Field of Science

Interaction effects in uptake bias and in the genome

Well, the postdoc and I continue to struggle with our revisions to his manuscript about the sequence bias of the Haemophilus influenzae DNA uptake machinery.  Quite a bit of the struggle is with each other, as we each try to clarify what we think.

One issue that's just come up is how interactions between bases at different positions of the preferred sequence motif will affect what sequences accumulate in the genome.

The top part of the figure below is a drawing of a double helix of DNA, with a specific sequence drawn on it, and below that are two 'sequence logos'.  The first one is the pattern derived from the uptake sequences in the genome, and below that is the pattern derived from the sequences that were preferentially taken up by the cells' uptake machinery.  The overall difference in height of the two logos isn't significant (they use sequences derived in very different ways), but the differences in the relative heights of the individual positions are.  For example, in the genomic logo all of the Gs on the left are about the same height, but in the uptake logo the first G is much smaller than the others.

One issue our paper needs to address is the reasons that these two logos are so different.

Both of these logos are derived by considering only how frequent each base (A, G, C or T) is at each position in the set of sequences being analyzed.  The analysis doesn't consider the actual sequences.  For example, the two sets of sequences in the figure below (made using WebLogo) give the same logo. But the two sets of sequences are different; in the left one we have only strings of six As or six Ts, whereas in the second the As and Ts are often interspersed or in strings of different lengths.

The postdoc has done a detailed analysis of the actual sequences taken up by the cells (see figure in this post), to find out the importance to uptake of the interaction effects that the logo analysis doesn't consider.  We were both thinking that these interaction effects might be responsible for at least part of the difference between the uptake-bias logo and the genomic logo.

But one of the reviewers of the version we originally submitted said that we were wrong: "If the consensus in the genome reflects only the incoming DNA and the filtering at the outer membrane (as the authors state) then the two consensus should be similar with or without interaction effects because the genomic consensus is the simple result of the initial consensus."  I've thought about this today, and I now think the reviewer is correct.

Let's consider two simple situations for an imaginary uptake machinery whose preferred sequences gave the A&T logo above.  In Situation 1, the actual sequences were those in Set 1, and we would conclude that there were strong interaction effects between the positions because the machinery preferred a sequence where six Ts in one strand were basepaired with six As in the other strand.  In Situation 2, the actual sequences were those in Set 2, and we would conclude that the uptake machinery preferred a string of six A:T basepairs but didn't care which base was in which strand at any position.

Now let's imagine that species exist with each of these uptake biases, and that each uptake bias is causing its preferred sequences to accumulate in its species' genome (because these sequences come in as part of longer DNA fragments that often replace homologous sequences in the genome by recombination - this is our molecular drive model).  In Situation 1 the genome will accumulate strings of 6 As on one strand paired with six Ts on the other.  In Situation 2 the genome will accumulate strings of six A:T pairs in various orders.

Now we sequence the evolved genomes, collecting sets of the overrepresented sequences in each, and make logos of the sequences.  Both logos will look like the logo above.  To see the how the interaction effects in the uptake bias affected the accumulated sequences in the genome, we'd have to do an interaction analysis of the genomic sequences.

Years ago we did an interaction analysis of the genome sequences; you can see them in the last figure in this post from 2006.  It found only weak interactions, and only between adjacent or near-neighbour positions, very different from the interactions the postdoc has identified in the uptake bias.  More recently he applied his interaction analysis to the set of genomic uptake sequences, and he's now  repeating it (that's easier than digging through his notes to find what it showed).

Have bacteria evolved gene-specific rates of point mutations?

A paper just out in Nature (Martincorena et al. Evidence of non-random mutation rates suggests an evolutionary risk management strategy) concludes that E. coli genes have different mutation rates.  Genes that serve important 'housekeeping' functions mutate less often than genes that are used less often or whose functions are less important for survival.

Although such a difference in mutation rates might indeed be beneficial, since most non-neutral mutations are harmful, the result seems very improbable because we don't know of any mechanism by which the processes that cause mutations could adjust their activities according to the function of particular DNA sequences.  The authors don't know of any such mechanism either but they postulate that one must exist.

This is very reminiscent of the 'directed mutation' controversy that arose about 15 years ago, in response to work by Jim Shapiro and John Cairns showing that selection for ability to use a sugar was much more effective if the sugar was present in the environment.  That phenomenon has been shown to not be due to changes in the mutation rate (considered per base pair), but to initially unsuspected cryptic growth on the sugar and changes in the number of copies of the gene under selection.

Mutation rates are tricky to measure directly because mutations are identified by examining the phenotypes or DNA sequences of bacterial cultures many generations after the mutations would have happened.   This means that there has been plenty of time for confounding forces to also act on the mutations - we find only the mutations present in surviving cells, not all the mutations that happened.  The most important confounding force is thought to be natural selection acting on any phenotypic changes the mutations cause, but lots of other factors are known or suspected.

On first reading, I think that the authors of this paper did a good job of controlling for these factors.  But, given what we know about the processes that cause and prevent mutation, their results are so improbable that  I suspect they have missed other factors we don't know about yet.  So I predict that, like the directed mutation controversy, the long-term outcome of this work will be identification of additional confounding factors in the analysis of mutation rates rather than of a clever risk management strategy in the bacteria.

Here's a quick outline of what the authors did:  They started by comparing the genome sequences of 34 E. coli isolates; I think these were sequences available in GenBank, not ones they determined themselves.  Even very closely related bacteria like these have a lot of variation in which genes are present, so the authors first identified a set of 3420 genes, each of which was present in at least 75% of these genomes.  They then carefully compared the DNA sequences of these genes to find all the differences, which must have arisen by mutations accumulating over the many millions of years since these genes shared a common ancestor.

They then filtered out all the differences whose accumulation might have been confounded by natural selection.  First they eliminated from consideration all the differences that changed an amino acid encoded by the DNA.  Then they corrected for effects of E. coli's known codon biases, because mutations that don't change the specified amino acid may still change how efficiently that amino acid is incorporated into the specified protein.  They also corrected for suspected effects of RNA folding by trimming off the ends of the gene sequences (I'm not sure how effective this would be...).

This analysis produced estimated gene-specific mutation rates that differed by as much as ten-fold (look at the jagged line and two examples below).  The mutation rates of nearby genes were strongly correlated over distances of 10-20 kb, especially for genes that were assigned the same 'function' and the same direction of transcription; these are likely to be mostly genes in the same operon.

One factor I wanted more information about is the functional classification scheme used.  This was something I hadn't heard of - the Multifun classification for E. coli, developed by Monica Riley and M. H. Serres.  It looks good, certainly better for E. coli genes than the usual COG analysis (clusters of orthologous groups).

Another issue important for their conclusions is how they assigned functional importance to each gene. They estimated the strength of selection on each gene using the number of changes that did change the encoded amino acids (the info they had discarded in estimating mutation rates).  By this measure, genes in subsets with higher mutation rates tended to have weaker evidence of selection.  Genes in the low-mutation-rate subsets were also enriched for known to be essential for survival in lab culture in rich medium, and they were, on average, expressed as mRNA at higher levels.

The authors then examined how other confounding effects might alter the results, by examining the sequences for evidence that natural selection had acted on them, by checking the possible sizes of other confounding effects (transcription-coupled DNA repair, base composition, homologous recombination), and by using computer simulations to estimate the sizes of possible effects.  These analyses revealed only effects that would be much too small to explain the big differences in estimated mutation rates they found.

Bottom line:  This appears to be a very well done piece of work.  (The Supplementary Materials file is enormous and dense with relevant information and analyses.)  Nevertheless I'm very skeptical of their conclusion that cells have evolved a mechanism to mark important genes and protect them from mutation.  That's both because we don't know of any way cells could do this, and because I think natural selection on such 'evolvability' traits is likely to be many orders of magnitude weaker than as-yet-unidentified direct effects on mutation accumulation.

Conference social skills

(Hmm, new Blogger interface...)

I'm just back from EVO-WIBO a small conference for evolutionary biologists in the Pacific Northwest (WIBO=Washington, Idaho, BC and Oregon).  The quality of the talks and the science was very high, but a few experiences got me thinking that I should write a post about how to handle social interactions at conferences.  So here goes.

On the conference bus:  Maybe you're sitting next to someone you don't know, and maybe they're too nerdy or shy or intimidated or self-centered to start a conversation.  Don't just sit there, ignoring each other.  Say 'Hi', my name is Sandra.  I work on axolotl toenail proteins in Joe Blow's lab.  What do you do?'  Or 'Hi, I'm Sam.  What did you think of that last talk?'

At the first-night mixer:  You and a friend (or a new acquaintance) are chatting with each other, when a complete stranger walks over and stands near you, looking like maybe they'd like to join the conversation.  Don't just ignore them!  Say 'Hi, we were just chatting about the snacks.  Do you think this could be real caviar?'  Or 'Oh, sorry, we're having a bit of a private conversation.  We'll go talk in the corner where it's quieter.'

At meals:  If your conference includes meals, try to sit at a table with people you don't already know.  If you're already seated and talking with someone when another person sits down, smile and say 'Hi, we're talking about the weird last slide in Susan Smith's talk.'  Then turn a bit so they feel included in the conversation.  If more people show up, start a round of introductions.  If you're planning a free-time side trip to the swimming hole or the farmer's market, ask your lunch companions if they'd like to come along.

If everyone has to find their own lunch and you're on your own, try to strike up a post-talk conversation with someone.  You can then say 'I'm going to look for some lunch, like to join me?'  If they're on their own too, they'll appreciate the invitation. If they already have lunch plans, maybe they'll invite you along. If you're the one who already has lunch plans, consider inviting someone who might otherwise be on their own.

At your poster:  Maybe you're explaining your poster to someone, thankful that it's attracted at least a bit of interest, when a second person walks up.  Don't ignore them until the first visitor walks away!  Make eye contact, smile, say 'Hi, I'm just explaining how we collected our data.  If you can wait a minute I'll be able to talk about our goals.'  Then continue your original conversation, but make it easy for the new person to join in or ask questions.

In the question period after your talk:  Try to choose questioners who aren't Mr. Big in the field, and who aren't your friends or labmates.  Make it easy for junior researchers to be heard.

You get the picture.  One of the big reasons we come to conferences is to talk with other researchers in our field.  Do what you can to help this along.  Many of the people at conferences are junior scientists, are there for the first time, don't know anyone.  Make them feel welcome and included.  If you're one of these people, you should expect to be welcomed as a new colleague.  If someone instead treats you as an interloper, go talk to someone with better social skills.

Latest on our #arseniclife manuscript

Here's an update on the status of our #arseniclife manuscript.

We originally submitted our manuscript to Science at the end of January, and posted a copy of it on the arXiv server, asking for comments/critiques from readers.  We received a few of these, and on March 16 we received three detailed reviews from Science, and a provisional acceptance.  On April 13 we submitted the revised version, and we're waiting with fingers crossed to for final acceptance.

I've just posted the revised manuscript on arXiv, replacing the original version.  Here's the link.  We tried to incorporate suggestions from blog comments too.

Below I've pasted the text of our 'Response to Reviews' letter.  We didn't do a detailed response to the reviews because the Editor had clearly indicated the changes she thought important.


                                                                                                                                    April 13, 2012

Dr. Caroline Ash,
Senior Editor, Science

Dear Dr. Ash,
Thank you for giving us the opportunity to improve our manuscript.  We are now submitting the revised version.
We have closely followed the suggestions in the pre-edited copy of the manuscript that you provided. We felt that the most important request of the reviewers was to directly measure the phosphate concentration in our basal AML60 medium. To this end, we conducted ICP-MS, obtaining a concentration 0.5 µM, in close agreement with our prior estimates based on cell growth. This new measurement fully supports our conclusion that the growth of GFAJ-1 in the hands of Wolfe-Simon et al. was due to residual phosphate in their putative -P conditions. When combined with shortening other text as indicated in the pre-edits, this has reduced the manuscript’s length from 2193 words to 1577 words.
We have retained a few sentences discussing explanations for the discrepancies between our results and those of Wolfe-Simon et al.  Since these discrepancies are the point of our paper we feel that possible explanations for them should be considered even when they cannot be directly tested.
We would prefer to retain our original title, as we feel that the word ‘negligible’ puts undue emphasis on the trace of arsenate present in the DNA.  Two alternative titles we would be happy with are ‘Absence of detectable arsenate in DNA from arsenate-grown GFAJ-1 cells’ and ‘No covalently bound arsenate in DNA from arsenate-grown GFAJ-1 cells’.
Changes in response to points raised by the reviewers:
The ‘-P’ and +P’ growth conditions we used are now clarified in both the Methods and the legend to Figure 1.
We now explain how cell numbers were determined.
We now explicitly say that we obtained strain GFAJ-1 from the authors of the Wolfe-Simon et al. paper.
The discrepancy in glutamate concentrations and the incorrect formulae have been corrected.
The ingredients of AML60 medium are now given in the Methods.
Reviewer 3 was concerned about our statement (in Methods) that cells were pre-grown in phosphate-limited medium containing 40 mM arsenate.  We now explain that the cells were thoroughly washed to remove the arsenate before being frozen, and that the purpose of this pregrowth was to deplete cellular reserves of phosphate and to replicate the standard growth conditions used by Wolfe-Simon et al.
All of the reviewers, but especially Reviewer 3, would have liked more information about GFAJ-1’s growth properties and metabolism.  Unfortunately, characterizing these in depth is beyond the scope of this work.  We do not know why GFAJ-1 cells need glutamate or another amino acid for growth in our AML60 medium independent of phosphate supplementation, why they reproducibly grew to a higher density in AML60 medium with 70 µM phosphate than with 250 or 1500 µM phosphate, nor why they did not grow in Wolfe-Simon’s low-phosphate AML60 medium unless arsenate was provided. 


Rosemary Redfield

Final checks on a surprising competence gene (Whew!)

We now have almost all the data in place for our paper about the roles of all the genes in Haemophilus influenzae's competence regulon.  We (really the RA) created deletion mutations of all the 26 genes except ssb, which is essential; these deletions remove almost all of each gene's coding sequence.  One set of mutations contains spectinomycin cassettes inserted at the site of the deletion; these are very useful because they let us select for each mutation by the SpcR phenotype it causes.  The other set is 'unmarked', and these clean deletions are 'in-frame', preventing disruptions of translation that could interfere with expression of downstream genes in the same operon ('polarity).

For each unmarked mutant we've examined (1) its growth using the Bioscreen incubator/recorder, (2) its survival after transfer to the MIV starvation medium that induces competence, (3) its MIV-inducible ability to take up radiolabeled DNA and (4) its ability to be transformed by genetically marked chromosomal DNA.  For all but one of the genes these phenotypes are at least roughly consistent with what we expected from the phenotypes of known mutations in H. influenzae or other bacteria and from the predicted properties of the encoded proteins.

But one gene's phenotypes surprised us.  HI0659 is predicted to be a small cytoplasmic protein, and it has a predicted helix-turn-helix that would be expected to bind to DNA, probably at a specific sequence.  It's mRNA is induced about 20-fold on MIV treatment.  We expected it to either play no role in DNA uptake and transformation or to have normal uptake but reduced transformation.  But our unmarked mutant (∆HI0659) doesn't take up any detectable DNA and doesn't transform at all, which suggests that it is required either for assembly/function of the uptake machinery or for continued expression of the competence regulon after initial induction by Sxy and CRP.  That's of course very interesting, and we've thought of lots of cool experiments we could eventually do to find out how it acts.

But there's one wrinkle that needs to be cleared up before we publish this result.  The phenotype of the marked (SpcR) HI0659 mutant (∆HI0659::spc) is not the same as that of the unmarked mutant - its transformation frequency is much higher, though still substantially lower than that of wildtype cells. (I don't know if its DNA uptake has been tested.)  This is unexpected and suggests that there's a problem with the structure of either the marked or the unmarked mutation.

The structure of the unmarked mutation has already been carefully checked by PCR and it appears exactly as it should, so we suspect a problem with the marked mutation.  The RA has now created new versions of the marked mutation, and yesterday I made four of these MIV-competent and transformed them.  I'll learn the results of this test later today - if they don't transform at all we'll conclude that all is well with our mutants.

But if the new marked mutants do transform, we'll have to suspect that something is instead wrong with the unmarked mutant.  The most likely problem is that this strain accidentally acquired a mutation elsewhere in its chromosome that prevents DNA uptake.  Testing for this is a bit tricky, but here's my plan, diagrammed below).

HI0659 is in the same operon as HI0660, whose mutants both transform normally.  If the new ∆HI0659::spc mutants transform, I'm going to transform the marked HI0660 mutant (∆HI0660::spc; SpcR) with a PCR fragment containing the normal HI0660 allele and the unmarked version of HI0659.  I have frozen ∆HI0660::spc competent cells ready to use, and the RA is making the PCR fragment for me using primers she already used for another experiment.  I'll plate the transformation mix without selection, using a control transformation with chromosomal NovR DNA to confirm that the cells were competent.  Then I'll screen the colonies for loss of SpcR by picking them onto plain and Spc agar plates.  Colonies that grow on the plain plate but not on the Spc plate will be ones that have lost their spc cassette by recombination with the ∆HI0659 fragment.  I'll test these for transformability - if those that have acquired the ∆HI0659 deletion (checked by PCR) have lost the ability to transform then we can be reasonably confident that the ∆HI0659 deletion prevents transformation.  If not then something is probably wrong with the ∆HI0659 mutant.

This would be a fair amount of work, probably too much to get done before the RA goes on a few months' leave at the end of April. so I very much hope that the transformations I did yesterday give no transformants.

Later:  All the new ∆HI0659::spc mutants are nontransformable, just like the ∆HI059 mutant.  Because I actually tested deletion mutants created in two independent experiments, this means we can be extra confident that the deletion is responsible for the loss of competence.

Harsh reviews of the postdoc's DNA uptake manuscript


Reviewer #1: 

Suitable Quality?: No 
Sufficient General Interest?: No 
Conclusions Justified?: No 
Clearly Written?: No 
Procedures Described?: Yes 



General comments: 







Specific comments: 






















P6 L9 Reference to Bakkali PNAS 2004 is missing 





This is the Discussion...







Reviewer #2: 

Suitable Quality?: No 
Sufficient General Interest?: No 
Conclusions Justified?: No 
Clearly Written?: Yes 
Procedures Described?: Yes 














Not true.





Oops, yes.


No, the formula and calculations are both correct.







Bioscreen growth curves for H. influenzae







Using UBC's open-access archive



First step:  Google "UBC open access archive":


A page called Scholarly Communication at UBC looks promising.


OK, the Open Access resource page got me this:




Well, it looks like I first have to register:


