tell if the blue slope is significantly different from either of the grey slopes? I know just enough about statistics to know that there will be a way to test this, but not enough to do it. And the post-doc I've relied on for statistics help just moved on to a second post-doc position on the other side of the country. What to do?

Google the problem, of course. I think I searched with 'calculate confidence interval for slope, and that led me to this page, one of a collection of Excel Tips for scientists and engineers posted by one Bernard Liengme, a retired professor of chemistry and lecturer in information systems at St. Francis Xavier University in Nova Scotia.

At first the page looked quite daunting. The post-doc confirmed by email that this was instructions for doing what I wanted, but didn't offer to do it for me. I then tried clicking on the link to the sample workbook, which gave me an actual Excel file with the example calculation all set up. So I just did to my data what the example did to its, and presto, I have the confidence intervals for my lines! it's a bit embarrasing to admit that I don't know what the "INDEX(LINEST" command does, but then I don't know what's in the secret buffers and columns of the kits we use either.

So thank you Dr. Liengme! Note - a new edition of his book A Guide to Microsoft Excel 2007 for Scientists and Engineers is available in paperback from Amazon.

p.s. The 95% confidence interval for the blue line overlaps slightly the intervals for the grey lines.

Another time course to do

We convinced our A. pleuropneumoniae collaborators that the manuscript should be submitted in its current form after I do another replicate of the time course (and with some genome-analysis data presented as a table rather than just described in a sentence in the text). So now I first need to do a test of expression-time requirements for starved and growing cells, which should be relatively simple.

 

Quick plan: start with frozen starved cells, thaw cells, resuspend half in fresh starvation medium (to get rid of the glycerol they're frozen in) and half in a larger volume of rich medium.  Add DNA to the first cells, incubate 15 minutes, add DNase I, incubate 5 minutes, add an equal volume of rich medium, continue incubating.  At intervals (0, 10, 30, 60 minutes) take samples, dilute and plate on plain and kanamycin plates.  Incubate the rich-medium cells until they're moderately dense (OD about 1.0), then add DNA and DNase I and sample as above.  Total of 8 samples, needing about 32 plain plates and about 50 kanamycin plates.  This time include a no-DNA control.

Then, do another time course like the one I did last week.

Manuscript genuinely nearing completion?

One of the bioinformatics manuscripts on my desk (on the shelf, on my office floor) has been 'nearing completion' for so many years now that I'd genuinely come to see this as its permanent state.  

The work began about 10 years ago as a collaboration with a colleague in Taiwan, produced interesting results about the effects of uptake sequences on proteomes, and was partly written up and then set aside when the student who had done the bioinformatics graduated and went on to unrelated work.  At that stage I had a manuscript that was fine in some parts but flawed in others. 

About four or five years ago we began a new collaboration with bioinformaticians in Ottawa, initially on another project, but later on a new version of the old proteome project.  That produced lots of data and a new draft manuscript, but we kept finding little problems with the data and getting new ideas for analysis.  And I kept getting setting it aside to work on other things, as did my Ottawa colleague.  

But I can see the light at the end of the tunnel now, and am seriously hoping to get the damned thing submitted by Christmas.  So yesterday I sat down to try to remember where it stood, and to look at the latest (hopefully final) data.  It looks good enough, so I just need to lower my standards and get it done.  (Maybe drinking some of the beer in the lab food fridge will help.)

Time course results and prospects

Well, my execution of the time course experiment was near-flawless, but the results leave a bit to be desired.  Once again the colony counts were erratic, and some of the results are inconsistent with results of previous 'replicates'.  (Is replicate the right word here?).

I do have a new hypothesis to explain the colony-count problems: too short of an 'expression time' for the antibiotic resistance allele.  When cells recombine an allele coding for an antibiotic-resistant version of a protein, they don't instantly become resistant -- some time is needed for the new allele to be transcribed and translated into protein, and full resistance may take an hour or more.  However this matters more for some antibiotics and some forms of resistance than for others.  

With kanamycin and H. influenzae, experiments I did when I first set up my lab showed that cells could be spread on kanamycin agar plates right away (15 minutes for DNA uptake, 5 minutes for DNaseI, maybe 5 minutes for dilutions and plating), and every cell that had the new allele could form a colony.  When I started working with A. pleuropneumoniae I was told to use a very high concentration of kanamycin to prevent sensitive cells growing on the kanamycin plates, and found that cells did need an hour for expression of the allele before being able to form colonies on plates.  But I tested lower concentrations and found that sensitive cells couldn't grow at a much lower concentration.  In writing up that test I speculated that maybe they wouldn't need expression time to grow on this concentration.  But I seem to have then just gone on to do subsequent experiments without expression time without ever really testing whether it was needed.  So maybe my erratic results and low colony counts on kanamycin plates are because many cells that had acquired the resistance allele didn't have time to become phenotypically resistant before they encountered the kanamycin.

To resolve this, I can thaw out and transform some frozen competent A. pleuropneumoniae cells and test their need for expression time.  But I now wonder if these cells, having been starved to induce competence, might actually need less expression time than the cells growing in rich medium, because the antibiotic causes most of its killing when cells are actively replicating their DNA.  This would be consistent with my results, because the plating problems did mainly happen with growing cells. That's an interesting issue in its own right, and but would entail doing a more complicated test.  If the tests showed that expression time was the problem (at least a big part of the problem), I'd redo the time course.

The issue may be moot, because we may now leave the time course out of the manuscript.  One of the authors from the other research group (the leader of the group) strongly feels that we need to do more experiments before submitting this for publication, even as a 'Note' to a fairly minor journal.  But of course he wouldn't be the one doing the experiments, and the rest of us think we should just send it in and see what the reviewers think.  I and the postdoc can also think of desirable experiments, but our lists don't overlap with his.  So we'll try to persuade him by sending him our list and arguing that we can't possibly do all of these experiments, so let's wait and see which ones the reviewers might want us to do, rather than trying to read their notoriously unpredictable minds. 

Good and bad news

The good news is that my test transformation worked quite well - the transformation frequency was a bit lower than I would like, but the numbers of colonies produced from the different dilutions were just what they should have been, and the colonies themselves were both vigorous and uniform.

The bad news is that our work-study student has the flu, so I won't have any help with the big experiment. But of course it isn't really that big of an experiment, just a full day of keeping track and paying attention and neglecting other responsibilities.