Tag Archives: competition

A Day in the Life of …

Today was a great day – busy and wonderful. Pretty typical, I’m happy to say, though a bit busier than usual but all of it great.

Woke up to beautiful Spring day in East Lansing and walked 1.7 miles to work at MSU.

Did the usual email stuff.

Worked on getting ready for teaching for a class on evolutionary medicine taught by my colleague Jim Smith. Today’s focus will be the paper by Tami Lieberman et al. on the evolution of Burkholderia dolosa in cystic fibrosis patients during an outbreak in Boston. Last night I re-read the paper for the umpteenth time, and I still enjoyed it. Today I organized a series of questions for the students – a very interactive and smart group – around three parts.

Part I: Some background about CF, the inheritance of this disease, the frequency of the disease, how that frequency allows one to estimate the frequency of carriers, why the allele might be so common (not understood), side questions about sickle-cell anemia and why it’s so prevalent, and why, if it’s inherited, the paper we read is all about infections.

Part II: Preparing slides so we could work our way, figure by figure and panel by panel, through all of the main points in Lieberman et al.  (Reminder: Explain to students how scientific papers are often written around figures.  Once the figures and tables are there, then start on the results, etc.)

Part III: Follow up questions about the paper, the system, the interface of epidemiology and evolutionary biology, prospects for the future of this field and the students’ careers (most in this class are premed, many with a research bent), etc. And whatever questions they might want to ask of me.

Sometime in the middle of doing all that: Chatted with second-year grad student Jay Bundy, who is reading some of Mike Travisano’s terrific earlier papers on the LTEE. Specifically, why do we sometimes express fitness as a ratio of growth rates (measured in head-to-head competitions) and sometimes as a difference in growth rates?

Also in the middle of doing all that: Had phone conversation with former Ph.D. student Bob Woods, now also an M.D. specializing in infectious disease, about a faculty job offer he has (congrats, Bob!), some of the issues he needs to clarify or negotiate, and some of the amazing work he’s now doing on the population dynamics and evolution of nasty infections.

Email from grad student Mike Wiser that our paper, submitted to PLOS ONE, has been officially accepted. We had posted a pre-submission version at bioRxiv – now it’s gone through peer-review and revisions and is accepted for publication. Congrats, Mike!

Got a draft of the fourth and final chapter of Caroline Turner’s dissertation. The first three chapters are in great shape. Congrats, Caroline! With teaching looming, I had only time to review the figures, tables, and legends on this one, and made some small suggestions. On to the text tomorrow … It’s a beautiful body of work on two fascinating aspects of the interplay between ecology and evolution that have emerged in the LTEE and another evolution experiment that Caroline performed. Stay tuned for these papers!

Took a phone call from an MSU colleague who has friend with a child in high school who is interested in microbiology, who is visiting MSU, and who wanted to see the lab. Yikes, I gotta run teach! But postdoc Zack Blount kindly agreed to give a guided tour as I headed off to teach.  Thanks, Zack!

Beautiful day continues as I walk to teach in another building. Touch base with Jim Smith about what I plan to cover.

Two straight hours of teaching (one 5-minute break) in an overly hot room. Almost all of it interactive, with me asking questions and the students conferring in small groups and then responding. Very interactive, very bright students! The two hours were nearly up, with little time for my third, post-paper set of questions. But all of the students stayed (despite the beautiful weather, hot room, and the dinner hour at hand) an extra 15-20 minutes for a couple of my questions and some great ones from them about the LTEE and the future prospects for microbial evolution in relation to medicine.

It’s 6:20 pm: I’m mentally exhausted but equally invigorated. Beautiful Spring day continues as I walk home. I’m greeted by our lovely hound, Cleopatra. Exercise and feed her. Then an even more lovely creature, Madeleine, returns home and I greet her.

Check email before dinner. Find that paper with grad student Rohan Maddamsetti and former postdoc Jeff Barrick has been provisionally accepted, pending minor revisions, at Genetics. We posted a pre-submission version of that paper, too, at bioRxiv. Though we still need to do some revisions, I think it’s fair to offer congrats to Rohan and Jeff, too!

Time to crack open a bottle of wine and have some dinner. Fortunately, some of the pre-packaged dinners are pretty tasty and healthy, too, these days ;>)

Refill wine glass. Sit down and start to write a blog on a day in the life of …


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Putting GMOs on a Tight Leash

Two papers appeared in the latest issue of Natureone from Farren Isaacs’ group and the other from George Church and colleagues—that presented, developed, and demonstrated a strategy for limiting the spread of genetically modified organisms, or GMOs, in the event that they are accidentally released or deliberately applied to the environment.

My Involvement with GMO Discussions in the 1980s

I was actively involved in discussions about environmental applications and field testing of genetically engineered organisms back in the 1980s. As a postdoc in 1984, I had a short letter in Nature where I suggested a containment strategy for an early proposed application of genetically modified “ice-minus” bacteria. Later that year I attended a small meeting on environmental applications of GMOs at the Cold Spring Harbor Laboratory, and a short report was published in the Bulletin of the Ecological Society of America. As faculty member at UC Irvine in 1986, I served as a consultant on a report for the Office of Technology Assessment of the US Congress. I also co-organized and moderated a lively public debate on the benefits and risks of genetically engineered organisms between Jerry Caulder, who was CEO of a biotech company, and the distinguished ecologist Daniel Simberloff, an expert on invasive species.

At that time, one of the arguments—the “excess baggage hypothesis”—for the safety of GMOs was that genetically engineered functions would impose a metabolic burden and thereby reduce the fitness of the organisms, so that they wouldn’t be good competitors in nature. While that argument made some sense as a trend or tendency, it didn’t seem likely that it would apply in every possible case given the potential for new environments and/or compensatory adaptations to favor novel functions. In 1988, I wrote a review for Trends in Ecology & Evolution with a postdoc, Toai Nguyen, on the “Stability of recombinant DNA and its effects on fitness” that made these points.

As a result of my interest in and involvement with these issues, I was asked to serve on two expert panels—one convened by the Ecological Society of America (ESA), the other by the National Research Council (NRC) arm of the National Academy of Sciences—that wrote lengthy reports, both published in 1989. In both reports, the committees tried to emphasize that one needed to consider two different issues. (1) What, if any, were the potential problems that might be caused by the release of particular GMO? (2) In the event that some problem actually did arise, would the GMO (or its engineered genes) survive and possibly spread in the environment? Or would the problem be resolved by halting further applications of the GMO, because they would then simply die off?

(These panels were hard work, but through them I met some great scientists, including Jim Tiedje and Rita Colwell among many others.)

After that extensive involvement with this science-policy issue in the 1980s, my research tended toward more basic questions in the years that followed. Meanwhile, of course, there has remained substantial scientific, commercial, and public interest in the methods and applications of genetic engineering. The two recent papers in Nature reflect the latest efforts to ensure the safety of GMOs by putting them on a tight leash.

My Thoughts on the Recent Papers

I was asked to comment on the Nature papers by Malcolm Ritter, a science reporter for the AP, and he briefly (and accurately) quoted me in a short news piece that appeared yesterday. In light of a question about my thoughts on Twitter, I thought I’d share my full remarks here:

Using genetically modified organisms in the environment raises a couple of intersecting issues. One concerns the effects those organisms have. Of course, GMOs are intended to provide some benefit—say, for bioenergy or agriculture—but in some cases the GMOs might have secondary or unanticipated harmful effects. If these harmful effects occur, and if they outweigh the benefits, then one would like to be able to recall the GMOs from the environment—sort of like recalling cars when some problem is discovered after they’ve been sold. The challenge is that GMOs are organisms, they are alive and can reproduce, and so they won’t necessarily just go away if one stops using them. Over the years, different strategies have been proposed to ensure that GMOs will, in fact, just die off after they’ve done their job, but these strategies have had holes, such as the possibility that evolution might break whatever leash the scientists put on the GMOs so that they could be recalled.

These two papers, though, point the way towards putting GMOs on a very tight leash, one that is meant to be unbreakable, by changing the genetic code of an organism so that its replication becomes dependent on certain synthetic building blocks—amino acids—that aren’t found in nature. So by applying these molecules along with the GMO in some environment, the GMOs can replicate and do their job. But if the synthetic amino acids aren’t supplied, then the GMOs won’t be able to replicate further after they’ve run out, and so that provides a leash that should rein the GMOs in if there is some problem. Of course, there are a lot of technical challenges to pulling this off, because you can’t make the organisms so weak that they can’t do their intended functions.

And of course, extending this approach from microorganisms—the subject of these papers—to crop plants would raise all sorts of additional questions about nutritional value and safety. Those are different issues and not what these papers are about.

Coda: Does this approach ensure containment of a GMO? Probably not. There aren’t many guarantees in life, and evolution has a history (billions of years, in fact) of finding clever solutions that might not occur to engineers and scientists. Does that mean that we should not use GMOs in nature? Not at all. As our ESA and NRC reports of a quarter-century ago stressed, one should consider both the benefits of a particular environmental application of a GMO and its potential harm if something goes wrong. In those cases where the benefits are great, and the potential for harm is very small (both in likelihood and magnitude), then the issues of containment and recall after a release are less critical. But in those instances where the potential risks of some GMO are substantial—either in terms of the likelihood or the magnitude of adverse effects—then every effort must be made to put the GMOs on a tight leash or, absent that, do not proceed with the proposed application.

[The image below is one part of Figure 1 from the Nature paper, titled “Recoded organisms engineered to depend on synthetic amino acids” and authored by Alexis J. Rovner, Adrian D. Haimovich, Spencer R. Katz, Zhe Li, Michael W. Grome, Brandon M. Gassaway, Miriam Amiram, Jaymin R. Patel, Ryan R. Gallagher, Jesse Rinehart and Farren J. Isaacs.  This image is shown here under the doctrine of fair use.]

Portion Fig 1 from Rovner et al, Nature, 2015

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What We’ve Learned about Evolution from the LTEE: Number 5

This is the fifth in a series of ~10 posts in which I summarize what I think are the most important findings and interesting discoveries from the LTEE.  The previous entry, number 4, also has links to the earlier entries.

Number 5.  We have seen large changes in the spontaneous mutation rate in some of the LTEE populations.  These changes reflect an interesting tradeoff between short-term fitness and long-term evolvability.

Proximate causes.  Six of the 12 LTEE populations evolved to be so-called “hypermutators” by 50,000 generations.  The proximate (i.e., biochemical) causes of these changes are mutations in genes whose products are involved in DNA repair or the degradation of molecules that cause damage to DNA.

These mutations typically cause the rate of point mutations throughout the genome to increase by roughly 100-fold (Sniegowski et al., 1997, Wielgoss et al., 2013), so their effects are not at all subtle.  They also change the spectrum of mutations:  mutations in the mutS gene, which encodes a protein involved in mismatch repair, cause increased A·T–>G·C and G·C–>A·T transitions (Lenski et al., 2003); while mutations in mutT, which encodes an enzyme that degrades an oxidized nucleotide, cause A·T–>C·G transversions (Barrick et al., 2009).

Evolutionary effects.  The evolutionary effects of these hypermutators are subtle and interesting.  In essence, one can think of mutations that produce hypermutators as affecting the tradeoff between short-term fitness and long-term evolvability.

Short-term cost.  Of all the possible mutations that might occur, many more are deleterious than are beneficial. Therefore, hypermutators produce more maladapted progeny than otherwise identical cells with a lower mutation rate.  Hence, hypermutators suffer a fitness cost caused by the increased production of progeny with deleterious mutations.

However, the E. coli strain that was the ancestor to the LTEE has a low point mutation rate, which we’ve estimated as ~10-10 per base-pair per generation (Wielgoss et al., 2011).  Given the genome contains ~5 x 106 base-pairs, this rate translates to only ~0.0005 point mutations per genome per generation.  Therefore, even a 100-fold increase means that most hypermutator progeny are mutation-free.  Considering that only a fraction of genomic sites are subject to mutations that would be deleterious in the LTEE environment, we infer that the short-term cost to a 100-fold hypermutator is ~1% (Wielgoss et al., 2013).

Evolvability benefit.  Even a 1% cost is not trivial, so how can a hypermutator survive and spread through a population?  In fact, most hypermutators do not survive; the vast majority of mutations that cause hypermutators will die out as a consequence of that short-term cost.  However, hypermutators result from loss-of-function mutations, and a dozen or so large genes are targets for these mutations.  Hence, new hypermutators will continually be regenerated in large populations.  Absent other forces, an equilibrium frequency of hypermutators would be reached that reflects the balance between the rate of appearance of hypermutators by new mutations in the relevant genes and the rate at which they are removed by selection against the deleterious mutations they cause—in other words, the familiar mutation-selection balance of population-genetics theory.

But another force is at play: the populations in the LTEE are not sitting on a fitness peak, so there are on-going opportunities for beneficial mutations to appear.  And a hypermutator cell has a much higher probability of generating a beneficial mutation than does a “normal” cell.  In essence, there’s a race to produce the next winner.  If a hypermutable cell generates the next beneficial mutation that sweeps through the population, then the hypermutator will “hitchhike” along with it because, without sex, the two mutations are linked.

Combining forces.  So how do the short-term cost and the evolvability benefit play out together?  Mutations that knock out any one of the genes involved in DNA repair probably occur at a rate between 10-5 and 10-6 per generation, and the resulting hypermutable cells have a fitness disadvantage of ~1% owing to the production of deleterious mutations.  At mutation-selection balance, the frequency of hypermutators is between 0.01% (10-4) and 0.1% (10-3).  Let’s use 0.05% to illustrate.

Although the hypermutators are a small minority, on a per capita basis each of them has a 100-times higher probability than a normal cell of generating the next winner.  So 5% of the time, a hypermutator will be swept to fixation, but most of the time the winner will be produced by a normal cell.  Now consider the fact that each of the LTEE populations has had many beneficial mutations go to fixation over its history.  After 14 selective sweeps, the odds are better than 50:50 that at least one of those beneficial mutations was generated by a hypermutator.

King of the mountain.  After a hypermutator becomes common, it becomes very hard to dislodge it from the population.  This difficulty follows from the same logic as above.  Once the hypermutator reaches 1% of the population, it has a 50% chance of generating the next winner; by the time it gets to a 50% frequency, the odds are 100:1 in its favor.  Thus, a hypermutator only needs to get lucky once, and then it becomes extremely difficult to displace it … at least so long as the population is far from the fitness peak.

Nothing lasts forever.  Even before a population reaches a fitness peak, its rate of fitness improvement typically decelerates, at least in a constant environment like that of the LTEE (Wiser et al., 2013).  At some point, the magnitude of the benefit that would result from reducing the mutation rate and its associated fitness cost may become commensurate with the fitness advantages that are available from other mutations.  When that happens, selection to reduce the mutation rate becomes effective, and the hypermutable “king of the mountain” can be displaced by a genotype with a lower mutation rate.

Indeed, we have observed this displacement occurring in one of the LTEE populations (Wielgoss et al., 2013).  In that population, not one but two lineages independently arose (see Figure below) that reduced the mutation rate by about half, while reducing the fitness cost from ~1% to ~0.5%.  The population thus remains hypermutable, but less so than before.

What the future may hold.  In that paper, we hinted that it is probably easier to reduce the mutation rate in stages rather than to revert to the ancestral rate in a single step.  That’s because the population is continuing to adapt, albeit at a slower rate.  A genotype with a 50% reduction in the mutation rate will save half of the fitness cost of the full-blown hypermutator, yet it will continue to produce 50 times as many other beneficial mutations as would a genotype that reverted to the ancestral mutation rate.  In essence, the fitness costs and the evolvability benefits are on very different scales.

The figure below shows the decelerating fitness trajectory (dark green curve, left axis) and the number of mutations (right axis) as the lineage with the ancestral mutation rate (blue) is replaced by a hypermutator lineage (red), which in turn is displaced by two independent lineages with somewhat lower mutation rates (light green and purple).  The figure comes from Wielgoss et al., 2013, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.

Mutation trajectory, PNAS 2013


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An Alternative to Schekman’s Boycott of Luxury Journals

In a provocative op-ed that appeared in The Guardian, Nobel Laureate Randy Schekman says that he and his scientific team will “avoid” luxury journals, and he “encourage[s] others to do likewise”.  In effect, Prof. Schekman is calling for scientists to boycott Science, Nature, and Cell, probably the three most prestigious scientific journals in the world.

Prof.  Schekman raises some important issues about scientific publishing—ones that are receiving more and more attention as scholars and publishers alike experiment with new modes and models for publishing.

But Prof. Schekman’s biggest concern seems to be with the problems that “luxury” journals (or ‘glam’ journals, as they’re called on Twitter) create in terms of excessive attention and inappropriate incentives.  These are important issues, too, but I think there are some flaws in his argument.

Prof. Schekman compares luxury journals and the problems they create with Wall Street’s out-sized bonuses and the problems they’ve caused for the financial system.  That certainly grabs attention.

Prof. Schekman is a professor at the University of California, Berkeley, one of the luxury universities.  Here’s the title and one key paragraph from his opinion piece:

“How journals like Nature, Cell and Science are damaging science”

“These luxury journals are supposed to be the epitome of quality, publishing only the best research. Because funding and appointment panels often use place of publication as a proxy for quality of science, appearing in these titles often leads to grants and professorships. But the big journals’ reputations are only partly warranted. While they publish many outstanding papers, they do not publish only outstanding papers. Neither are they the only publishers of outstanding research.”

Now let’s make a few small changes. I don’t think the words I’ve substituted are any less true than those that Prof. Schekman wrote.  I’ve changed only those words in italics:  

“How universities like Harvard, Caltech, and Berkeley are damaging science”

“These luxury universities are supposed to be the epitome of quality, training only the best students. Because funding and appointment panels often use place of degree as a proxy for quality of science, obtaining degrees from these institutions often leads to fellowships and professorships. But the big universities’ reputations are only partly warranted. While they produce many outstanding scientists, they do not produce only outstanding scientists. Neither are they the only producers of outstanding scientists.”

So, will Prof. Schekman and his group also avoid luxury universities, and will he encourage others to do the same?


[EDIT: ADDED 1:30 PM]  Let me be clear: I am not suggesting that these universities should be boycotted. Rather, I simply want to point out that there many dimension of “luxury” and “glamor” in science (as in life more generally), and these can distort attention and incentives.  I’m not convinced that boycotts are the best way to address the underlying issues with respect to either journals or universities.

[EDIT #2: ADDED 1:35 PM] Let me also say I think eLife is off to a great start, with some new ideas on how to improve scientific publication.  I wish Prof. Schekman and the journal every success.


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Fifty-Thousand Squared

I’ve been thinking a lot about the long-term evolution experiment (LTEE) with E. coli lately – even more than usual.  One impetus has been the paper by Mike Wiser, Noah Ribeck, and me that appeared today (14-Nov-2013) in Science (online publication in advance of print).  Another reason is that I’m working on the competitive renewal for the NSF grant that funds this experiment.

The Experiment that Keeps on Giving

Both of these have got me thinking about the long-term fate of this long-term experiment.  Should the experiment continue?  For how long should it continue?  Who will take it over when (or before) I retire?  And after that person retires, then what?  How will they sustain it?  Will they rely on the usual competitive grants?  Would an endowment be more suitable?  How does one raise an endowment?

I like to say that the LTEE is the experiment that just keeps on giving.  Between the element of time, the inventiveness of the bacteria (even in their simple, confined, little flask worlds), and the many talented students, postdocs, and collaborators who have worked on the LTEE, there seems to be no end to the insights this experiment can provide into fundamental questions about evolution.  Why shouldn’t this experiment keep on giving, even after I’m gone?

When I started the LTEE in February of 1988, I had no idea that it would continue for more than 50,000 generations.  I had previously done some other experiments that went for a few hundred generations, and I intended this one to run for at least 2,000 generations.  That would deserve the “long-term” moniker.  Although we made some fitness measurements along the way, most of the hard work comes after a milestone is reached.  That’s when one begins the intensive assays to quantify the changes that occurred.  And while we performed those assays, we continued the daily transfers.  So by the time the first paper was prepared, submitted, reviewed, revised, and published in December of 1991, the LTEE was past 5,000 generations.  And so it has gone:  new milestones, new questions, new assays, new data, new analyses, and new papers.

And the new questions keep coming based on new hypotheses of students, postdocs, and collaborators (occasionally even me), new technologies such as genome sequencing, and new observations of what the evolving bacteria have done.

Fitness Unlimited

This latest paper is an interesting one because it uses our most old-fashioned assays – the kind that was the heart of the LTEE when it started, and which also formed the core of that first paper back in 1991.  That is, the results are based on measurements of relative fitness, coupled with new models – both descriptive and dynamical.  (Although this blog post emphasizes the descriptive model, the Science paper also presents new theory showing that the descriptive model can be derived from a dynamical model of evolution that incorporates two phenomena – clonal interference and diminishing-returns epistasis – that are known to occur in the LTEE and other studies of evolving asexual populations.)

Fitness is the central phenotype in evolutionary theory; it integrates and encapsulates the effects of all mutations and their resulting phenotypic changes on reproductive success.  Fitness depends, of course, on the environment, and here we measure fitness in the same medium and other conditions as used in the LTEE.  We estimate the mean fitness of a sample from a particular population at a particular generation by competing the sample against the ancestral strain, and we distinguish them based on a neutral genetic marker.  Prior to the competition, both competitors have been stored in a deep freezer, then revived, and acclimated separately for several generations before they are mixed to start the assay proper.  Fitness is calculated as the ratio of their realized growth rates as the ancestor and its descendants compete head-to-head under the conditions that prevailed for 500 … or 5000 … or 50,000 generations.

The exciting new result is that the fitness of these evolving bacteria shows no evidence of an upper bound or asymptote.  A two-parameter power law fits the data much better than does a two-parameter hyperbolic model.  According to both models, the rate of fitness increase decelerates over time, as it clearly does.  However, the power-law model has no asymptote, whereas the hyperbolic model has an upper bound.

Even more striking and important, to my mind, is that the models differ in their predictive power.  We fit these two models to truncated datasets that included only the first 20,000 generations of data and asked how well they predicted the fitness values observed over the next 30,000 generations of data.  The unbounded power law beautifully predicts the fitness trajectory that it had not seen, whereas the asymptotic hyperbolic model underestimated later measurements.  The underestimation of the asymptotic model becomes progressively worse as the temporal data are more and more truncated; that is, the evolving bacteria consistently pass right through the “limit” predicted from previous data.  By contrast, even with only 5000 generations of data, the power-law model very nicely predicts the fitness trajectory all the way out to 50,000 generations.

A Thought-Experiment

How long can this continue?  In our paper, we present the following thought-experiment.  I’ve overseen 50,000 generations of the LTEE in my scientific life; now imagine another 49,999 generations of scientists, each one overseeing 50,000 more bacterial generations. That’s 50,000^2 generations, or 2.5 billion generations in total.  (It will take about a million years to get there.)  You’re probably thinking that the unbounded power-law model must predict some crazy high fitness that would imply a ridiculously fast growth rate.

In fact, the power law predicts that fitness relative to the ancestor will increase from ~1.7 after 50,000 generations to ~4.7 after 2,500,000,000 generations.  If the bacteria eliminate the lag time associated with the transition from starvation to growth each day (which they have already largely done), then a fitness value of 4.7 implies that the bacteria will have to reduce their doubling time from the ancestor’s ~55 minutes to ~23 minutes.  That’s very fast given that the LTEE uses a minimal medium where cells must synthesize everything from glucose, ammonium, and a few other molecules.  But it’s not so fast that it suggests the bacteria would violate some biophysical constraint.  Indeed, some bacteria can grow twice that fast, albeit in a nutrient-rich medium.

What Does the Future Hold?

I’d really like science to test this prediction!  How often does evolutionary biology make quantitative predictions that extend a million years into the future?  Maybe the LTEE won’t last that long, but I see no reason that, with some proper support, it can’t reach 250,000 generations.  That would be less than a century from now.  If the experiment gets that far, I’d like to propose that it be renamed the VLTEE – the very long-term evolution experiment.

And this prediction about the future fitness trajectory is not the only – or even the main – reason to keep the LTEE going.  Some important things in evolution simply require a lot of time.  In my presidential address to the Society for the Study of Evolution this past summer, I highlighted three findings where it proved to be important that the LTEE had continued for many years (and, if I’d had more time, I could have added more to that list).  First, it takes a very long time series to distinguish between asymptotic and non-asymptotic fitness trajectories.  Second, it took over 30,000 generations before the most dramatic phenotypic change occurred in one of the populations, which evolved the ability to use citrate – which has been present in the medium of the LTEE throughout its duration – as a second source of energy.  Third, postdoc Zachary Blount is currently studying whether the refinement of that new function is leading to changes that would qualify the citrate users as a new, or incipient, species.

What other new traits might the bacteria evolve?  Could they evolve some means of genetic exchange?  Might the within-population competitive interactions ever take a turn toward predation?  Who knows?  Only time will tell – and only if we allow time, the bacteria, and future generations of scientists to do the work of evolution and science.

Some Additional Links


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Teaching Competition and Predation from a Microbiological Perspective

Life has been busy, very busy.  And life has been good!  But the busy-ness has made it hard for me to keep up with this blog.  In the next few weeks, I hope to share some of the things that have kept me so occupied this past month.

For starters, I’d like to discuss some recent teaching where I tried to emphasize the interplay between theory and experiments in ecology.

I recently taught part of our graduate-level course called “Integrative Microbial Biology.”  Some years ago this course replaced several other graduate courses (microbial ecology, microbial physiology, microbial diversity, etc.) that each had a low enrollment.  The idea is that we now offer a single, annual, intensive, team-taught course that covers all these topics, albeit more superficially but with the hope that it encourages students and faculty alike to develop a more integrated perspective of microorganisms as organisms.  More specialized courses, with a focus on reading and discussion, are offered as occasional seminar-style courses.

I teach two parts of the course – one on aspects of microbial ecology, the other on microbial evolution.   Many of the students have not had an undergraduate course in general ecology or evolutionary biology, and so I try to bring them up to speed, albeit with examples that focus on microorganisms.

So, for the ecology portion I begin with population growth and competition.  I’m a fan of resource-based competition theory, as opposed to the more familiar logistic growth and Lotka-Volterra competition models.  The key strength of resource-based competition theory is that one can predict the outcome of competition based on parameters that can be measured separately for each species or strain, without requiring that one compete them in order to understand their competition.  Of course, there are many reasons the predictions might fail, but the resource-based model (and extensions to it) provide a mechanistic framework for understanding competition.

I then present predator-prey interactions, surveying the extraordinary diversity of microbe-on-microbe predation and parasitism, and then providing again a dynamical framework for understanding those interactions.  Here, Lotka-Volterra predator-prey models do provide a reasonable starting point because one can measure key parameters that have mechanistic interpretations (e.g., attack rates, conversion efficiencies) and use them to make new predictions about the dynamics of the system as a whole.

Besides presenting the general theory, I also present empirical studies from the primary literature.  In some cases, I summarize the papers in my lectures, while in other cases the students read the papers and we then discuss them.  Here are four of the papers with summaries; I hope to blog someday in greater depth on at least the Hansen & Hubbell and Rainey & Travisano papers, which I view as “must-read” papers in the field of ecology.

Hansen, S. R., and S. P. Hubbell.  1980.  Single-nutrient microbial competition: qualitative agreement between experimental and theoretically forecast outcomes.  Science 207:1491-1493.

This paper presented an early, concise, and compelling demonstration of the utility of resource-based competition theory.  By choosing three pairs of competitors that differed in various parameters, and then competing them in chemostats, the authors showed that the outcome depended on the two competitors’ relative “break-even” (equilibrium) concentrations of the growth-limiting resource.  For any student who wants more information on this approach – and every year at least some students ask for more – I recommend they read David Tilman’s outstanding book, Resource Competition and Community Structure (1982, Princeton University Press).

Rainey, P. B., and M. Travisano.  1998.  Adaptive radiation in a heterogeneous environment.  Nature 394:69-72.

This paper is a beauty.  The authors showed that the evolutionary emergence of diversity can sometimes depend on something as simple as whether a flask is shaken or not.  In the absence of shaking, an initially monotypic population of Pseudomonas fluorescens evolved into a community of three distinct ecotypes that differentially exploit the environmental gradients that arise without constant mixing; that diversity is stably maintained, as was shown by analyzing pairwise interactions.  By contrast, simply shaking the flask, with all else being equal, homogenizes the environment and the ecotypic diversity does not evolve; and if the diversity had already evolved, then it was eliminated as a single type came to dominate the well-mixed system.

Lenski, R. E., and B. R. Levin.  1985.  Constraints on the coevolution of bacteria and virulent phage: a model, some experiments, and predictions for natural communities.  American Naturalist 125:585-602.

Virulent phage infect bacteria, and they have life-cycles like those of insect parasitoids; that is, a successful infection is lethal to the host, and many phage are produced from a single infection.  In this paper, we examined the ecological and evolutionary dynamics of the interactions between E. coli and four different virulent phages.  First, the Lotka-Volterra predator-prey model – modified to include resource-based growth for the prey (bacteria) and a time-lag associated with predator reproduction (phage replicating inside bacteria) – predicted reasonably well the short-term dynamics of the interaction between E. coli and one of the phages, called T4.  Second, the model was extended to include the evolution of bacteria that are resistant to phage attack.  Resistance mutations changed the equilibrium density of the bacteria by several orders of magnitude, as the bacterial population went from top-down predator limitation to bottom-up resource limitation.  Yet despite complete resistance, the phage population persisted because there was a “cost of resistance” – in the absence of phage, the sensitive bacteria out-competed the resistant mutants.  In essence, the system becomes one of predator-mediated coexistence of sensitive and resistant prey populations.  Third, the interactions between E. coli and three other phages were examined.  Each interaction had somewhat different dynamics depending on whether resistance was costly or not, whether resistance was partial or complete, and whether the phage population produced host-range mutants that could infect the mutant bacteria that had become resistant to the progenitor phage.  [This paper built on related work that Lin Chao had done a few years earlier with Bruce Levin, and which inspired me to contact Bruce about joining his lab.]

Bohannan, B. J. M., and R. E. Lenski.  2000.  Linking genetic change to community evolution: insights from studies of bacteria and bacteriophage.  Ecology Letters 3:362-377.

This paper reviews the research that Brendan Bohannan did for his dissertation in my lab.  His work examined the same four bacteria-phage interactions studied in the Lenski and Levin paper above, but the work was extended to include some elegant new manipulations and analyses.  In particular, by changing the levels of resource available to the bacteria, the classic “paradox of enrichment” predicted by Lotka-Volterra predator-prey models was confirmed, with respect to the effects of enrichment on both equilibrium densities and the temporal fluctuations in population densities.  These experiments also provided compelling evidence for predator-prey cycles and the effects of bacterial resistance on the dynamics of the interaction between the remaining sensitive bacteria and phage populations.


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Chao and Levin, 1981, PNAS

This is the third in my series of must-read papers.  It’s an elegant paper that sits right at the interface of ecology, evolution, and behavior.  And like the last paper that I wrote about, this one is superb for teaching and capturing the interest of students.

Chao, L., and Levin, B. R.  1981. Structured habitats and the evolution of anticompetitor toxins in bacteria.  Proc. Natl. Acad. Sci. USA 78, 6324-6328.

Short summary:  Some bacterial strains produce and release toxins that kill members of their own species – except, that is, close kin that possess a linked immunity function.  The production of the toxins is also lethal to the small fraction of cells that actually do so in any given generation.  Lin Chao and Bruce Levin sought to understand when and how this trait would be beneficial.  When killer and sensitive strains competed in liquid, the killer strain prevailed, but only if it started out above a threshold frequency.  That raised the question of how the killer strain could reach that frequency, because it was at a disadvantage when it was below that threshold.  When the same strains competed in a structured environment (a gel-like matrix), this conundrum was resolved—the killer strain could invade a population of sensitive cells even if the killers started at an arbitrarily low frequency.  The difference arises because, in the structured environment, the resources made available by the killers accrue disproportionately to the killers’ kin.  This paper was ahead of its time, but it set the conceptual stage for the now-blossoming field that uses microbes to study the evolution of social traits and interactions.

Some additional background and explanation:  Many bacteria can produce and release toxins that kill other members of the same species.  These toxins are called bacteriocins in general; those studied by Chao and Levin are also called colicins because they are produced by, and used against, E. coli.  The toxin production and immunity functions are tightly linked in a genetic module, and such modules are often located on extra-chromosomal elements called plasmids.  Interestingly, the production of the toxin is lethal to the individual cell that does so, because the cell must lyse to release the toxin.  However, only a small proportion (maybe 1%) of the potential killers that carry the toxin/immunity module actually produce toxin in a given generation, while the others constitutively express the immunity function.

How can a function evolve that is lethal to the individual organism that expresses it?  Chao and Levin began by competing two otherwise identical E. coli strains—one that carries the toxin/immunity module, the other sensitive to the toxin—in a well-mixed liquid medium.  Let’s call the strains K and S for killer and sensitive, respectively.  If there were enough K cells (above ~2% in their experimental conditions), then K rose in frequency and drove the S type extinct.  Although the K population experienced some deaths from the production of the toxin, the resulting toxin concentration was so high that the death rate of S exceeded its growth rate.

But if the initial frequency of the K type was below that ~2% threshold, then the outcome was reversed—the S population rose in frequency, and the K population declined, although the exclusion played out more slowly than when K started out above the threshold.  What’s happening here?  The K cells still had the extra cell deaths caused by the release of toxin, but the concentration of toxin was not sufficient to wipe out the S population.  Some S cells were killed, and their resources—those released upon their death plus those they could no longer consume—became available to other cells.  Because the competition environment was well mixed, any cell—whether K or S—had equal access to the freed-up resources.  If the death rate of the K type (the proportion that produces toxin and then lyses) were greater than the kill rate of the S type, then K would decline in frequency because the resulting benefit—the extra resource that became available—was equally available to all survivors, regardless of whether they had the K or S genotype.

From an ecological standpoint, it’s a nice example of a dynamically unstable equilibrium between two competitors.  However, it raises a problem from an evolutionary perspective.  If possession of the toxin/immunity module is beneficial when it is common in a population, but disadvantageous when it is rare, then how can it go from being rare to common?

Chao and Levin recognized that a physically structured environment might be important, because it would change the distribution of the freed-up resources to the two cell types.  So they repeated the competitions between K and S strains, again varying the initial frequency of the K type, except now in a semi-solid medium called “soft agar.”   (The procedures get more complicated here; to propagate the competing cell types, each day they had to free the cells from the soft-agar matrix and transfer them into a new matrix.)  When the two types competed in this structured environment, the unstable equilibrium disappeared, and the K strain could invade and take over from an arbitrarily low initial frequency.  That is, the K genotype could now go from being rare to common.

Why this difference between the liquid and semi-solid environments?  In the structured environment, the bacteria grew as colonies, not as individuals floating about at random.  As a consequence, the extra resources made available by the killers flowed disproportionately to their own kin.  Here a picture is worth a thousand words; I show a figure from Chao and Levin below that makes this point graphically.  In a sea of crowded S colonies, there’s one K colony.  The K colony is larger than most of the S colonies.  Each colony began from a single cell; the fact that the K colony is larger than most means that it got more than its share of resources.  Even more strikingly, the K colony is surrounded by a large zone that is entirely devoid of colonies—the toxins released by the small proportion of K cells that lysed have diffused into this zone and prevented growth of S cells.  The resources diffused randomly, but the K colony sat alone in the middle of this zone of inhibition that it generated, and so indeed it got more than its share of resources.

Chao and Levin Fig 3

The figure above is from Chao and Levin, 1981, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.  The image is centered on a single colony of toxin-producing bacteria surrounded by an inhibition zone and, further out, by colonies of sensitive bacteria.  The scale bar is 0.5 mm.

A Later, Related Paper:  There’s another nice paper by Ben Kerr, Peg Riley, Marc Feldman and Brendan Bohannan (2002, Nature) that builds on the work by Chao and Levin.  Kerr et al. added a third “player”—a third strain—into these experiments, one that was resistant to the toxin but did not produce it.  In a physically structured environment, the toxin-producing killer strain could invade and displace the sensitive strain, just as Levin and Chao saw.  However, the resistant strain could invade and displace the toxin-producer, because the physiological cost of resistance was less than the combined costs of toxin-production and immunity.  And the sensitive strain could invade and displace the resistant strain, because the sensitive strain did not pay the cost of resistance.  In other words, the pairwise interactions were non-transitive, just like the game of rock-paper-scissors.  But although each pairwise interaction had a winner and a loser, the three types could coexist indefinitely in a spatially structured environment provided different spatial regions were out of phase—in effect, the three populations chased one another around in space and time.

Why I like this paper so much:  First, the paper by Chao and Levin beautifully illustrates how population biologists frame, dissect and analyze a complex problem—one that involves frequency-dependent effects, tradeoffs, spatial structure, and genetic relatedness along with both scramble and interference competition.  Out of all these complications, there comes that “Aha!” moment when it all makes sense—just like the feeling one gets from the Luria and Delbrück experiment.

Second, there’s been a boom in the study of the evolution of social behaviors using microbes over the last 15 years or so.  The current phase began with papers by Paul Tuner and Lin Chao on interactions among viruses infecting the same cell leading to a Prisoner’s Dilemma (Nature, 1999); by Greg Velicer, Lee Kroos, and myself on cheating during multicellular fruiting-body development in the bacterium Myxococcus xanthus (Nature, 2000); and by Joan Strassmann, Yong Zhu, and David Queller on cooperation and cheating in aggregations of the social amoeba Dictyostelium discoideum (Nature, 2000).  Today, there are many groups around the world who study quorum sensing, fruiting-body formation, biofilms, toxin degradation, and other microbial behaviors from an evolutionary perspective.  The 1981 paper by Chao and Levin showed that microbial systems could serve as model systems for studying social evolution while being fascinating in their own right.  (It’s also fitting to note that John Bonner, who pioneered the study of D. discoideum, served as the editor for Chao and Levin’s paper.)

Third, Bruce Levin was my postdoctoral mentor, and Lin Chao did his graduate work with Bruce.  Lin had moved on to a postdoc position before I joined the lab, but this paper was one of my formative exposures to the conceptual elegance and experimental power of using microbes to study population dynamics.  Lin and Bruce had also written two papers on the dynamics of interactions between bacteria and phage (Levin et al., 1977, Am. Nat.; Chao et al., 1977, Ecology), and those papers were the ones that first led me to write Bruce about the possibility of joining his group as a postdoc.

Finally, this paper provides a sobering reminder that we humans are not as special as we often imagine, even in warfare.  Mindless bacteria were killing each other billions of years before we came on the scene.  Perhaps we can use our minds to suppress the worst of our primal urges.

[ADDED 13 Sept. 2013] Lin Chao emailed me that “The inspiration of that work was a lecture that Bruce gave in his Pop Biology class at UMass where he discussed the limitations of Lotka Volterra equations for interference competition.  That sat in my mind for a couple of years until it became a real project.” So this must-read paper also provides a nice example of the productive interplay between teaching and research.


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