Anaerobically grown Bacillus need to eat DNA

I've stumbled across a paper that came out three years ago. Its title is "Anaerobic growth of Bacillus mojavensis and Bacillus subtilis requires deoxyribonucleosides or DNA", by Folmsbee, McInerney and Nagle (2004, Applied and Environmental Microbiology 70:5252-5257).

It turns out that B. subtilis and B. mohavensis grown without oxygen can synthesize ribonucleotides but can't convert them into deoxyribonucleotides, because their ribonucleotide reductase enzyme needs oxygen to remove the oxygen from ribose, converting it to deoxyribose (this seems backwards, but trust me). You can't just feed them deoxyribose, because they can't add it to bases. You can feed them deoxyribonucleosides (deoxyribose plus base) because they have specific kinases that put phosphates onto deoxynucleosides. Or you can give them DNA. Salmon sperm DNA, E. coli DNA, synthetic DNA, it all works fine. One microgram DNA/ml increases growth about 20-fold. The DNA can't replace sugar in the medium so the cells aren't using it as an energy source (i.e. they're not burning the DNA for fuel), they're just using it as building blocks for their own chromosome replication.

Is 1 microgram/ml enough to grow lots of B. subtilis cells? Back of the envelope calculation: say 1 cell/5000kb x 10^12 kb/microgram x 1 microgram/ml = 2 x 10^8 cells/ml. Yes, that's just about right.

The authors see no problem with cells using DNA as food; they're microbial physiologists and engineers, and apparently haven't been indoctrinated with the dogma that DNA can only provide genetic information. They write:

"The requirement of some Bacillus strains for DNA or deoxyribonucleosides for anaerobic growth should not be unexpected given their ability to uptake DNA by way of the competence system (7). Also, B. subtilis can cannibalize sibling cells (12), which prevents sporulation during transient periods of nutrient starvation. The nutrients released from lysed cells would include DNA and/or deoxyribonucleosides that could be used as nutrients. Thus, it is likely that some bacilli would have evolved the ability to utilize DNA or deonyribonucleosides as a nutrient source."

The situation is different in E. coli. You can't feed them deoxyribonucleosides because they don't have the requisite kinases, and you can't just feed them deoxyribonucleotides because they have to strip the phosphate off (converting them to deoxyribonucloesides) to get them across the membrane. But E. coli has another ribonucleotide reductase, one that doesn't need oxygen, so it can always make the deoxyribonucleotides it needs from the ribonucleotides it synthesizes. Most other bacteria, including H. influenzae and some isolates of B. subtilis, also have this anaerobic ribonucleotide reductase.

Bacillus? Acinetobacter? Thermus?!?

I'm leaning towards doing the experiment that tests whether competent bacteria benefit nutritionally from the DNA they take up. There are two main issues to resolve. First is the design of the experiment, second is which organism to use.

I was originally thinking of simply measuring cell growth in medium with and without added DNA. This will work only if the effect of DNA is large. For it to be large, the cells will need to take up quite a lot of DNA while they are growing. If they only take up DNA when they run out of other nutrients, the effect is likely to be quite small. I would need to measure differences in growth or survival over long periods in stationary phase, rather like what Steve Finkel has done with E. coli.

An alternative to growth experiments is measuring competition. This would involve mixing cells that can take up DNA with cells that can't (probably cells with a knockout in a DNA uptake gene), and growing them in medium with and without DNA. This growth could be done over multiple dilution/growth cycles, potentially amplifying any advantage of DNA uptake. Using a non-competent mutant provides a nice internal control, but it's important that the mutation not reduce growth under conditions where DNA is not available. A minor effect might be acceptable, but I don't want to have to use statistical wizardry to show the effect of DNA.

What bacteria should this be done with? I wouldn't use H. influenzae - their nutritional requirements are too complex and the uptake specificity is a big complication. My first thought was Bacillus subtilis. I've worked with them before, and they are easy to make competent and can be grown on very simple defined media. So I spent much of yesterday reading papers about the regulation of competence in B. subtilis.

What a regulatory nightmare! I don't know whether B. subtilis gene regulation is intrinsically complex or whether the people who work on it just delight in digging up more complexities, but nobody seems to be trying to make functional sense of it. They do propose some absurd evolutionary just-so stories, such as that B. subtilis has prophage in its genome so cells can lyse under stressed conditions, allowing other cells to take up their genes.

Competence in B. subtilis is a bit of a phenotypic nightmare too - when food runs out most of the culture forms spores but about 10% of the cells instead become competent. While competent they're unable to replicate their DNA (I think) or septate their cells, so they grow into long filaments that divide into many short cells once the competence fit has passed (I saw the movie so I know this is true). Nobody who works on B. subtilis competence seems concerned by this....

I had already known about most of the B. subtilis complications (though not the DNA replication/septation arrest), but being reminded of them made me start thinking about alternatives. The first I thought of was Acinetobacter. These guys are easy to grow and nutritionally simple, like B. subtilis. Their competence development appears to be a lot simpler - there's no sporulation involved. Cells in lab culture express all the competence genes when they get into stationary phase, but they can't actually take up DNA until they're diluted into fresh medium. Then they gradually lose competence during exponential growth. This would make them quite suitable for a multi-cycle competition experiment.

But while reading about Acinetobacter I was reminded about competence in Thermus thermophilus. In lab culture these cells take up DNA all the time (less in stationary phase), giving transformation frequencies of about 1% for most genes. This might give a growth advantage large enough to be detected in a single-step growth experiment. Mutants carrying knockouts of DNA-uptake genes are readily available and well characterized. I think T. thermophilus's nutritional requirements are quite simple, but growing it is likely to be complicated because it's a hyperthermophile - lab cultures are usually grown at 60-70°C! We do have a spare shaking waterbath that could be set this high, but for competence assays and cell counts we'd want to grow cells on solid medium, and I don't even know if one can use agar at this temperature.

I'm going to email the T. thermophilus expert (Beate Averhoff, in Frankfurt) asking if she can send me a pdf of a recent chapter she wrote for a methods book (our library doesn't have it), and whether she thinks it's easy to work with.

Time to get back to experiments

I seem to have run out of urgent desk-work (or my brain has conveniently forgotten what remains to be done) so I'm going to get back to the bench and do an experiment or two. What should I do?

Maybe I should test whether lab cultures of Bacillus subtilis grow better if given DNA they can use as a nutrient. I briefly posted about this last month. Or maybe I should try to find out whether the H. influenzae rec-2 gene is indeed regulated by the PurR repressor. Or get back to trying to find the conditions that cause E. coli to express its sxy gene and (we predict) become naturally competent. Or, farther back, continue last spring's preparations for laser-tweezers analysis of DNA uptake by H. influenzae.

Erroneous old dprA data

A few days ago I posted an old graph of transformation data that appeared to show that the DNA-protection gene dprA was not needed in cells whose recBC gene was knocked out. This was a result I didn't remember getting, so I was going to search through my old notebook (2003-04) to see if this was a real result.

This experiment turned out to not be in my notebook because it was done by an undergraduate working in the lab. She did good work, but I can't find her lab notes on our shelf of old notebooks. (I hereby vow to work with our new lab assistant to get these notebooks better labeled and organized.) My notes of her lab meeting presentations and other discussions don't directly address this point.

But I gradually remembered that her initial results had been wrong (maybe that the strain she thought was a double dprA recBC mutant was actually only a single recBC mutant?), and that correcting the error had eliminated this interesting result, replacing it with a more solid but less interesting one. I did find a copy of her final report . It shows clearly that the double mutant had the same transformation phenotype as the single dprA mutant, so she concluded that DprA's job is not to inhibit the nuclease activity of RecBC.

What's species got to do with it?

Today I'm one of the speakers at a one-day symposium (sponsored by the Swedish government and organized by Patrick Keeling) in honour of the 300th birthday of Linnnaeus. The focus of the symposium is "Species", so I've been working to integrate issues surrounding species to my talk on "Do bacteria have sex?".

The two pictures at the left illustrate how genetic exchange transforms what would otherwise be a clonal population into a species. Think of each skinny line as representing a gene or allele. In the clonal population, all the alleles in any one genome stay together (the broad green bars represent genomes). Alleles change only by rare mutation, so each allele becomes specialized to work well with only the particular alleles of other genes that are present in its own genome.

But in populations with sexual reproduction, all the alleles of the genomes' genes are reshuffled each generation. The two sets of alleles we inherited from our two parents are recombined into new single sets in the gametes we contribute to our children. That's why I've represented the sexual species by a mesh, like macrame, with different strands coming together and then separating to join with other strands. This frequent changing of 'genetic environment' means that individual alleles can't specialize, but must be generalists, able to work well with all the alleles of all the other genes in the population. This is what creates the genetic cohesion that makes a biological species.

A function for the appendix? (sloppy science, slick spin)

A paper proposing a function for the human appendix is all over the news. The paper itself is in Journal of Theoretical Biology (Articles in Press). I'd post a copy of the pdf but the journal is published by Elsevier and they'd probably put me in jail, so instead here's the Abstract

The human vermiform (“worm-like”) appendix is a 5 to 10 cm long and 0.5 to 1 cm wide pouch that extends from the cecum of the large bowel. The architecture of the human appendix is unique among mammals, and few mammals other than humans have an appendix at all. The function of the human appendix has long been a matter of debate, with the structure often considered to be a vestige of evolutionary development despite evidence to the contrary based on comparative primate anatomy. The appendix is thought to have some immune function based on its association with substantial lymphatic tissue, although the specific nature of that putative function is unknown. Based (a) on a recently acquired understanding of immune-mediated biofilm formation by commensal bacteria in the mammalian gut, (b) on biofilm distribution in the large bowel, (c) the association of lymphoid tissue with the appendix, (d) the potential for biofilms to protect and support colonization by commensal bacteria, and (e) on the architecture of the human bowel, we propose that the human appendix is well suited as a “safe house” for commensal bacteria, providing support for bacterial growth and potentially facilitating re-inoculation of the colon in the event that the contents of the intestinal tract are purged following exposure to a pathogen.

Their points (a) to (e) are fine, but the conclusion depends on the totally unjustified assumption that a severe bout of diarrhoea eliminates commensal bacteria from the colon. Of course diarrhoea can reduce the number of bacteria in the colon, and cholera is likely to disrupt its epithelial biofilms. But even the most severe diarrhoeal infections are very unlikely to sterilize it, largely because there will be so many microenvironments within the colon that retain at least part of their biofilm. Biofilms are notoriously difficult to remove and to sterilize.

Other issues:

• What about animals that don't have an appendix? Only humans and anthropoid apes have appendices, but they are certainly not the only animals that get diarrhoeal infections. The authors argue that humans in Western cultures don't need their appendices because they're so rarely at risk of contracting the severe diarrhoeal diseases that the authors postulate necessitate reinoculation from the appendix. But animals are much more like humans in primitive cultures than like affluent humans.
  
• Some of the news reports (but not the paper itself) suggest that humans in primitive cultures need their appendix for reinoculation because populations are sparse and reinoculation from other people would not be reliable. But humans always live in social groups, and one thing severe diarrhoea does is spread intestinal bacteria around the environment, making reinoculation even more likely that it would otherwise be.
  

Mysterious old DprA data

I'm preparing a talk on "Do Bacteria Have Sex" for a symposium celebrating the 300th birthday of Linnaeus. While looking through old PowerPoint slides for ones appropriate for this topic and this audience and this amount of time, I found this slide, from a 2004 talk.

I don't know whether I'd forgotten about this data because subsequent experiments proved it wrong, or just because I'm forgetful. I've written a couple of posts about the DprA protein (here and, more recently, here) - the main issue is that it protects DNA from degradation, but we don't know how or why.

I do remember doing these and related experiments; I just don't remember getting this very interesting result. The logic is as follows: The recBC genes specify a nuclease that degrades DNA. The degradation can facilitate recombination, which is how the nuclease was discovered and why the genes have 'rec' names, but its primary function is to help resolve stalled/tangled replication forks. If DprA's job is to protect DNA from the RecBC nuclease; then cells that lack the recBC gene shouldn't need DprA. The experiment shows that this is the case; the transformation frequency of the recBC dprA double mutant is much higher than that of the dprA single mutant, even though on its own the recBC mutation does decrease transformation slightly. In genetic terms, the recBC mutation is epistatic to the dprA mutation (it covers up the dprA phenotype).

Now I really need to go back to my 2003/04 lab notebook to find out exactly what I did and what I thought it meant.

Paper progress

Yesterday we sent the revised and we hope finalized version back to the journal editor. The requested revisions were minor and we did them all (the first-author post-doc did them all), so we expect the manuscript to be accepted.

And I posted the latest draft of the manuscript about uptake sequences in bacterial proteomes onto the Google site my collaborator had set up for file sharing. This site is a nice tool for collaboration. Before we were emailing versions of figures back and forth, so that when I needed a file I'd have to search for the appropriate version on my computer, (and later discover that I had the wrong version). But now the versions are all available in one place on this site, so I can easily tell which is the one I want. (Yes, I could have/should have just sorted the emailed files into the right folders when they arrived on my computer. I did try to do this but I still wound up with a mess.)

Today the post-docs and I meet to begin our discussion of the old H. influenzae transformation literature (papers pre-1970 this time). And I'll try to find time to check the concentrations of our new RNA preps and run them in a gel to check quality; the collaborating lab would like to have them by Tuesday.

A simpler test for transferred genes?

What if we just plotted base composition as a function of USS or DUS number (really the number of perfect matches to the USS or DUS core). If some genes lack uptake sequences because they've only recently entered the genome (coming from a source genome with no uptake sequences), we predict that genes with aberrant base compositions will be preferentially found in the class with no uptake sequences.

I imagine a graph looking something like this. Each dot represents a gene. Almost all the genes have base compositions close to 38%; this gives far too many points to resolve so the number of points in this group is indicated by the pale blue circles. I could clarify this by writing the actual numbers in blue beside these circles.

The numbers below the 0, 1, etc. on the bottom axis would be the fraction of the genes in that group that had aberrant base compositions. If our hypothesis is correct that newly acquired genes tend to lack uptake sequences, I anticipate these fractions would be highest for genes with no uptake sequences.

Doing this analysis would be much simpler than one that incorporated BLAST search results. On the other hand, the BLAST searches are already done, so maybe we can do both. But probably we should do this first, just to see if there is support for our hypothesis. If there isn't we'll know not to waste time doing the fancier analyses. If there is, we should also do this using codon adaptation index instead of base composition. I found a web page that calculates this index, but only one gene at a time, and I think my collaborators could probably automate it quite easily. (Maybe codon adaptation index has been replaced by a better measure - I'd better do some searching.)

no homolog or weak homolog?

With my Ottawa bioinformatics colleagues I'm analyzing the effects of uptake sequence accumulation on the H. influenzae and N. meningitidis proteomes. One issue I'm struggling with right now is how to tell the difference between genes that give poor "E-value" scores in BLAST searches against test genomes from the same group (e.g. gamma proteobacteria) because they have no true homologs in the test genomes being searched, and genes that give poor scores because they have no homologs in these genomes. The latter genes would be ones that were acquired by transfer from more distantly-related bacteria. I know (at least in principle) how to use phylogenetic analysis to show that a gene has been acquired by lateral gene transfer (LGT) rather than having simply diverged, but here I'm looking for a less rigorous and more automatable method, one that can be used to quickly screen many genes (say 100).

So far I think we could use a combination of low BLAST score against the test genomes and aberrant base composition and codon adaptation index to identify genes that are good candidates for having been acquired by LGT. But I'd like to go one step further - to do one more test where genes acquired by LGT are predicted to differ from genes that have simply diverged in situ.

One test I'm considering would be to take the genes that the base composition and low CAI tests flagged, and BLAST them against a closer relative (something in the same family). If the genes are just divergent, they should give higher scores with the relative. But if they were acquired by LGT, they should either give very high scores (if they were acquired before the H. influenzae and relative lineages diverged) or just as low scores (if they were acquired by the H. influenzae lineage after the relative diverged. The analysis should be also done on a control set of genes, ones that had equally low BLAST scores in the original tests but normal base composition (38% G+C) and codon adaptation indices (???). The BLAST scores of these genes should be modestly higher against the close relative than against the test genomes.

So. Prediction: Control gene set, blasted against close relative: most (all?) BLAST scores modestly higher than with test genomes. LGT gene set, blasted against close relative: Some BLAST scores much higher than with test genomes, others not improved at all.

Growing cells and making RNA (continued)

On Thursday and Friday I finally was able to collect cells from cultures of wildtype H. influenzae cells growing happily (exponentially) in medium with and without a sub-inhibitory concentration of rifampcin. On Friday I did a RNA prep of the cells I'd collected on Thursday, working with our new undergraduate assistant. Yesterday the undergrad did the RNA preps of Friday's cells by herself.

And tomorrow, she and I will run a gel to check that the RNAs are in good shape (not degraded), treat them with DNA-Free, and pass them on to our collaborators for real-time PCR analysis.

Salmonella in space

A paper I've been waiting to see finally appeared, several days after the news articles about its findings. (Larry Moran has a post today about PNAs's wicked practice of letting the media gush about results of papers that aren't yet available.)

Space flight alters bacterial gene expression and virulence and reveals a role for global regulator Hfq. J. W. Wilson et al. (many authors), PNAS Sept. 27 2007. Link

Here's what the news reports said:

"The researchers found 167 genes had changed in the salmonella that went to space..."

Dr. Cheryl Nickerson, the PI on the project, is quoted (misquoted?) as saying "These bugs can sense where they are by changes in their environment. The minute they sense a different environment, they change their genetic machinery so they can survive."

This left me thinking that the genotypes of the bacteria had consistently mutated (cosmic rays) in a way that made them more virulent. From an evolutionary perspective this seemed very improbable - both in having similar mutations arise in many bacteria and in having the bacteria direct their mutations to fit their changed environment.

Now I read the paper I discover that the genes hadn't changed at all. As the title indicates, what had changed was the expression of some genes (some turned up and some down) - this is a transient response to the altered culture conditions in space, not a genetic change. The cells in the culture that had been in space were more clumped together, in what may be the zero-gravity broth-phase equivalent of a biofilm. These bacteria caused more serious disease when injected into mice, probably because the clumping made it harder for the mouse immune system to kill them.

Growing cells and making RNA (...tomorrow...)

Today I'm actually doing something in the lab!

As my latest contribution to the sub-inhibitory antibiotic effect collaboration, I'm growing wild type cells with and without a sub-inhibitory concentration of the antibiotic rifampicin (aka rifampin), collecting the cells in log phase, and making RNA from them.

The plan (discussed previously) is to get the cells growing in their normal medium and then transfer them to parallel flasks with and without the antibiotic. The cells will be allowed to grow and divide for at least two hours (four cell doublings), diluting the cultures with fresh medium as needed to keep the cell density low enough that the cells continue to double exponentially. This will make sure that the cells have fully adjusted their metabolism to the availability of abundant medium with and without antibiotic.

Then I'll collect samples of cells at OD = 0.1 and OD = 0.2 (OD is optical density, a measure of the turbidity of the culture and this of cell density). I'll pellet the cells in the microcentrifuge and freeze the pellets, so I can do the RNA preps later (tomorrow?). Two 2 ml tubes of each OD = 0.1 culture and one of each OD = 0.2 culture, should give me plenty of RNA.

Fine plan, but one immediate hurdle. I meant to inoculate a starter culture of the cells last night, from a frozen stock, but forgot. So this morning I used some frozen cells prepared for just such an eventuality. But after more than two hours the OD of this culture isn't increasing. First thought - maybe my culture medium was no good. H. influenzae grows in brain-heart infusion medium supplemented with hemin and NAD, but I used the old stocks of hemin and NAD I had in my fridge. Maybe they were too old. So I got fresh stocks, supplemented some more medium, and inoculated some more cells from more of the same frozen cells. After an hour their OD hadn't increased either. Then I remembered the great freezer meltdown of 2005. Check the date on the frozen cells - yes, January 2005, probably these stocks went through the meltdown so that most of the frozen cells in those tubes would have been dead. Probably my old medium was fine, but the very small numbers of viable cells in the frozen stocks means that most of the OD I was reading was turbidity due to dead cells.

Finally the ODs are starting to increase. But there's a seminar I really want to go to at 4pm (Peter and Rosemary Grant - very famous evolutionary biologists), so I think I'll have to just let these cells grow overnight and use them as the starter for proper cultures tomorrow.

A new paper on DprA in S. pneumoniae

I'm going to use this post to work through a new paper on the function of DprA (Mortier-Barriere et al. 2007. A key presynaptic role in transformation for a widespread bacterial protein: DprA conveys incoming ssDNA to RecA. Cell 130:824-836). See also my previous post on DprA and ComM. Here's the abstract of the new paper:

Natural transformation is a mechanism for genetic exchange in many bacterial genera. It proceeds through the uptake of exogenous DNA and subsequent homology-dependent integration into the genome. In Streptococcus pneumoniae, this integration requires the ubiquitous recombinase, RecA, and DprA, a protein of unknown function widely conserved in bacteria. To unravel the role of DprA, we have studied the properties of the purified S. pneumoniae protein and its Bacillus subtilis ortholog (Smf). We report that DprA and Smf bind cooperatively to single-stranded DNA (ssDNA) and that these proteins both self-interact and interact with RecA. We demonstrate that DprA-RecA-ssDNA filaments are produced and that these filaments catalyze the homology-dependent formation of joint molecules. Finally, we show that while the Escherichia coli ssDNA-binding protein SSB limits access of RecA to ssDNA, DprA lowers this barrier. We propose that DprA is a new member of the recombination-mediator protein family, dedicated to natural bacterial transformation.

The topic is important because DprA is a ubiquitous and highly conserved bacteerial protein. In naturally competent bacteria it is induced when cells become competent to take up DNA, and without it the incoming DNA gets rapidly degraded. But most bacteria with DprA aren't known to ever take up DNA, so we suspect that protecting incoming DNA isn't its usual function (maybe just a side effect of its usual function). Because almost all bacteria have this protein, its function must be important.

Mortier-Barriere et al. mainly investigated the activities of purified DprA (from S. pneumoniae and sometimes also from B. subtilis) in various combinations with DNA and with two other proteins known to interact with DNA, SSB and RecA. SSB is also often competence-induced, but it's known to have a primary function in DNA replication and repair. RecA is competence induced in some bacteria but not others (not in H. influenzae) and has a primary function in DNA replication and repair.

What the authors' experiments showed:

DprA binds single stranded DNA: They use band-shift assays to show that purified DprA binds single stranded DNA. The lanes with 5-10-fold ratios of DprA to the 90 nt test DNA fragment have about half of the fragment shifted (actually stuck in the wells). The authors never discuss how many bp of DNA a DprA fragment might be expected to interact with. It's a medium-sized protein (about 30 kD) so probably about 10bp? So 10 DprAs might be enough to coat a 9o nt fragment along its length. Once complex had formed it was difficult to disrupt; even a 1000-fold excess of cold fragment added to a previously formed DprA-DNA complex didn't displace all the original fragment from the DprA.

They also examined DprA binding to the circular ssDNA plasmid phiX174. These assays used 4 micromolar DprA and a DNA concentration described as 8 micromolar (nucleotides), so there was one molecules of DprA for every two nucleotide of the plasmid. As phiX174 is 5386 nt long, its molecular concentration was 0.0015 micromolar, giving about 2700 DprAs per plasmid. But considering the ration of protein to nucleotides probably makes more sense.

DprA (and Smf, its B. subtilis homolog) would not bind linear or relaxed-circle double stranded DNAs, but they did bind supercoiled circular DNAs. The authors conclude that this binding is to regions that are locally single stranded (because of the supercoiling?), but the evidence for this isn't very strong (what are "sleeve-like complexes"?). However the evidence is good that, given a DNA molecule that is partly ssDNA and partly dsDNA, DprA binds only to the ssDNA part.

DprA molecules stick to each other: DprA molecules interact with each other in a yeast 2-hybrid assay. They also appear to bind DNA cooperatively - at a ratio of one DprA to 20 nucleotides of phiX174, some DNA molecules formed complexes with DprA while others remained bare. In electron micrographs, distinct DNA molecules were often seen to be attached to each other by globs of DprA. The DprA-coated DNA is lumpy when viewed by TEM, unlike the thick smooth filaments formed by RecA. The authors describe the lumps as "secondary structures/intramolecular bridges".

Abundant DprA can protect DNA from nucleases: DNA that was pre-incubated with a 1000-fold excess of DprA could no longer be digested by the nucleases ExoT and RecJ (opposite polarities) or by the mung bean endonuclease. But this is a LOT of DprA. Probably because DprA molecules stick to each other as well as to DNA, they form what the authors call ' tightly packed discrete complexes' (I'd describe them as 'globs'); these spread out a bit when the salt concentration is increased.

DprA helps complementary single strands anneal: (Fig. 4C, S3CDE) The authors mixed DprA with labeled ssDNA and then added unlabeled complementary DNA strand. The DprA-bound strand left the DprA and instead base-paired with its complement, faster than it would have base paired in the absence of DprA. The ratio of DprA to the 80 nt DNA fragment DNA was 250, which seems very high. The SSB control used a ratio of 100. This experiment tells us that ssDNA would rather associate with SSB than its complement, and rather associate with its complement than DprA, but it also tells us that associating with DprA helps ssDNA find its complement. This may be just because DprA molecules bound to DNA like to stick to each other, or it might be due to something more sophisticated.

DprA interacts with RecA: When cells expressing His-tagged S. pneumoniae RecA were passed over a metal-chelate column, both RecA and DprA were bound. A yeast 2-hybrid screen for proteins interacting with DprA also identified RecA. I don't know whether this interaction depends on both proteins binding to DNA; the cell lysate was not treated with DNase before being passed over the column.

E. coli RecA has an ATPase activity that reflects its ability to unwind dsDNA when annealing a complementary strand. Addition of a low concentration of DprA to RecA bound to a ssDNA filament slightly increased RecA's ATPase activity (by about 20%), but higher concentrations reduced it (to about 50%. when there was twice as much DprA as RecA). E. coli SSB consistently reduced it.

DprA also made it easier for RecA to form filaments on ssDNA. At least some of the DprA remained on the DNA with the RecA. If the DNA was initially coated with SSB, the presence of DprA helped E. coli RecA to form a filament on the DNA. In this case the filaments didn't contain any DprA.

RecA that has formed filaments on ssDNA can help the DNA invade a double-stranded form of the same sequence. But if the ssDNA is first coated with SSB, RecA can't form a filament on it or promote strand invasion. Presence of DprA helps RecA get around the SSB barrier, and allows RecA to promote strand invasion even if the DNA has been previously coated with SSB.

Quibbles and complaints:

The authors cite their 2003 paper as evidence for the competence decrease in a ssb mutant, but this paper only briefly mentions a result and says the data will be in a manuscript in preparation. This means I can't check on how sick the ssb mutant cells are. The introduction also doesn't mention that both S. pneumoniae and B. subtilis have two ssb genes, and that only the one not induced in competence is orthologous to the well-studied E. coli ssb. H. influenzae only has one ssb homolog.

The authors never tell us where the SSB they used came from. I think it is likely to have been E. coli SSB, which can be purchased, rather than either SSB-A (its S. pneumoniae homolog) or SSB-B (a competence-induced S. pneumoniae paralog).

What the authors concluded:

The authors firmly believe that recombination is the reason for DNA uptake (that transformation is the function of competence). They also omit any mention of the consensus that DNA replication and repair are the true function (the evolutionary cause) of RecA and other proteins that contribute to recombination, leaving the naive reader with the impression that these proteins too must exist to create new genetic combinations. Thus I'm not surprised by their conclusion that "DprA is the prototype for a new recombination-mediator protein dedicated to bacterial transformation".

They go on to consider why DprA is so ubiquitous, present and highly conserved in almost all bacteria except those that live as intracellular parasites. Most of the bacteria with DprA homologs are not known to ever be naturally competent. I and others have interpreted this distribution as meaning that DprA has a primary function that is independent of DNA uptake (see previous post). But the authors go the other way. In their last paragraph they use the "it is tempting to speculate that..." qualifier to put forward the idea that all bacteria with DprA homologs are naturally competent.

What I conclude:

In competent cells DprA is likely to slow degradation of incoming DNA by binding to it, rather than by directly inhibiting a nuclease. This is consistent with our unpublished evidence that neither DprA or ComM acts by inhibiting the RecBCD nuclease.

Does anything about these experiments support or contradict the idea that DprA has an important function in non-competent cells? I don't think so. Induction of DprA in competent cells is unlikely to interfere with RecA's recombinational repair activity, but these new experiments don't provide strong evidence that DprA will enhance it.

One critical future goal should be to find out what goes wrong in dprA-knockout cells when they are not competent. As I described in an earlier post, an attempt to do this in E. coli found that the knockout had no detectable effect under a wide range of conditions. The authors of this paper summarize evidence that this is also the case for S. pneumoniae and B. subtilis. Perhaps our 'alternative' perspective on competence will enable us to think of tests the others have overlooked.

As the dprA gene is strongly induced in competent H. influenzae, maybe we should test whether cells with a dprA knockout survive competence (with or without DNA uptake) as well as do dprA+ cells. I may even have some old data that addresses this.

and a book chapter

For weeks now the post-docs have been moaning about their writing work on a book chapter they convinced me we should write. But the work has paid off beautifully.

Today we'll send our chapter in to the editor. It's an excellent overview of everything that's known about competence and transformation in the Pasteurellaceae - something that's never been reviewed before. While the iron is hot we should probably use it as the foundation for one or more shorter reviews to be submitted to journals.

Manuscripts

The sxy manuscript has been quickly reviewed and provisionally accepted by the fine journal we sent it to. The reviewers' suggestions are simple to deal with and don't require new experiments, just a bit of new analysis and some minor improvements to the text and figures. One reviewer did suggest an RNA bandshift experiment, to test whether an unknown protein binds to the sxy mRNA secondary structure, and the editor liked this idea. But they both overlooked our compensatory mutation analysis, which unambiguously shows that two of the sxy mutations increase expression because they disrupt base pairing, not because they change the recognition sequence for a protein. We'll point this out in our response and beef up the relevant part of the Discussion so readers don't make the same mistake.

The USS manuscript is hung up (the editor tells us) because the two reviewers disagreed on whether our revisions were satisfactory and the editor doesn't know what to do about it. Although this is an on-line journal promising a fast publication schedule, the review process has been very slow - 9 weeks for the less-favourable review in the first round and 7 weeks for one of the reviews of the revised manuscript (the same reviewer?). We had similar problems with a previous submission to another journal in this family (it's BioMed Central), so I think we'll avoid them in future.

@#!^%$ microarray data!

You may recall (if you have had nothing better to do than read previous posts) that a few posts ago I raised a concern about how microarray data from independent slides should be combined (as ratio of means or as mean of ratios). The issue arose because the undergraduate student who hybridized the arrays and did the original analysis didn't report over-expression of the genes we have found over-expressed in our reanalysis of the data. I wanted to go back and compare her calculations to ours to see where the discrepancy arose.

So today I hoped to do that. I only had a rough draft of the student's undergraduate thesis. This didn't explain how she did her calculations, so I asked for the final version from her supervisor. He provided a copy, but it turns out to be identical to the version I have, although with his comments and suggestions rather than mine. So we have no information at all about how she did her calculations. (More annoyingly, I now suspect that she completely ignored all my carefully thought out suggestions for improving her thesis.)

But I also got a CD containing her data files from the other lab's computer. So I spent much of today going through her lists of genes that were up-regulated or down-regulated at least 2-fold, and comparing them to our lists from our reanalysis of the array data. The lists disagree completely. For the antibiotic we're not very interested in (ery), the genes scored as 'down' in this file are reported as 'up' in her thesis. (Well, the thesis only considered the subset of genes she thought interesting -'virulence genes'.) These same genes are 'up' in our analysis. So maybe she just switched 'up' and 'down' in the file, and discovered and corrected this error while writing her thesis.

But it's worse for the antibiotic we are very interested in (rif). For this, genes listed as 'up' in her file are also 'up' in her thesis. But these are genes that our analysis says are 'down'. And vice versa - the genes we find to be 'up', she lists as 'down'.

So now there are two discrepancies, and thus different places where an error could be. First, maybe she switched up and down in both the ery and rif files, and corrected only the ery error in her thesis. Second, maybe the dye assignments we used for both our analyses (ery and rif) were reversed but the up and down assignments in her lists are correct. In this case she must have mistakenly switched the up and down assignments for the ery analysis in her thesis.

I'm still digging into this accursed data set because we found a surprising pattern of gene induction in the rif-treated cells. But if we've switched the dye assignments then these genes are down, not up. Is this less surprising? I have no idea.

I'd like to throw this whole project out the window. But I've promised to grow some cells and do some RNA preps so the apparent gene induction effect can be tested by my collaborator's technician and student, using real-time PCR on cDNAs generated from independent RNA samples.

How long will this take? - not too long I think. I'll need to start a cell culture the night before. The next morning dilute the cells into medium with and without rif. Let the cells grow for at least 5 doublings (probably about 3 hours) and collect cells in microfuge tubes, and collect more after one more doubling. I won't need large amounts of culture because the real-time PCR analysis needs very little RNA, so one 2.0 ml tube of each culture at each time should be enough. Well, maybe two tubes of the first samples, because I'll need enough RNA to see in a gel. I'll do RNA preps of these cells, using the Qiagen RNAeasy kit; we have the kit and the preps take less than an hour as I recall. I said I'd grow cells and do preps twice, on different days, to get independent replicate RNAs. I'll need to run samples of each prep in a gel to check that the ribosomal RNAs are largely intact. Once I know the RNA concentration, treat 5 micrograms with 'DNA-free' to get rid of chromosomal DNA that would confound the PCR analysis.

I expect that the new RNA analysis will not confirm the surprising result we see in our present analysis, partly because the result is unexpected and thus more likely to be due to an error than to a previously unknown biological process, and partly because we now know that the data is indeed full of errors. But at least this will get me back at the bench, if only for a couple of days.

Metabolic reconstruction of B. subtilis

The last post wandered away from its original topic (metabolic reconstruction of H. influenzae) and into B. subtilis nucleotide catabolism. I then realized that a similar reconstruction of B. subtilis metabolism was likely to be available, and so did a Google Scholar search. Not only did I find a paper describing this, but it's a very recent paper and so should reflect the latest advances in metabolic reconstruction and all the latest experimental data on B. subtilis metabolism (Oh et al. 2007. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol Chem June 17 epub).

The metabolic reconstruction looks to be much too complex to understand in its entirety. They didn't test DNA as a nutrient, but did test nucleotides and nucleosides and deoxyribose (all supported growth as carbon sources both in silico and in vivo, and the nucleotides and nucleosides also served as nitrogen sources. I'm going to email the author asking for his perspective on whether B. subtilis should be able to use DNA as a carbon and nitrogen source.

Metabolic reconstruction of H. influenzae

A commenter on Wednesday's post suggested that I consider using the available 'metabolic reconstruction' of H. influenzae to assess whether the nucleotides from DNA uptake would make a significant contribution to growth. "Metabolic reconstruction?", I said to myself. Sounds like something I ought to know about.

So I found the papers. The first one appeared way back in 1999, but I've been reading a slightly more recent one (Schilling and Pallson, 2000. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis. J. Theor. Biol. 203:249-283). Metabolic reconstruction uses computer simulations of the catalytic activities of the proteins encoded in a genome to infer as much as possible about the organism's metabolism.

H. influenzae was done first only because its genome sequence was the first to be available. I suspect that more recent analyses of better-known organisms are more sophisticated, but I don't expect anyone to go back and redo the H. influenzae analysis (though maybe that's too pessimistic).

What Schilling and Palssen did: They included only proteins that are metabolic enzymes or membrane transporters - this is less than 25% of the proteins encoded by the genome (~400 genes, 461 reactions). This includes some reactions for which they had biochemical evidence but not identified genes. Their model metabolism did not include any effects of regulation of gene expression or (I think) of regulation of protein activity. No transcription or translation or DNA replication. The model included 51 metabolites required for biomass generation and maintenance (amino acids for protein synthesis, NTPs and dNTPs for RNA and DNA synthesis, phospholipids for membranes, etc).

They simplified the model metabolic structure by subdividing it into six subsystems: amino acids (A), nucleotides (N), vitamins and cofactors (V), lipids (L), central metabolism (C) and transport/energy/redox (T). For each of these they then identified the 'extreme' pathways that (I think) set the metabolic limits. Finally, they used their model to investigate H. influenzae's metabolic capabilities.

Their conclusions are interesting, though some of them are clearly wrong (i.e. their model predicts X, but we know X is not true for real H. influenzae). But the reason I'm reading this paper is to find out whether this model can be used to make predictions about the effect of DNA uptake on metabolism and on growth. I was thinking that the answer is No, mainly because of the simplifying assumptions the model had to make.

The fundamental problem is that I would like a quantitative answer, such as "Taking up 200 kb of DNA per generation would increase the growth yield by 0.5%". But I think that the model will only give qualitative answers about capabilities, such as "Yes, taking up DNA could increase the growth rate". I suppose that kind of answer might be useful, enabling us to make the important distinction between it and "No, taking up DNA can't increase the growth rate, because reaction A is missing".

How would getting one or the other answer change our thinking? If we got a "No, because..." answer, the first step would be to track down the reason(s) and find out whether we'd expect them to hold true in real cells. Is the needed gene really missing? Do we have other evidence that this reason will not apply? (For example, the model predicts that H. influenzae would not be able to use citrulline as its pyrimidine source because a gene is missing, but real cells can do this and we have identified the supposedly-missing gene.) If the reasons for the "No" answer did hold up under scrutiny, we'd need to seriously rethink our hypothesis that cells take up DNA as a nutrient source.

What if we got a "Yes" answer? I don't think we'd do anything differently than we're doing already, but we might be slightly more confident that our hypothesis is reasonable.

I think a genuine "No" answer is unlikely in principle, because, to use the DNA they have taken up for new DNA synthesis, all cells need to do is rephosphorylate the dNMPs that result from nuclease degradation of this DNA. Do H. influenzae have the pathway to do this? They do have enzymes that can catalyze these reactions, but I don't know enough biochemistry to be absolutely certain that these reactions will proceed efficiently.

Could cells use the dNMPs as sources of NTPs for RNA synthesis? This is more complex, as the deoxyribose sugar needs to be removed from each base and replaced with a ribose sugar (cells have no enzyme that could simply create ribose from deoxyribose by adding back the missing oxygen). What cells actually do is remove the deoxyribose and phosphate from the dNMP, and then add ribose plus phosphate to create a NMP which can then be phosphorylated into a NTP. These reactions are included in the metabolic reconstruction; they consume an ATP, but much more will be saved by not having to synthesize the NMP from scratch. The cells also get some energy by metabolizing the deoxyribose-1-phosphate this releases, although this isn't included in the reconstruction. I just checked the pathway: the deoC gene product converts deoxyribose-1-phosphate into glyceraldehyle-3-phosphate plus acetaldehyde - the former feeds into glycolysis but I don't know what happens to the acetaldehyde (maybe ADH converts it to alcohol?).

What about using the dNMPs as sources of carbon (= energy), nitrogen and/or phosphate? Presumably this would only happen when the cell had satisfied its need for dNTPs and NTPs for DNA and RNA synthesis. Our visitor told us that at least some bacteria lack the enzymes needed to break down purine and/or pyrimidine bases; such a cell couldn't use nucleotides as nitrogen sources, and using the sugar for energy might leave cells clogged up with the leftover bases.

This probably isn't a big issue for H. influenzae, as I expect it to only use dNMPs for DNA and maybe RNA synthesis. But if I'm hoping to show that adding DNA to a B. subtilis culture improves growth yield, knowing whether or not it can use the bases could make a big different to my expectations. I've found a paper (Schultz et al. J. Bact. 183:3293) showing that B. subtilis can metabolize purine bases to NH3 and CO2 (with no energy gained but none expended). And an earlier paper from the same group (Per Nygaard's) gives an overview of nucleoside catabolism by B. subtilis; the basic conclusion is yes they can.

So it looks like feeding B. subtilis DNA is a reasonable idea.

Yet another issue in interpreting microarray data

This afternoon I met with my collaborators on the sub-inhibitory antibiotic effects project. We considered where the project could lead (or end): Because the results of the (low-resolution) microarrays suggest some genes are turned up about two-fold by the antibiotic treatment, we plan to do real-time PCR on a few of these genes, looking for the same effect; call this analysis A. (I'm giving fewer details than usual to protect the delicate sensibilities of my collaborator.)

If the results of A show at least a two-fold increase, we do experiment B. If the results of B also show at least a two-fold increase, we could write a small paper. If the results of B don't show at least a two-fold increase, we could repeat the microarray analysis (doing it better this time) and maybe examine protein levels. Maybe, if A gives the two-fold increase, we also consider writing a grant to get money to do this properly. If A doesn't show at least a two-fold increase, we could either end the project, or repeat the microarray analysis anyway in the hope of discovering something the first analysis missed.

But at the end of the meeting the technician raised an issue we hadn't thought about. The student who did the original microarray analysis didn't see the two-fold increase we see in our reanalysis of her data - I've been assuming that this is because she did something wrong in her analysis of the data. However the technician described what the student had done differently than us, and I think it may not be wrong at all - it might be what we should have done.

Here's the issue: In an earlier post I raised the concern of how much we should trust individual data points in a microarray analysis, and pointed out that strongly expressed genes are likely to give more trustworthy signals than weakly expressed genes. But the issue of signal strength and trust may also apply to whole microarray slides, not just individual genes on them. Some RNA preps give better probes than others, due to differences in RNA quality and/or in the various processing steps involved in creating dye-tagged cDNA from the RNA. And some hybridizations give better signals than others, due to variations in the quality of the slide and in the hybridization and washing conditions. An inexperienced researcher will also generate more variation between slides. The net result is that some slide hybridizations (some 'samples, in GeneSpring's terminology) may have substantially stronger signals than others, averaged across all the genes on the slide.

For each gene on each slide, an expression 'ratio' is calculated by dividing the fluorescence signals from the 'test' RNA by the fluorescence signals from the 'control' RNA. Should we put equal trust in slides that gave generally weak signals and slides that gave generally strong signals? Or should we put more confidence in the slides with generally strong signals, because the ratios calculated from those with weak signals will have suffered more from random effects?

What we have done is first calculate the ratio for each slide, and then calculate a mean ratio over the three slides whose signals looked most consistent. But what the student did was to first sum all the 'test' signals for the three (?) slides and calculate their mean, similarly sum all the 'control' signals and calculate their mean, and then use the ratio of these means to decide about significance. Provided there is little slide-to slide variation in overall signal intensity or in the ratio of 'test' to 'control' signals for the gene in question, both methods (mean of ratios or ratio of means) will give similar answers. But if there is substantial variation the answers will differ.

Because the effect for most genes was not much higher than the two-fold cutoff the student used, the differences in analysis methods could have determined whether these genes were just-above or just-below the cutoff. For genes that were below the cutoff in weak signal experiments, but above it in high-signal experiments, she would have seen significance where we didn't. On the other hand, for genes that were above the cutoff in weak experiments but below it in strong ones, we would see significance where she didn't.

Should we worry about this? I've just looked back at the ratios for the different slides - no one slide has conspicuously more significant ratios than the others, as we would expect if one strong slide was biasing the averages. But if it isn't a lot more trouble, it might be wise to go back and redo the calculations the way the student did them, so we can be certain we understand why her conclusions differed from ours.